Building Tomorrow’s Tech: AI Investments in Full Swing – SwissCognitive AI Investment Radar

Welcome back to this week’s SwissCognitive AI Investment Radar, where we spotlight the latest in global AI investments shaping the tech landscape.

This week, we see a broad spectrum of strategic moves and financial commitments, from General Catalyst’s $8 billion leap into AI-driven enterprise transformation to Reflexivity’s $30 million for advancing AI-powered financial analysis. Across the Atlantic, the European Union pledges $1.5 billion for deep tech research, aiming to keep Europe competitive in an arena largely dominated by the U.S. and China.

Automotive innovation takes center stage as Toyota and NTT invest $3.3 billion to push forward predictive, accident-avoiding AI for self-driving cars by 2028. Meanwhile, Google’s $5.8 million initiative in Sub-Saharan Africa seeks to bridge the AI skills gap, enabling local talent to address challenges in health, climate, and more. AI’s influence is also reshaping sectors like architecture and construction, as industry players ramp up investments to integrate AI capabilities into their workflows.

From the defense sector, Helsing’s £350 million commitment underscores the importance of AI in national security, while Saudi Aramco’s VC arm dedicates $100 million to nurture AI startups. And as AI startups now command a third of U.S. venture funding, countries like Indonesia are positioning themselves as new hubs for AI with favorable policies and low energy costs.

Join us as we delve into these developments, capturing how AI’s financial momentum is redefining industries and shaping the future.

Previous SwissCognitive AI Radar: Tech Giants Lead AI Infrastructure, Startups Follow Suit.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag Building Tomorrow’s Tech: AI Investments in Full Swing – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: intelligencebriefing.substack.com – “AI’s Swiss Watch Effect: The Value Of Human Creativity And Skills”

I’ve been thinking about the value of human creativity lately. I am still forming my opinion on whether, where, and when the result of human creativity is a real differentiator vs. just a “nice hobby”. But a recent interaction that turned from small talk to resonating more deeply is a good reminder of the impact of AI and the range of mindsets with which people are viewing AI — and that applies to your business as well.

Before we talk about what all of that has to do with Swiss watches, let me share a brief story to illustrate the point…

AI’s Impact: Conversation with an Artist

Last week, I attended SAP’s annual Sapphire conference in Orlando. Naturally, AI was the talk of the town among the 12,000+ attendees, but that’s a separate topic. During a reception in the evening, I had a conversation with a sketch artist while he was drawing the sketch below:

“I have to be an artist,” Kenny said.

It was one of these rare moments that you don’t expect a tech conference.

Yet, this one sentence says it all.

As Kenny was drawing this sketch of me, I shared that I value his human creativity and skill.

I asked what he thought about AI.

“It’s already taking away some of my business,” he shared. So, he’s started digital sculpting and 3D animation.

I asked whether he’s ever tried using any AI tools, and shared that he could perfect what average GenAI users don’t know how to do (and charge for it).

“They’ve taken art and illegally built their AI with it. The art I’ve posted could be in it, too. I don’t want to use it.”

I admitted he had a valid point.

“Maybe GenAI will be like CGI: Lots of excitement at the beginning until people realize you need natural effects,” he added.

And our conversation comes full circle — maybe we’ll eventually value human creativity and skill again more at some point.

Until then, Kenny said, he will continue transitioning into new creative topics.

“I have to be an artist. It’s the only thing I know.”

As he hands me the sketch, I let him know how profound that statement is.

And I leave, wondering what to make of it.[…]

Read more: www.intelligencebriefing.substack.com

Der Beitrag AI’s Swiss Watch Effect: The Value Of Human Creativity And Skills erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

For the conference details, agenda, speaker line-up, and handouts CLICK HERE.
For the conference recording CLICK HERE
“The AI Trajectory 2024. Invest for Impact Wrap-up”

Yesterday, at the “AI Trajectory 2024 – Invest for Impact” virtual conference, we embarked on an enlightening journey, exploring the multifaceted role of AI across various sectors. From dissecting investment strategies in AI to leveraging its potential in healthcare, the event unfolded as a tapestry of insights, innovations, and forward-thinking discussions. Esteemed speakers from diverse industries shared their profound expertise, shedding light on the transformative impact of AI and setting the stage for a future where technology and humanity coalesce more seamlessly than ever. This article serves as a wrap-up for those who missed this illuminating event, encapsulating the key highlights and takeaways from our panel of visionary experts.

Why Invest in AI Today

With Petra Vorsteher and Ragnar Kruse.

We stand at the threshold of a transformative era, where AI’s role in our lives is not just supportive but deeply integral. Investing in AI today is not just an option but a necessity, propelled by its rapid technological maturity and the increasing attention it commands at the top echelons of business leadership.

Dalith Steiger, Co-Founder of SwissCognitive, World-Leading AI Network, Petra Vorsteher, Founding Partner AI.FUND; Founding Partner AI.INVEST, Ragnar Kruse, Founding Partner & Managing Director, AI.FUND; Founder, AI.HAMBURG

The Future of Investments in AI Foundation Models

With Jacques Ludik, Cinderella Amar, David Shrier, Nils Reimers, John Wesley

The discussion around AI foundation models marked a pivotal shift in perspective. It’s a landscape where democratization of technology grapples with security and ethical use. For investors, grasping these nuances is vital for safeguarding their interests and remaining ahead in a rapidly changing environment. This includes striking a balance between harnessing proprietary data and adapting to the trend of democratization. They must navigate a world where foundational AI models, once the domain of a select few due to high development costs, are now becoming more accessible, prompting a need for innovative and sustainable business models.

Jacques Ludik, Founder & CEO, Cortex Logic & Cortex Group, Founder & President, Machine Intelligence Institute of Africa, Cinderella Amar, Managing Partner, Glass Ventures, David Shrier, Professor of Practice, AI & Innovation, Imperial College Business School; Founder & Managing Director, Visionary Future, Nils Reimers, Director of Machine Learning, Cohere, John Wesley, Senior Investment Director, NVentures

Bringing the Reading Gap: AI Innovations in Dyslexia Support

With Artem Pochechuev

The landscape of technology is undergoing a profound transformation with AI-driven initiatives, significantly impacting both education and practical applications. In the realm of education and personal development, AI is revolutionizing the way we support individuals with dyslexia, serving as a prime example of how human-centric AI systems are unlocking people’s full potential.

Artem Pochechuev, AI Expert, Head of Data Science, Sigli

Healthcare Industry Transformation – Harnessing Automation, AI, and Talent

With Alexander Büsser, Laura Modiano, Anita Puppe, Heinrich Zetlmayer, Jose Pedro Almeida

The integration of generative AI in healthcare is creating a paradigm shift by efficiently structuring and analyzing complex data, leading to improved patient care and outcomes. This technology is crucial in consolidating fragmented data, transforming workflows, and the healthcare value chain, thereby enhancing efficiency and freeing resources for more critical tasks. Looking to the future, AI is pivotal in addressing global healthcare challenges, facilitating international collaboration and the sharing of medical data, and fostering a more unified and effective global healthcare system.

Alexander Büsser, Director Data, AI & Platforms, Idorsia Pharmaceuticals, Laura Modiano, Principal Venture Capital and Startups Business Development, EMEA AWS, Anita Puppe, Healthcare Industry AI Transformation Leader; Senior Consultant Strategy & Business Design, IBM iX DACH, Heinrich Zetlmayer, General Partner, yabeo; Founder & General Partner, Blockchain Valley Ventures, Jose Pedro Almeida, Chief AI Strategist for Healthcare, Advisory Board Member, Intelligence Ventures

Computer Vision in Water

With Omer Bar-Ilan, Co-Founder, CEO, Lynxight

Simultaneously, the integration of computer vision in aquatic environments marks the dawn of a new technological era. This technology is not only optimizing feeding patterns and enhancing navigation for underwater ROVs but also playing a critical role in public safety. Its capability to rapidly assess risks in water environments, such as swimming pools, for drowning prevention, is a testament to its versatility and impact, showcasing the diverse and transformative applications of AI across various sectors.

Omer Bar-Ilan, Co-Founder, CEO, Lynxight

STEM Evolution 2024: Redefining Science in the Age of AI and LLM

With Solomon Amar, Jeanne Lim, Stéphanie Bretonniere, Arnaud Quintin, Assaf Araki

In the evolving landscape of STEM, we are witnessing a redefinition of scientific approaches and applications. This includes the development of scalable science-based applications that leverage content generation, idea ideation, and knowledge navigation, all underpinned by engaging AI bots. In In the meantime, AI’s role in industries like food and automotive is transformative, optimizing resources like water for sustainability and reshaping customer engagement and internal processes. Of course, this progress is accompanied by challenges in privacy, bias, and the need for regulatory measures, highlighting the importance of a balanced and ethical approach to AI integration in our society.

