AI Funding Highlights – SwissCognitive AI Investment Radar

This week’s AI investment landscape has been defined by diverging strategies, capital flows, and a widening discussion around equity, access, and economic consequence. On one side, the U.S. and EU are outlining ambitious visions for leadership. While the Stargate initiative pushes scale and speed, the EU’s dual strategy of financial commitment and regulatory positioning is placing ethical trust at the heart of its long game.

At the institutional level, signals of maturity are surfacing. Stanford’s AI Index highlighted pressure points shaping enterprise tech strategy, while BCG’s IT Spending Pulse underlined a shift: budgets are recalibrating as generative AI moves from novelty to core capability. Large investors are responding in kind—Bay Area-based SignalFire closed a $1 billion fund focused solely on applied AI companies, and Microsoft’s AI alliance with MSCI emphasizes the financial sector’s shift to AI-informed strategies.

From a regional angle, the Gates Foundation is betting $7.5 million on Rwanda as a launch point for AI scaling hubs in health, agriculture, and education. Canada attracted a CAD$150 million investment from Siemens for a global AI R&D center focused on battery production, while Italy’s Axyon AI secured €4.3 million for financial forecasting, and Ukraine’s QurieGen raised €2.2 million for AI-driven cancer drug R&D.

Meanwhile, a different class of firms is recalibrating customer interaction models. Arta Finance unveiled a suite of AI agents for portfolio insight, and startups skipping traditional funding stages—especially in Europe—signal a shift toward faster, more efficient capital strategies. But UNCTAD’s report reminds us that AI’s projected $4.8 trillion global impact comes with significant risks: unless addressed, the gap between early adopters and the rest could deepen.

This week’s updates confirm that the race is no longer about who adopts AI—it’s about how, and at what cost.

Previous SwissCognitive AI Radar: From Mega Rounds to Market Ripples .

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.

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Copyright: imd.org – “Exploiting the Ethically Positive Potential of AI”

The increasing use of generative AI is understandably causing great alarm amongst politicians, policymakers, businesses, and consumers. The unrestrained use of digital systems poses complex and far-reaching threats. Not only is AI greatly increasing global inequality, but tech giants are also massive users of energy, seriously impacting global climate-change goals. We are seeing unchecked violations of the right to privacy, with Big Tech capturing vast amounts of data to be sold to the highest bidder – usually without our knowledge or consent.

As with any new technology, society needs guardrails to protect its users from those who own and operate it. So, we need rules to regulate the use of AI – but how do we compose those rules, and what should they look like?

Big Tech wants to regulate itself and argues that it is uniquely well-placed to do so. This is tantamount to the poacher not so much turning gamekeeper but performing both jobs simultaneously. Letting it write the global rules for AI and the digital realm would be disastrous, given it has consistently created dangerous tools that exploit its users without regard for their interests, and which undermine democracy in the name of maximizing profits.

Historical precedents for successful technology regulation

I am optimistic, however, that we can come up with well-functioning global rules to constrain AI systems. One good example is how the world agreed to stop the use of ozone-depleting substances under the Montreal Protocol, which became effective in 1991 and continues to be amended in light of new scientific, technical, and economic developments. This precedent shows that humans can distinguish between what is technically possible – what we can do – and the things we should (or should not) do. Humanity has shown that it is able to make normative assessments and follow them through in its actions.[…]

Read more: www.imd.org

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Copyright: theconversation.com – “AI and Criminal Justice: How AI Can Support – Not Undermine – Justice”

Interpol Secretary General Jürgen Stock recently warned that artificial intelligence (AI) is facilitating crime on an “industrial scale” using deepfakes, voice simulation and phony documents.

Police around the world are also turning to AI tools such as facial recognition, automated licence plate readers, gunshot detection systems, social media analysis and even police robots. AI use by lawyers is similarly “skyrocketing” as judges adopt new guidelines for using AI.

While AI promises to transform criminal justice by increasing operational efficiency and improving public safety, it also comes with risks related to privacy, accountability, fairness and human rights.

Concerns about AI bias and discrimination are well documented. Without safeguards, AI risks undermining the very principles of truth, fairness, and accountability that our justice system depends on.

In a recent report from the University of British Columbia’s School of Law, Artificial Intelligence & Criminal Justice: A Primer, we highlighted the myriad ways AI is already impacting people in the criminal justice system. Here are a few examples that reveal the significance of this evolving phenomenon.

The promises and perils of police using AI

In 2020, an investigation by The New York Times exposed the sweeping reach of Clearview AI, an American company that had built a facial recognition database using more than three billion images scraped from the internet, including social media, without users’ consent.

Policing agencies worldwide that used the program, including several in Canada, faced public backlash. Regulators in multiple countries found the company had violated privacy laws. It was asked to cease operations in Canada.

Clearview AI continues to operate, citing success stories of helping to exonerate a wrongfully convicted person by identifying a witness at a crime scene; identifying someone who exploited a child, which led to their rescue; and even detecting potential Russian soldiers seeking to infiltrate Ukrainian checkpoints.[…]

Read more: www.theconversation.com

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Copyright: investmentexecutive.com – “Can AI Boost Market Returns?”

Artificial intelligence (AI) evangelists promise a revolution for the economy. However, deploying AI to gain an edge in financial markets and generate excess investment returns is increasingly in doubt.

A paper published by the International Monetary Fund in November 2023 said financial sector spending on AI is projected to more than double to US$97 billion by 2027, rising at a 29% compound annual growth rate.

And recent research from Statistics Canada singled out financial sector professionals as the cohort most exposed to AI disruption, alongside computer programmers and IT professionals.

“A broader segment of the labour force could be affected in an era when sophisticated large language models such as ChatGPT increasingly excel at performing non-routine and cognitive tasks typically done by highly skilled workers,” StatCan’s paper said.

Even as the financial sector appears ripe for AI disruption, the feasibility of companies replacing human workers with AI-powered technology is unclear. StatCan noted there are legal, financial and institutional barriers to replacing educated professionals with tech.

Further, the promised payoff from AI for financial firms will probably be limited.

In a report, analysts with Moody’s Investors Service Inc. point out that “harnessing AI is fraught with technical and organizational challenges.”

While the technology can automate routine tasks and enable portfolio managers to process data — and potentially glean novel investment insights — practical constraints mean AI deployment is unlikely to drive vastly improved investment performance.

According to the Moody’s report, generative AI tools aren’t particularly useful for uncovering investment signals.

“Using large language models to identify investment opportunities