Solomon Amar, Founder & CEO, ALLSTARSIT; Founder, AI Labs, Jeanne Lim, Angel Investor, Co-founder & CEO, beingAI, Stéphanie Bretonniere, Founder & CEO, WE IMPACT.WORLD, Arnaud Quintin, VP, Organisation and Transformation, Renault Group, Assaf Araki, Investment Director, Intel Capital

Rhythmic Intelligence: AI’s Impact on Music and Health

With Walter Werzowa

Finally, we head how AI is revolutionizing the music industry by enabling anyone to create music, thereby enhancing creativity and democratizing the art form. Rather than feeling threatened, we can learn more about how currently available technology and AI can help us take our music-making skills to even higher levels creating works that were previously impossible.

Walter Werzowa, University Professor – University of Music and Performing Arts Vienna; Head Of Music, MYTHOS MOZART

If you missed our “The AI Trajectory 2024. Invest for Impact.” virtual conference, here you can find the video recording:

For the conference details, agenda, speaker line-up and handouts CLICK HERE

Der Beitrag Navigating the Future: The AI Trajectory 2024 – Invest for Impact Virtual Conference Wrap-up erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Global Investments Reshaping Tomorrow – SwissCognitive AI Radar

Welcome back to SwissCognitive’s AI Radar, the vanguard of artificial intelligence investment insights. In this edition, we’re tracking investments and deciphering the strategic manoeuvres and bold aspirations that are sculpting the AI landscape of tomorrow. This isn’t just about dollars and deals; it’s about vision and value creation in a world increasingly powered by intelligent technology.

From Google’s landmark $2 billion investment in Anthropic, challenging the OpenAI realm, to Britain’s £118 million commitment to nurturing AI skills, we are witnessing a fascinating global chess game of innovation. These moves tell us more than just where money flows; they paint a picture of where nations and corporations envision their future in an AI-integrated world.

The landscape is diverse and dynamic. In the U.S., a cradle of tech innovation, AI startups are springing up like stars in the night sky, each burning with potential. Meanwhile, the UK strategically fortifies its position, not just by funding but by opening doors to global AI talent. This is a testament to the understanding that advancing AI is not just about technology; it’s also about the human minds that drive it.

And it’s not just the tech giants or the Western world that are in this game. We see strategic partnerships forming in the Middle East with NEOM’s investment in autonomous mobility, Sequoia Capital’s growing AI portfolio indicating a broader investment trend, and insurtech startups like Sprout.ai revolutionizing traditional sectors.

Look beyond the surface of these investments and delve into what they signify for the future of technology, business, and society. It is crucial to understand not just the ‘how much’ but the ‘why’ and ‘what next.’

Previous SwissCognitive AI Radar: Billion-Dollar AI Bets.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag Global Investments Reshaping Tomorrow – SwissCognitive AI Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

“Generative AI: A New Frontier for VC Investments” Q&A with Heinrich Zetlmayer, Assaf Araki and Bo Percival

As the world continues to witness the transformative potential of Generative AI, and venture capitalists progressively invest in this emerging technology, your questions and concerns have never been more important.

In this follow-up article, we delve deeper into the thought-provoking questions gathered from our global audience during the recent “Generative AI: A New Frontier for VC Investments” conference. We’re thrilled to present you with a curated selection of responses from our distinguished panellists: Heinrich Zetlmayer, Assaf Araki, and Bo Percival.

Let’s keep the conversation going. Read on to explore these comprehensive responses from our experts, and get a front-row seat to the evolving narrative of Generative AI in the VC world.

For the conference details, agenda, speaker line-up, and handouts CLICK HERE.
For the conference recording CLICK HERE

Zeev Abrams: Although Heinrich mentioned that having proprietary models (and IPs) is important, considering the rate of change in the AI landscape (faster than any other field I’ve ever been in!), how important is that “barrier” for a startup, compared with getting market traction and a strong potential market?

[Heinrich Zetlmayer]: If you go too “thin” as a startup in the market you might get quick initial traction but the challenge will be to hold on to that beyond first 12 months. Also investors will challenge it. So you need a good strategy to expand from initial gains.

[Assaf Araki]: For the last 20 years, data scientists have been focused on open-source models; after the breakthrough in deep learning a decade ago, the reliance on open source has increased, and today, all the main DL libraries are open source. The AI community is collaborative and open source is a core value (data scientists are from Mars, and developers are from Venus, both CS but different in culture). Even if a company has a breakthrough in a proprietary model, it will only last for a short time as another innovation will overcome it. The core IP in AI should be at the product level. Bringing together an ensemble of models to create a stable product that optimizes prediction for the business KPI (vs. the highest model accuracy). Startups should focus on combining innovation across different algorithms and integrate it smoothly into their application; The company IP is to make the solution robust, doesn’t hallucinate, or become unstable in production while bringing real business value.

Q: Eleanor Wright: Will bias in venture capital lead to bias in AI?

[Heinrich Zetlmayer]: There are +10k venture capital firms. The question of bias in AI is an important one but i think goes beyond VCs.

[Bo Percival]: I think this is a great question and one that anyone in VC spaces need to consider carefully. We already see this happening as a larger proportion of AI based funding has automatically been funnelled into typical tech ecosystems (e.g. the Bay and The Valley). At the Venture Fund we advocate and act on making atypical investments for exactly this reason. It has been said that AI tech is an extension of the values of those who create it. To this point, that we need to ensure that there is diverse representation of those underrepresented values that we don’t traditionally see represented in frontier tech spaces. This may include emerging markets, diverse co-founders and investments in early-stage companies in atypical domains.

Q: Antonio Sainz: The real creators of AI/ML are a few players, for VC the way is more to find use case generators and use all the tools that exist and can be used, is this your point of view?

[Heinrich Zetlmayer]: Yes and No. I know of very deep AI/ML startups that are more small/mid-sized and can excel in their niche, and are attractive targets for VCs as well. But… AI is a general-purpose technology, so theoretically, you can use it anywhere, and therefore you need to select an area of focus as VC or investor. For us, we have seen that analyzing AI applications in industry verticals allows us to structure the complexity and yield attractive investment possibilities.

[Assaf Araki]: I agree that a small number of players are the creators of AI algorithms, but the creation of AI products is endless. AI is another way of writing SW by the algorithm, not the entire product. To build a financial service product, you must know how to write SW and have business acumen. That still needs to change with AI, and you still need business acumen. Context is highly needed to build a good product. Building models without context can take you a long way, but adding context is the last mile; without it, the product is incomplete.

[Bo Percival]: Particularly in the work of UNICEF, we believe strongly in AI work addressing real problems that face the less represented populations. We are passionate about problems and where there are tools created by the few, we want to ensure that are both accessible and inclusive of the many (or in many cases, the minority. The challenge we face is that those ‘few’ often represent a specific subset of the population who may not have or share the experiences of the many. For this reason, we believe VCs need to reflect carefully on how existing tools may include or exclude underrepresented populations and further amplify the very problems we are aiming to address.

Q: Danny Mwala: How will AI assist in the proliferation of cyber crime and crime criminals some who are state supported actors bearing in mind that there is a high deficit of Cyber security professionals around the globe?

[Heinrich Zetlmayer]: All technologies are very fast used also by criminals but we have seen in the crypto industry and in the cybersecurity industry that quickly startups are created that fight cybercrime and add more and more automation which in return alleviates a bit the lack of skilled staff. AI and ML will certainly contribute to automation of fraud detection. Law enforcement agencies are also a typical a sponsor/first client of startups in tech crime detection and prevention space.

[Bo Percival]: I am not an expert in cybercrime and there are people better positioned to answer this question. That being said, my experience tells me that not only do we have capacity gap globally, but we have particularly concerning capacity gaps in specific geographies and domains. The risk here, is that if this capacity is not filled in an intentionally inclusive and equitable way then vulnerable populations stand to be exploited at significantly higher rates than other groups.

Q: Kuzey Çalışkan: What will be the key industries in the future?

[Heinrich Zetlmayer]: I think we live in the “digital decade” which makes IT combined with ever more progress in the medical/health sector probably the most important sectors.

[Bo Percival]: Great question, I wish I had an answer to this one. To be honest, due to the rapid pace of change, I don’t think we truly know all the industries today that will be key for tomorrow. My only hope is that whatever they are, they are inclusive, equitable and accessible for people everywhere and not just restricted to the Western, Education, Industrialised, Rich and Deomcratised countries at the cost of others.

Q: 5. Arvind Punj: Can anyone comment on the change in the education system which is needed because of the LLM models impacting learning?

[Heinrich Zetlmayer]: I cannot give a complete answer here but the curriculums need to have more and more information technology in it because this is what everybody needs to be skilled at in working responsibly with it.

[Assaf Araki]: AI should be mandatory for undergrads in CS and Engineering, we see some early adopters, but this is still a grad school topic in general.

[Bo Percival]: Education is a key component of the work of UNICEF and discussions are already taking place on how we can leverage LLMs and similar technology for greater positive potential for education. I think because the broader conversation on this topic is so nascent it’s too early to say in which direction this change is taking us. What we hope at UNICEF is that we are able to harness these and future technologies to increase the access and effectiveness for education purposes, while at the same time not losing important key human factors that should not be lost. If these technologies increase critical thinking, creativity, and other similar skills, that is fantastic. However, we also envision a world in which every child has access to education and that the growth and development of children is not perceived through a lens where technology is seen as a panacea or a replacement to education methodologies that are not focuses solely on concepts of learning as a road toward computational thinking alone.

Q: Margaret Glover-Campbell: When it comes to regulation, how can we ensure multiple points of view are taken into consideration? How do we safeguard against views that are too restrictive or too liberal?

[Heinrich Zetlmayer]: Regulations are made by the lawmakers in each country or region. It is important that the various viewpoints are voiced enough in public with enough public discussion so that lawmakers notice.

[Bo Percival]: Regulation is a critical conversation in the ability to move the ethical and responsible adoption of AI around the globe. Therefore, it will be important for representation to be both diverse and equitable. It will be important for regulators to ensure that opportunities are provided for the engagement of different voices to shape the development of regulation, regardless of colour, culture, or creed. Aspiring to a single vision for all will likely lead to an outcome that benefits the majority and further reinforces existing inequities in society. What is defined as ‘too’ one way, or another often depends on where on that spectrum an observer places themselves. Ensuring that there is representation from all ends of the spectrum including those on the furthest margins may not safeguard against going ‘too’ far in one direction of the other, but at the very least it should hopefully at least ensure that no one is excluded in ways that have been all ‘too’ common in the past.

This being said, as mentioned by the Secretary General just last week, the UN should be playing a critical role in facilitating these kinds of discussions to ensure that there is a neutrality to how these conversations are being had and how we can push toward a more free, open, inclusive and secure digital future for us all to live in.

Q: Nanjun Li: Do you think the world will be more divided as generative AI deepening the division of labour?

[Heinrich Zetlmayer]: AI and automation will definetly bring changes to the labour market as it is a large productivity enhancer. On the positive it will alleviate some shortages on the labour market but much more work has to be done to understand the societal impacts.

[Bo Percival]: I think it’s hard to predict one way or the other, and as we know from previous experience, what divides us rarely comes down to a specific issue alone, let alone a specific technology. While technology does have both the capacity and the potential to amplify division, it also has the potential to unite.

Q: mi nova: What is your view of TAM for Conversational Voice AI​, as a front end to Web3 solutions?

[Heinrich Zetlmayer]: I don’t have a number for that but expect it to be the main interface in the mobile space.

Q: Boris Bend: Thinking beyond the atypical: How do you see the world changing once true AGI will be achieved and when do you personally expect that this may be achieved? (There are quite a few experts that expect that this could happen much faster than most people believe due to the current exponential progress of AI research.)

[Bo Percival]: As someone with a background in cognitive psychology, I think the idea of ‘AGI’ is something that is still somewhat contested. While there have been some notable reports of us being close, I think that there are still more questions than answers on this topic. It would be remiss of me to take a position or make a prediction on when I personally think this could happen, as I still believe there are still many questions on what ‘true’ would mean in this case. To be able to claim ‘true’ we would have to first accept an assumption based on traditional and somewhat outdated definition of ‘intelligence’. Even the tests we use these days are still contentious in terms of what they measure. We would also need to be able to say with assertion whether or not the imitation of human intelligence constitutes ‘true’ intelligence. While this is an interesting thought experiment, I think that to understand AGI we would first need to truly understand the ‘I’, and I believe that understanding is moving much slower than the current progress of technology.

Q: Any thoughts on Healthtech like Apple Vision for patient treatments? There is definitely a demand for this from the sector but until now, there are not much change. (Nurses have a heavy workload in this area (except for robot surgery and MRI scans)).

[Heinrich Zetlmayer]: Healthtech is a very large area for AI and there are many startups and AR/VR etc.. are additional important innovations. Until it comes to the patient, hurdles have to be overcome in each national health system which each has its own setup of medical care system between doctors/health providers, health insurances and regulation. That slows it.

[Bo Percival]: Health is another critically important topic area for UNICEF. In fact, this year The Venture Fund will be releasing a public call for applications related to health, both mental and physical, to which, I am sure the AI applications will be considerable.  The challenge however with health, is the difference between technology that is engaging and technology that is efficacious. As we all know, technology can be designed to engage users and keep them connected, however, what the technology sector struggles with is the time it takes to evaluate a health intervention to better understand it’s efficacy in the medical sense. I believe this creates an areas of high risk in the health sector. In a survey done in 2019, of the approx. 60,000 health related apps in the Apple and Play stores only around 3.5% had any empirical evidence to support their efficacy.

If we want to leverage AI effectively, we should be finding the problems in healthcare that are most suited to the value that AI brings and applying them to that. Unfortunately however, we start with the solution and try and find a problem. It is most concerning to me that this conversation and the breakthroughs in this area are led by tech companies and not necessarily by academic or health institutions. The risk we run here is that we are developing solutions for shareholders and that the most needed solutions may not be the most profitable ones.

Q: Eleanor Wright: How protectable is AI IP?

[Heinrich Zetlmayer]: I am not a lawyer who can answer that better from the legal side. The legal protection of IP is often overestimated, more often the topic is: do I have unique data sets, unique employees, know how to access, unique models and unique market access in combination that allow me a sustainable competitive advantage? Many base AI components will be and are available as open source or from providers.

[Assaf Araki]: See my reply to Arvind Punj above. It is irrelevant because of the pace of innovation and the enormous effort invested in global research.

[Bo Percival]: I’m not sure I’m the best one to comment on this. However, as a core part of UNICEF’s Venture Fund’s thesis, we invest intentionally in open-source products, including open data, open models and open products. For us, we see it as paramount that contributions to organisations like ours result in investments that deliver Equitable returns. It is for this reason that we would actually like less AI to be protected so it is transparent and can be leveraged to make the world better for children.

Q: Antonija Hinckel Osojnik: Do you consider environmental footprint of AI? This is now part of regulative negotiations.

[Heinrich Zetlmayer]: We expect a sharp decline in training and running costs for AI and there will be much technological development. It is therefore difficult to judge the impact, at least for us. So currently I think the best lever is at the level of data centers/computer farms and to make sure they are optimized.

[Bo Percival]: Yes, indeed, we can’t be working for future generations unless we are working for a better climate. In addition to the climate conscious practices that we have embedded into the organisation, we also invest in reviews and evaluations of our Fund and our portfolios to ensure that we are being not only climate aware but we are taking climate action.

Q: Marufa Bhuiyan: Based on the data and investment, Which country is the AI capital of the world?

[Heinrich Zetlmayer]: Silicon Valley, due to its combination of large tech firms and universities, certainly is a center but the landscape is rapidly evolving and we will have startups all over the global map. Geography will not be a good criterion for searching for great companies to invest in.

[Assaf Araki]: There are different ways to measure it. One is by publications in the main AI conferences such as NeurIPS; the second is by papers on arxiv, and the third is by location of the leading AI companies. Ultimately, it is like asking who is the c apital of CS around the world. You have many competencies centers in North America, Europe, China, and Israel.

There are different ways to measure it. One is by publications in the main AI conferences such as NeurIPS; the second is by papers on arxiv, and the third is by location of the leading AI companies. Ultimately, it is like asking who is the c apital of CS around the world. You have many competencies centers in North America, Europe, China, and Israel.

[Bo Percival]: Unfortunately, I don’t have the data to support an informed response to this. What I would say is that we should strive to ensure that no specific country or city attains a ‘dominance’ over the field of AI. If we have learnt anything from history, it is that access to these technologies is key for more equitable development. As a result, we advocate strongly to ensure that technologies and building the capacity to develop these technologies should be accessible to people not just based on where they live but based on what enables better livelihoods and development. We would hate to think that we repeat errors of times past and we want to strive for a digitally decolonised future.

About the Authors:

Heinrich Zetlmayer is the founder, CEO and managing partner of BVV. His journey with BVV started at the launch of Lykke Corporation; the global trading platform based on blockchain technology. Heinrich saw a need for the investment of blockchain activity in the market, and with Lykke and his partners he set out to create BVV. Heinrich has a unique experience as the previous Vice President of IBM, Co-CEO of ESL, and an active member of the board in Lykke and Skaylink.

Assaf Araki is an Investment Director in Israel. He joined Intel Capital in 2018. In his role, Assaf is focused on investing in data, analytics, and machine learning platforms and applications worldwide. He has been involved in several investments including Anyscale, Opaque, OtterTune, Ponder, and Verta. Before Intel Capital, Assaf was an engineering lead on the Intel AI team leading multiple machine learning projects to reduce cost, increase revenue, accelerate the process, and improve products.

Bo Percival is a ‘geek for good’ working at the intersection of technology, economics, innovation, and social justice, using his diverse qualifications in psychology, design, economics, marketing, and interpreting to promote positive development. Currently, he is serving as a Senior Advisor for UNICEF’s Office of Innovation Ventures team, applying his extensive experience in open innovation across various fields in over 25 countries worldwide.

Der Beitrag Unlocking the Generative AI Investment Frontier: Expert Q&A – Part 2 erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Penny Schiffer – “The Power of Data, Unleashed: Transforming the VC Industry with AI”

In the era of technological transformation, few areas of business remain untouched by the powerful ripple effects of digitization. At the heart of this wave are artificial intelligence (AI) and data-driven decision-making, which are rapidly transforming an array of industries, including venture capital (VC). To delve into this fascinating world and help us unravel its complexities, we asked one of the preeminent voices in the field — Penny Schiffer.

As the CEO and Co-Founder of Raized.ai, a Startup Coach, an Angel Investor, and a Global Thought Leader in AI in the VC industry, Penny embodies the intersection of technology and investment strategy.

Join us as we venture into this exciting and complex terrain, guided by her seasoned expertise.

How far has the VC industry come in terms of data-driven initiatives and AI?

We observe three phases in the industry:

• Up until 2019: some VCs were exploring how to source and evaluate startups with data, mainly large VC firms (like a16z or EQT) or very specialized smaller VC funds.
• 2019-2022: More VCs realize that data and information it contains are key and either start their own activities or look for helpful tools. Yet, this proves to be more difficult than initially thought and added value is sometimes hard to prove.

Most data pipelines struggle to deliver a favorable signal to noise ratio at manageable cost, allow for fast innovation without having to generate massive training datasets and complex, hard to maintain machine learning models.

Sidenote: It was more work for us at Raized.ai than we initially thought, too.

• 2023 and beyond: Using data has become mainstream, now more complex AI capabilities become relevant – and are finally feasible as large language models and autonomous agent technology become available.

Pure sourcing and enriching of startup data is not enough as we can soon facilitate use cases such as:

• Ad hoc market analysis for any given industry / startup that a VC sees
• AI enabled inbound: Dynamic application forms and ways to generate deal memos before meeting a startup in person
• AI enabled outbound: Autonomous identification of relevant startup companies, personalized outreach and assessment if a company should be taken to due diligence based on the information gathered
• Advanced company analytics taken the founders, funding and financials of a startup company into account

What are the reasons for VCs to embrace a data-driven approach?

There are mainly two reasons:
VCs can become more efficient (important in times of budgets that are harder to get) and they can increase their chances of not overlooking outlier opportunities.

Those VCs who play their narrow niche of expertise successfully and neither look for more or for better business may stay in their current state for another few years, i.e. until competition walks in or their niche transforms.

For all other VC’s and/or investors data make positive differences:

• Get a more complete overview over investment opportunities out there not to be missed
• Be more efficient, i.e. do more right investment decisions per month
• Over time improve your business and decision processes, as experiences from data driven decisions add to your personal and company knowledge.

What is your viewpoint on the buy versus build dilemma? Is it a choice between the two options or a combination of both?

Let me tell you a story: A couple months ago we had a call with a tech savvy associate from a large German VC fund. He was proud to tell me that they are now building their own startup discovery pipeline and that he will be live with it in October. Having gone through this myself I was almost envying him for his unrestricted optimism. He had no clue about the difficulty of getting hard to get web data at scale, the often boring work of creating and maintaining a data scheme and what happens if you have no proper orchestration and monitoring of your pipeline.

Last week, we had a chat again and discussed how he could leverage our data and add value to it for his fund.

We have seen that it consumes much more resources than initially thought to build and maintain a solid data pipeline that can identify and screen startup companies. And to make it into an engine that is really giving a VC firm a competitive advantage one will need to spend another hundreds of thousands of USD.

However, a standard system or generic database is not able to add value per se, so it will need customization and thus tech skills inhouse. This is probably only feasible for funds that have deeper pockets and/or specialize into an AI sourcing approach.

In short, our proposition to the VC industry simply is: Let’s do the core once and do this right. Then let everybody profit via tailored and personalized services. With Raized.ai we try to build customizable solution on a standardized platform.

“If you want to be fast, go alone. If you want to go far, go together.”

Do you know any company programming their own spreadsheet calculation program? Yet, it looks like very few VCs have a clear strategy on this, get interns and tech students working on half-hearted data-driven projects that lack critical mass and top management support to lead to data pipelines of even AI systems that can add real value to the VC firm.

When developing internal tools, which aspects of the value chain hold the highest importance for you?

We’re convinced an A to Z vertical solution is key, conditio sine qua non. It interconnects all relevant elements turning internet data into meaningful business action.
For relevant investment opportunity information this covers: Data sourcing – aggregation – enrichment – interpretation of content through algorithms including AI, personalized and configured presentation to client.

Leave one out and the vertical chain is broken.
Disregard the interplay between business and data sense and you just have another IT project.

What are the primary obstacles or limitations you face when considering data-driven initiatives?

On the technical side it’s keeping up with the new platforms and technologies, see e.g. availability of ChatGPT, relevant to our solution while simultaneously keeping up an operative, stable and running business. Continuous innovation and maintenance is key and more often than not underestimated upfront.
On the business side, adopting and embracing the additional horsepower which data-driven solutions bring to the table profits of clear commitment and leadership. Sparking enthusiasm for melting business knowledge with high tech is more than justified and key to do.

With the new way of developing models with large language models the complexity to integrate business knowledge with technical expertise rises even further: With prompt engineering, data scientists have little possibility to test the quality of what they are building as there are no large validation data sets required and they oftentimes need a subject matter expert to judge the quality of the system response.

What advice would you give to a VC firm seeking to adopt a more data-driven approach?

Be aware and enjoy that you embark more on a journey than reaching a destination. Data-driven approaches initially adapt to known business needs and processes. Simultaneously they allow for more efficiency / quality and may foster improvements or simplifications in business processes. It’s this mutual benefit which creates a cascade of improvement steps.

The more you are aware how you work today and why, the faster you can connect business and data.

About the Author:

Penny Schiffer is the CEO & Cofounder of Raized.ai, an AI engine for the VC industry. Named a global thought leader by datadrivenvc.io and a top 10 European female investor by Insider, Penny is an ex-VC turned entrepreneur with a background in data science. With experience investing in 30 startups and sourcing thousands of investment opportunities, she believes the industry is ready for disruption.

Der Beitrag The Power of Data, Unleashed: Transforming the VC Industry with AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Jacques Ludik, Founder & CEO, Cortex Logic & Cortex Group – “The Power of Generative AI: Exploring its Impact, Applications, Limitations, and Future – Redefining Business Performance with Generative AI”

It looks to be on course to have far-reaching implications for industries and society, and could potentially transform various job positions. From a rational optimistic perspective, if used in a smart, wise and responsible fashion, it can really amplify our humanity, but there are many challenges and risks that need to be navigated as we’ll also explore in this article.

The recent advancements in generative AI have also revolutionized several industries, such as art, music, fashion, and gaming. These models can perform a range of tasks, including text-to-image, text-to-audio, text-to-3D, text-to-code, and even text-to-science. They can optimize both creative and non-creative tasks, and have enormous implications for the industry and society as a whole. However, there are limitations and challenges that come with these models. One of the biggest challenges is the amount of time and computation power needed to run them, as well as the difficulty in finding adequate datasets. Additionally, these models can be biased due to the data they are trained on, and there is still a lack of understanding around the ethical implications of their use.

Despite these limitations, generative AI has enormous potential for growth and development in the future. As the technology advances, it will be important to address these challenges and limitations in order to fully realize the benefits of generative AI. Furthermore, individuals and businesses should stay informed on the latest developments in the field to take advantage of the opportunities presented by these models. Boston Consulting Group (BCG) has recently published a The CEO’s Guide to the Generative AI Revolution article emphasizing how business leaders should focus on how generative AI will impact their organizations and their industries and what strategic choices will enable them to exploit opportunities and manage challenges. They specifically mention that the choices are centered on three key pillars:

• Potential: through identifying the use cases that will differentiate their organization (i.e., discover their strategic advantage)
• People: through adapting organizational structures and prepare employees to support deployment (i.e., prepare their workforce)
• Policies: through setting up ethical guardrails and legal protections (i.e., protect their business)

At the upcoming SwissCognitive, World-Leading AI Network virtual conference Redefining Business Performance with Generative AI on March 28, 2023, we’ll be discussing topics such as: what are the generative AI-propelled opportunities across businesses, industries, and domains; asking if generative AI is possibly overhyped or underhyped; and what are the challenges and solutions. These questions will also be examined later in this article.

To get a balanced perspective on Generative AI and its application landscape, Sequoia Capital‘s “Generative AI: A Creative New World” article, which was written by a generative AI large language model (GPT-3) and human collaborators, discusses the four waves in Generative AI that started out with small models (pre-2015), then the race to scale with the Transformer neural network architecture for natural language understanding (introduced by Google Research in the paper “Attention is All you Need“) and OpenAI‘s Generative Pre-trained Transformer (GPT) models (GPT, GPT-2, and GPT-3), followed by the third wave of better, faster and cheaper in 2022, and killer Generative AI applications emerging in 2023 as the fourth wave of which the generative AI large language model ChatGPT is a prime example.

Apart from a plethora of startups focusing on the Generative AI tech stack and applications (both model and application layers for text, code, image, speech, video, 3D, and other), we can expect AI-infused applications from all the tech giants, in particular Microsoft (with the support of OpenAI‘s impressive AI technologies) and Google who will have their own suite of impressive and useful AI-fuelled offerings as well and is already bringing AI into Google’s Workspace. Google’s PaLM large language model is similar to the GPT series created by OpenAI or Meta’s LLaMA family of models. Microsoft has for example announced a new AI-powered tool called Microsoft 365 Copilot, which is currently being tested with selected commercial customers. This tool combines the power of large language models with business data and Microsoft 365 apps to increase skills, creativity, and productivity. This new service will essentially integrate the functionality of a ChatGPT-like large language model with most-used Microsoft applications like Word, Excel, PowerPoint, Outlook and Teams.

OpenAI has also recently released their much improved next generation GPT-4 model to the public on their ChatGPT Plus offering which can solve difficult problems with greater accuracy due to its broader general knowledge and problem solving capabilities. It has more advanced reasoning capabilities, can handle a much longer context (more than 25000 words of text), and is more creative and collaborative with respect to generating, editing and iterating with users on technical as well as creative writing tasks such as writing screenplays, composing songs or learning a user’s writing style. GPT-4 can also accept images as inputs and generate captions analyses and classifications. It is also safer and more aligned by being more likely to producing factual responses and less likely to correspond to request for disallowed content.

Reid Hoffman co-wrote a book with GPT-4 called Impromptu: Amplifying Our Humanity Through AI that offers readers a travelog of the future through exploring how AI, and in particular generative AI Large Language Models like GPT-4, can elevate humanity across key areas like education, business, justice, journalism, social media and creativity. It also explores how we might address risk as we continue to develop AI technologies that can boost human progress at a time when the need for rapid solutions at scale has never been greater.

This message is also aligned with some key thoughts in my book “Democratizing Artificial Intelligence to Benefit Everyone: Shaping a Better Future in the Smart Technology Era” which takes us on a holistic sense-making journey and lays a foundation to synthesize a more balanced view and better understanding of AI, its applications, its benefits, its risks, its limitations, its progress, and its likely future paths. Specific solutions are also shared to address AI’s potential negative impacts, designing AI for social good and beneficial outcomes, building human-compatible AI that is ethical and trustworthy, addressing bias and discrimination, and the skills and competencies needed for a human-centric AI-driven workplace. The book aims to help with the drive towards democratizing AI and its applications to maximize the beneficial outcomes for humanity and specifically arguing for a more decentralized beneficial human-centric future where AI and its benefits can be democratized to as many people as possible. It also examines what it means to be human and living meaningful in the 21st century and share some ideas for reshaping our civilization for beneficial outcomes as well as various potential outcomes for the future of civilization.

So let ‘s dive a little bit deeper. With a little bit of assistance from ChatGPT, let’s briefly explore Generative AI in more detail, the taxonomy of popular generative AI models, its applications, its business opportunities, its challenges and limitations, if it is currently over- or under-hyped, and its future.

What is Generative AI?

Generative AI is a subset of artificial intelligence that allows machines to create and generate new content. Unlike other types of AI, such as machine learning and deep learning, generative AI is focused on the creation of new data rather than analyzing existing data to make predictions.

At the heart of generative AI are generative models, which are mathematical algorithms that generate new data based on patterns learned from existing data. These models are trained on large datasets of images, text, or other types of data, and then use that knowledge to create new content that is similar in style, tone, or structure to the original data.

Generative models can be used for a wide range of applications, from generating images and videos to creating music and poetry. They can even be used to generate realistic human-like conversations or to generate synthetic data for use in scientific research.

There are several different types of generative models used in generative AI, including Variational Auto-encoders (VAEs), Generative Adversarial Networks (GANs), and autoregressive models (of which Recurrent Neural Networks (RNNs) and Transformer Neural Networks are examples). Each of these models works in a slightly different way, but they all share the goal of creating new data that is similar to the original data used to train the model.

In general, generative AI is an exciting field that is rapidly evolving and has the potential to revolutionize the way we think about creativity and innovation. By using generative models to create new content, machines can help humans unlock new ideas, insights, and possibilities that would otherwise be impossible to achieve.

How Does Generative AI Work?

Generative AI is built on a variety of technologies and techniques that work together to create new content. One of the most important technologies used in generative AI is neural networks, which are mathematical models that are designed to simulate the way the human brain works. These networks are used to analyze and learn from large datasets of existing content, and then generate new content based on what they have learned.

Another important technology used in generative AI is reinforcement learning, which involves training a model to learn from its environment by rewarding or punishing it based on its actions. Reinforcement learning is often used in gaming and robotics, where the goal is to teach a model how to make decisions based on a set of rules and objectives.

Generative adversarial networks (GANs) are another key technology used in generative AI. GANs work by pitting two neural networks against each other: one network generates new content, while the other network evaluates that content to determine if it is real or fake. This back-and-forth process helps to refine the generative model over time, resulting in more realistic and high-quality content.

Transformers and large language models have also played a major role in the recent advances of generative AI. These models are based on natural language processing (NLP) and can be trained to generate realistic, human-like language. This has led to the creation of advanced chatbots, language translation tools, and even algorithms that can write news articles or fiction stories.

Training data is also a crucial component of generative AI. To create accurate and high-quality content, generative models must be trained on large datasets of existing content. These datasets can come from a variety of sources, such as publicly available data, user-generated content, or proprietary data. The quality and diversity of the training data can have a significant impact on the accuracy and quality of the generated content.

As can be seen, generative AI is a complex and dynamic field that relies on a range of technologies and techniques to create new content. By combining these technologies in innovative ways, researchers and developers are pushing the boundaries of what is possible with artificial intelligence, and creating new opportunities for creativity and innovation.

A Taxonomy of Popular Generative Models

The January 2023 article ChatGPT is not all you need. A State of the Art Review of large Generative AI models provides, amongst others, a taxonomy of the main generative models published recently. The diagram below shows this taxonomy classified according to their input and generated formats. Over the past two years, a multitude of large generative models, such as Stable Diffusion and ChatGPT, have been introduced, showcasing impressive capabilities in tasks like automatic generation of artistic images and general question answering. Generative AI can effectively and creatively transform text to other texts (e.g. ChatGPT), text to images (e.g. DALLE-2 model), text to 3D images (e.g. Dreamfusion model), images to text (e.g. Flamingo model), text to video (e.g. Phenaki model), text to audio (e.g. AudioLM model), text to code (e.g. Codex model), and even generate scientific texts and algorithms (e.g. Galactica and AlphaTensor models, respectively).

Various Generative AI models have been developed through collaborations between different entities. Notably, Microsoft invested $1 billion in OpenAI to help with the development of their models, while Google acquired DeepMind in 2014. Academic institutions such as KAUST (King Abdullah University of Science and Technology), Carnegie Mellon University, and Nanyang Technological University Singapore collaborated to develop VisualGPT, while Tel Aviv University in Israel developed the Human Motion Diffusion Model. Additionally, some models were developed through collaborations between companies and universities. For instance, Stable Diffusion was developed by Runway, Stability AI, and Ludwig-Maximilians-Universität München, Soundify was developed by Runway and Carnegie Mellon University, and DreamFusion was developed by Google and University of California, Berkeley. The following diagram highlights some of the main generative AI models by notable developers.

Applications of Generative AI

Generative AI has numerous applications across various industries, ranging from art and fashion to gaming and healthcare. Some of the most exciting applications of generative AI include:

• Art and Design: Generative AI is used to create artwork, animations, and other visual content. Artists and designers can use generative models to generate new ideas, explore new styles and textures, and create unique pieces of art.
• Music: Generative AI is used to compose and generate music. Musicians can use generative models to create new melodies, harmonies, and rhythms, and to explore new genres and styles.
• Fashion: Generative AI is used to generate new fashion designs, patterns, and textures. Fashion designers can use generative models to explore new design ideas, create unique patterns, and generate custom-made garments.
• Gaming: Generative AI is used to create game worlds, characters, and narratives. Game developers can use generative models to generate new environments, create new characters, and generate custom-made game levels.
• Healthcare and Medical research: Generative AI is used in healthcare to generate synthetic data for research purposes. Researchers can use generative models to generate synthetic medical images, such as CT scans and X-rays, to train machine learning models.
• Finance: Generative AI is used in finance to generate financial forecasts and trading strategies. Financial analysts can use generative models to generate new investment opportunities, analyze market trends, and make predictions about future market conditions.
• Marketing: Generative AI is used in marketing to generate personalized content for customers. Marketers can use generative models to create custom-made advertisements, product recommendations, and other types of content based on customer data.
• Customer service: Generative AI-powered chatbots can be used to provide personalized customer service, answering questions and resolving issues in real-time.

AIMultiple has recently updated their top 16 generative AI applications in 2023 article to the Top 70+ GAI Applications in 2023 and divides the applications into two main categories: General applications (visual, audio, text-based, code-based, and other) and Industry-specific applications such as healthcare, education, fashion, banking, customer services, marketing, search engine optimization, and human resources.

It is clear that generative AI has the potential to transform many different industries and sectors, and to create new opportunities for innovation and creativity. As the field continues to evolve and advance, we can expect to see even more exciting applications of generative AI in the future.

What are the current applications and potentials of Generative AI in business?

Generative AI has a wide range of potential applications in business. Here are a few examples:

1. Content Creation: Generative AI can be used to generate content such as articles, reports, and social media posts, which can help businesses to create and distribute content more efficiently and effectively. It can also be used to generate personalized content for individual customers or segments.
2. Product Design and Prototyping: Generative AI can be used to design and prototype new products, enabling businesses to rapidly iterate and test new ideas. It can also be used to create 3D models and visualizations that can help businesses to showcase their products and designs to potential customers.
3. Customer Service: Generative AI-powered chatbots can be used to provide personalized customer service, answering questions and resolving issues in real-time. It can therefore be used to automate customer service interactions, enabling businesses to provide 24/7 support to their customers. It can also be used to generate personalized responses and recommendations based on customer data.
4. Fraud Detection: Generative AI can be used to detect and prevent fraud by analyzing patterns and anomalies in large datasets. This can help businesses to identify potential fraudsters and prevent fraudulent activities before they occur.
5. Supply Chain Optimization: Generative AI can be used to optimize supply chain operations by predicting demand, optimizing inventory levels, and identifying potential bottlenecks or inefficiencies in the supply chain.
6. Financial Services and Investment: Generative AI can be used to analyze financial data, make predictions about market trends, and make investment recommendations enabling businesses to make more informed decisions about investment strategies and portfolio management.
7. Healthcare and medical research: Generative AI can be used to analyze medical images, generate drug compounds, and develop personalized treatment plans for patients.

Generative AI has the potential to transform many aspects of business operations, enabling businesses to create new types of content and products, improve efficiency and productivity, and enhance customer experiences. As Generative AI technology continues to advance and businesses explore more of its possibilities, we can expect to see even more transformative applications and innovations in the future.

What are the generative AI-propelled opportunities across businesses, industries, and domains?

The World Economic Forum‘s Strategic Intelligence page highlights the following areas of impact and opportunities for Generative AI (see also diagram below):

• Health and Healthcare
• Education, Skills and Learning
• Arts and Culture
• Future of Media, Entertainment and Sports
• Future of Computing
• Civic Participation
• Future of Work
• Internet Governance
• Economic Progress
• Fourth Industrial Revolution

Generative AI has the potential to create many opportunities across businesses, industries, and domains. Here are a few examples:

1. Customization: Generative AI can help businesses to create personalized experiences for their customers by generating personalized recommendations, product suggestions, and even personalized content such as news articles or social media posts. This can help businesses to build stronger relationships with their customers and increase customer loyalty.
2. Innovation: Generative AI can help businesses to innovate by generating new ideas, designs, and prototypes. This can help businesses to stay ahead of the competition and create products and services that are tailored to their customers’ needs and preferences.
3. Automation: Generative AI can automate many business processes that are currently time-consuming and require a lot of human effort. This can increase efficiency and reduce costs, freeing up employees to focus on higher-level tasks.
4. Optimization: Generative AI can optimize business operations by predicting demand, optimizing inventory levels, and identifying potential bottlenecks or inefficiencies in the supply chain. This can help businesses to reduce waste and increase profitability.
5. Decision Making: Generative AI can provide businesses with new insights and perspectives that were previously not possible with traditional data analytics. This can help businesses to make better decisions and identify new opportunities for growth and innovation.
6. Creativity: Generative AI can help businesses to explore new creative directions by generating novel ideas and content. This can help businesses to differentiate themselves from competitors and stand out in crowded markets.

The opportunities created by Generative AI are wide-ranging and diverse, and they will continue to expand as the technology develops and matures. By leveraging the power of Generative AI, businesses can unlock new possibilities and create innovative solutions to complex problems.

How is business performance redefined by Generative AI?

Generative AI has the potential to redefine business performance in several ways. Here are a few examples:

1. Automation and Efficiency: Generative AI can automate various business processes that are currently time-consuming and require a lot of human effort, thereby increasing efficiency and reducing costs. For example, it can be used to generate reports, automate customer service interactions, or even create personalized marketing messages.
2. Product Innovation: Generative AI can help businesses to innovate new products and services by generating new designs, prototypes, and ideas. This can help businesses to stay ahead of the competition and create products that are tailored to their customers’ needs and preferences.
3. Decision Making: Generative AI can provide businesses with new insights and perspectives that were previously not possible with traditional data analytics. This can help businesses to make better decisions and identify new opportunities for growth and innovation.
4. Personalization: Generative AI can help businesses to create personalized experiences for their customers by generating personalized recommendations, product suggestions, and even personalized content such as news articles or social media posts.

From this we can see that Generative AI has the potential to revolutionize the way businesses operate, improving efficiency, driving innovation, and creating new opportunities for growth and expansion.

Is generative AI Overhyped or Underhyped?

The hype surrounding Generative AI is a topic of debate in the AI community. Some argue that it is overhyped, while others argue that it is underhyped.

On the one hand, some experts believe that Generative AI is overhyped. They argue that the current state of the technology is not as advanced as some of the media coverage suggests. They also point out that many of the applications touted as being powered by Generative AI are actually using other AI techniques, such as supervised learning.

On the other hand, some experts argue that Generative AI is underhyped. They believe that the technology has the potential to revolutionize many industries and domains, but that the full potential of Generative AI is not yet widely understood or appreciated.

In reality, the truth is likely somewhere in between. While it is true that the technology is not yet fully mature and that there are limitations and challenges that need to be overcome, Generative AI has already shown significant promise in a wide range of applications, from content creation and product design to healthcare and scientific research.

When people have limited knowledge about the underlying Generative AI technology and its applications, we also see a lot of hype. The diagram below which views ChatGPT through the lens of the Dunning-Kruger effect (which occurs when a person’s lack of knowledge and skills in a certain area cause them to overestimate their own competence), provides some perspective on how ChatGPT as an example can be on this spectrum of believing the hype and understanding reality. Apart from that, we know that ChatGPT also sometimes goes to the extent of providing an answer that seems grounded and factual, but sometime it is fabricated or hallucinated or just plain misleading.

While it is important to be realistic about the current state of Generative AI, it is also important to recognize its potential and to continue investing in its development and application. With continued innovation and investment, Generative AI has the potential to transform many aspects of our lives and our society.

The Future of Generative AI

The future of generative AI is promising, with new trends and technologies emerging that are poised to drive further innovation and advancement in the field.

Sequoia Capital‘s “Generative AI: A Creative New World” article includes the chart below that illustrates a timeline for how one might expect to see Generative AI models progress and the associated applications that become possible for text, code, images, video, 3D, and gaming applications.

The potential impact of Generative AI on business and society is significant and multifaceted. It will depend on how the technology is developed and deployed. With careful consideration of ethical concerns and risks, Generative AI has the potential to transform many aspects of business and society for the better. Here are a few key ways in which it could shape our world:

1. Increased efficiency and productivity: Generative AI can help businesses automate repetitive tasks and generate new types of content and products more quickly and efficiently, leading to cost savings and increased productivity.

2. Enhanced customer experiences: Generative AI-powered chatbots and personalized content can help businesses deliver more personalized and engaging experiences to their customers, improving customer satisfaction and loyalty.

3. Creation of new types of content and products: Generative AI has the potential to create new types of content and products that were previously impossible, leading to new business opportunities and innovations.

4. Ethical concerns and risks: As with any new technology, Generative AI raises ethical concerns and risks, such as the potential for bias and misuse. It will be important for businesses and society to consider these risks and develop appropriate safeguards.

5. Job displacement and re-skilling: Generative AI could lead to job displacement in some industries, while creating new job opportunities in others. It will be important for businesses and society to invest in re-skilling programs to help workers transition to new types of work.

One major trend that we are seeing is the increasing use of generative AI in areas such as healthcare and education, where it can be used to create personalized content and improve learning outcomes.

Another trend that is emerging is the use of generative AI in robotics and automation. As robots become more advanced and sophisticated, they will need to be able to generate their own ideas and solutions to complex problems. Generative AI can help to enable this by allowing robots to generate new ideas and strategies based on their environment and objectives.

In addition to these emerging trends, there are also new technologies being developed in the field of generative AI. For example, researchers are working on developing more advanced generative models that can generate highly realistic and accurate content, such as images and videos.

However, the increasing use of generative AI also raises concerns about its potential impact on the job market and society as a whole. As generative AI becomes more advanced and capable, it may lead to the automation of many jobs, particularly those that involve repetitive tasks or data analysis. This could have significant implications for the workforce, and may require new policies and regulations to address.

Moreover, generative AI also raises ethical concerns, such as the potential for the creation of deepfakes or other forms of misinformation. As such, it is important for researchers and developers to prioritize ethical considerations and ensure that generative AI is used responsibly and ethically.

The future of generative AI is likely to be shaped by a combination of emerging trends, new technologies, and ethical considerations. As the field continues to evolve and develop, we can expect to see further advances that have the potential to transform many different industries and sectors.

Challenges and Limitations of Generative AI

Despite the many potential benefits of generative AI, there are also several challenges and limitations that must be addressed. Some of these challenges include:

• Bias and Ethics: One of the most pressing challenges facing generative AI is the potential for bias and ethical concerns. Generative models can perpetuate and amplify existing biases in data, leading to the creation of biased or discriminatory content. Moreover, generative AI can also be used to create deepfakes or other forms of misinformation, which can have significant social and political implications.
• Difficulty of Creating Original Content: While generative AI can be used to create new content, it can be difficult to create truly original and innovative content that is not simply a recombination of existing data. Generating content that is both original and high-quality remains a significant challenge in the field.
• Complexity of Training: Training generative models can be a time-consuming and resource-intensive process, requiring large amounts of high-quality data and significant computing power. This can limit the accessibility of generative AI for smaller organizations or individuals.
• Risks Associated with Generative AI: As generative AI becomes more advanced, there are also risks associated with its use. For example, the ability to create convincing deepfakes could have significant implications for security and privacy. Moreover, generative AI could also be used for malicious purposes, such as creating fake social media accounts or generating fake news articles.

Herewith some potential solutions to some of the challenges:

• Bias: Generative AI models can sometimes produce biased outputs if they are trained on biased data. This can result in unfair or discriminatory outcomes. One solution is to carefully curate training data and test for bias at each stage of the development process.
• Ethical concerns: Generative AI models have the potential to generate new types of content and products that raise ethical concerns, such as deepfakes or fake news. A solution is to develop ethical guidelines and regulations for the development and use of Generative AI.
• Interpretability: Generative AI models can be complex and difficult to interpret, making it challenging to understand why they produce certain outputs. An obvious solution is to develop tools and methods for visualizing and interpreting the inner workings of the models.
• Scalability: Generative AI models can require significant computational resources, making it challenging to scale them up to handle large datasets or high volumes of traffic. A solution to address this is to optimize models for specific hardware architectures and to develop new hardware specifically designed for Generative AI.
• Security: Generative AI models can be vulnerable to attacks, such as adversarial examples, which can cause them to produce incorrect or undesirable outputs. One solution is to develop methods for detecting and mitigating these types of attacks.

The challenges and limitations of generative AI highlight the need for responsible and ethical use of the technology. Researchers and developers must prioritize issues such as bias and ethics, and work to address the challenges associated with creating truly original and high-quality content. At the same time, policymakers and society as a whole must also be aware of the potential risks associated with generative AI, and work to ensure that it is used in a responsible and ethical manner.

Conclusion

Generative AI has the potential to revolutionize a wide range of industries, from art and music to healthcare and finance. By using advanced algorithms and neural networks, generative models can create new and innovative content that was previously impossible to generate. However, as with any technology, there are also challenges and limitations that must be addressed, such as bias and ethics, the difficulty of creating original content, and the risks associated with its use.

Despite these challenges, the potential of generative AI is vast, and we can expect to see continued growth and innovation in the field in the years to come. Individuals and businesses can stay up-to-date on the latest developments by following industry news and attending conferences and seminars. By staying informed and taking advantage of the opportunities presented by generative AI, we can work together to build a better and more innovative future.

Democratizing Artificial Intelligence to Benefit Everyone: Shaping a Better Future in the Smart Technology Era

• Kindle and Paperback versions of the book is available on Amazon https://www.amazon.com/Democratizing-Artificial-Intelligence-Benefit-Everyone-ebook/dp/B08ZYW9487/.
• A PDF version is available on jacquesludik.com.
• The Audio book version is available on most major audio book market places (e.g., https://www.audible.com/pd/Democratizing-Artificial-Intelligence-to-Benefit-Everyone-Audiobook/B09LNL4JHC) .
• Listen also to the audio version on https://www.youtube.com/c/JacquesLudik and all major podcast channels (see links on jacquesludik.com)

See also the Democratizing AI Newsletter: https://www.linkedin.com/newsletters/democratizing-ai-6906521507938258944/

• AI’s Impact on Society, Governments, and the Public Sector (https://www.linkedin.com/pulse/ais-impact-society-governments-public-sector-jacques-ludik)
• The Debates, Progress and Likely Future Paths of Artificial Intelligence (https://www.linkedin.com/pulse/debates-progress-likely-future-paths-artificial-jacques-ludik)
• Beneficial Outcomes for Humanity in the Smart Technology Era (https://www.linkedin.com/pulse/beneficial-outcomes-humanity-smart-technology-era-jacques-ludik)
• Democratizing AI to Help Shape a Beneficial Human-centric Future (https://www.linkedin.com/pulse/democratizing-ai-help-shape-beneficial-human-centric-future-ludik)

Jacques will be speaking at the SwissCognitive World-Leading AI Network AI Conference focused on Redefining Business Performance with Generative AI on 28th March.

Der Beitrag The Power of Generative AI: Exploring its Impact, Applications, Limitations, and Future – Redefining Business Performance with Generative AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: htworld.co.uk – “29 health tech innovations which are truly changing healthcare”

1- Intuition Robotics | Israel

Intuition Robotics is on a mission to empower older adults to live happier, healthier and more independent lives at home.

The company’s award-winning product, ElliQ, is a proactive care companion for older adults – which is significantly improving healthcare for seniors ageing in place and dealing with the loneliness crisis.

Loneliness has been an epidemic for years, and it’s been exacerbated by social isolation brought on by the COVID-19 pandemic.

ElliQ Intuition Robotics has won several awards for its work with ElliQ including Fast Company’s Most Innovative Companies and the CES Best of Innovation award.

The company was founded in 2016 and is based in Tel Aviv with offices in San Francisco and Athens.

Intuition Robotics’ investors include: Toyota Ventures, Samsung NEXT, iRobot, OurCrowd, Terra Ventures and Venture Capital firms from California, Israel, Japan, and Asia.

• CEO: Dor Skuler
• Explore Intuition Robotics

2 – NovaXS | USA

NovaXS is a smart medical device company developing a needle-free injection device that syncs with a smartphone app to make the treatment process easier and pain-free for those suffering from chronic conditions and diseases.

One in four adults and two in three children have a fear of needles, only 50% of medications for chronic disease are taken as prescribed and the error rate for administering medications at home is up to 33%.

This all adds up to higher healthcare costs and results in negative health outcomes for patients that suffer from common illnesses like diabetes, growth hormone deficiencies, allergies and more.

NovaXS Biotech is on a mission to make medication self-administration as easy as making morning coffee.

The prominent medical device startup is focused on advanced drug delivery and users’ long-term health. Its patent-pending technology is a needle-free drug delivery platform. It allows patients to self-administer biologics subcutaneously or intramuscularly and track long-term treatment progress through IoT integration and software app.

• Founder: Alina Su
• Explore NovaXS

3 – Oxford Longevity Project | Oxford, UK

The Oxford Longevity Project aims to make the latest scientific breakthroughs in longevity accessible to the general public. It is a team of scientists and doctors uniting to change how we understand healthcare for good.

Together, they’ve capitalised on the public’s increased comfort with virtual learning and conferences by holding quarterly global webinars via main online platforms.

What makes OLP unique is their commitment to connect influential clinical practitioners and leading scientists on the same topic.

The speakers have two aims: First and foremost to translate the latest science into accessible information that patients and other interested non-scientists can action themselves; and, second, to more quickly connect doctors to the latest protocols.

Many researchers believe that it takes up to 17 years to translate breakthrough bench research into protocols used in clinics and OLP hope to change this by forging these connections.

One of the scientific discoveries that inhibits ageing are autophagy or cellular renewal, recycling and repair and this led to their previous webcasts ‘Autophagy and Alzheimers’ and ‘Autophagy and Ageing.’ to discuss this topic.

They’re committed to offering free information and empowering people to pursue health and living longer on their own terms, with accessible language, live seminars and free catch-up videos-all at no cost.

• Founders: Leslie Kenny & Oxford University Emeritus Professor Denis Noble
• Explore Oxford Longevity Project […]

Read more: www.htworld.co.uk

Der Beitrag 29 health tech innovations which are truly changing healthcare erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

copyright by www.forbes.com
To cut through the spin, Forbes partnered with venture firms Sequoia Capital and Meritech Capital to create our second annual AI 50, a list of private, U.S.-based companies that are using artificial intelligence in meaningful business-oriented ways. To be included, companies had to be privately-held and focused on techniques like machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to “understand” written or spoken language), or computer vision (which relates to how machines “see”).

The list was compiled through a submission process open to any AI company in the U.S. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). In total, Forbes received about 400 entries. From there, our VC partners applied an algorithm to identify the 100 with the highest quantitative scores and then a panel of eight expert AI judges identified the 50 most compelling companies.

“It’s not about creating some magic algorithms,” says judge Anima Anandkumar, a Caltech professor and Nvidia’s director of machine-learning research. “Focus on the problem and impact side.”

Among the notable trends this year: Augmented intelligence, which seeks to help humans do their jobs better and not replace them, is on the rise as the excitement over full automation loses some steam. Self-driving tech startups remain hot; the seven autonomous vehicle companies on this year’s list have raised over $3 billion in total venture capital. Another area to watch: AI applications to discover drugs or diagnose diseases faster.

In terms of valuation, at least 10 of the AI 50 are valued at $100 million or less, while 13 are unicorns valued at $1 billion or more. Silicon Valley remains the hub for AI startups, with 34 of 50 honorees coming from the San Francisco Bay Area. […]

read more www.forbes.com

Der Beitrag AI 50: America’s Most Promising Artificial Intelligence Companies erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright by www.forbes.com

So it’s only fitting that Kim, cofounder of Superb AI, has been selected as the featured honoree for the Enterprise Technology category of this year’s Forbes 30 Under 30 Asia list, leading a pack of several fellow honorees who founded startups based on AI. Since launching Superb AI in April 2018 with four cofounders, Kim has grown his startup to $2 million in revenues last year and 21 employees, fueled by increasing demand for AI. Profits are still in the future, but Superb AI also managed last year to join Y Combinator, a prominent Silicon Valley startup accelerator. So far, it has raised $2 million in funding from Y Combinator, Duke University and VC firms in Silicon Valley, Seoul and Dubai, giving it a valuation of $12 million as of March 2019.

Superb AI helps companies create and manage the huge amounts of customized datasets they need to build algorithms. “We wanted to solve these problems and lower the hurdle for industries to adopt machine-learning technology,” says Kim.

The inspiration for Superb AI came to Kim while he was pursuing a Ph.D. in robotics and AI at Duke University. When companies work on a machine-learning project, data must be manually labeled in order to train a computer in the algorithms—an expensive, laborious and error-prone process. “This is partly because building a deep learning system requires extreme amounts of labeled data that involve labor-intensive manual work and because a standalone AI system is not accurate enough to be fully trusted in most situations,” says Kim, who dropped out of the Ph.D. program to focus on Superb AI.

Superb AI uses deep learning AI to label and analyze images and videos up to 10 times faster than manual processes can, Kim says. About 30 companies already use Superb AI’s platform, mostly small businesses but also Samsung, LG, Qualcomm and Pokémon Go maker Niantic.

Now Kim is looking to expand further in North America and enter Europe. “We believe that AI should be widely adopted and used as a commodity across all industries to truly empower humans,” says Kim. “And we will make it happen.”

According to a McKinsey & Company report, about half of the 2,135 business leaders it surveyed in 2018 across various sectors said their companies deployed at least one AI-based system into its business. Another McKinsey report in 2018 estimates that AI could add around $13 trillion by 2030 to the current global economic output.

Hong Kong-based Gerardo Salandra is one of the business leaders contributing to the AI-driven economic growth. Salandra is cofounder and CEO of Rocketbots, an AI-powered chat automation platform for customer engagement. The AI element allows Rocketbots to learn messages in more than 15 languages, including English, Spanish and Chinese, then suggest a response to speed up conversations with customers.

According to Rocketbots, more than 10,000 companies use the platform, including Accenture, AXA and PricewaterhouseCoopers. Salandra is also the president of the Artificial Intelligence Society of Hong Kong, an association with more than 4,000 members dedicated to the development of AI and adoption of the technology.

Transforming Industries

One of the industries where AI is playing a big tangible role and driving transformation is finance. Last year, financial-services firms invested an estimated $5.6 billion on AI, according to research firm IDC, second only to the retail industry, which is estimated to have spent $5.9 billion.

Founded by Ashish Airon, CogniTensor is an example of how AI is being utilized in finance. The startup uses AI mainly to predict prices of commodities and energy. CogniTensor is working with India’s largest power trader, the state-owned Power Trading Corp., to help it with its power procurement strategies. In commodities, it started off with predicting prices for aluminum. Airon is also a member of the WHO – ITU Focus Group working on standardizing AI for health solutions.

AI technology is not just being used to improve business operations, but also for sustainable development. 30 Under 30 Asia list honorees Ayushi Mishra and Utkarsh Singh are doing just that.

The duo cofounded DronaMaps, a startup operating an AI-powered platform that creates and analyzes 3D maps to develop cities, villages and neighborhoods with pipeline planning, precision agriculture and flood mitigation, among others. Since its launch in 2016, it has mapped 600 square kilometers across six states in India. DronaMaps has worked with governments, universities and companies, including Reliance Industries, SAP and Johns Hopkins University. […]

Read more – www.forbes.com

Der Beitrag 30 Under 30 Asia 2020: The Startups Leveraging AI And Machine Learning To Transform Businesses erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.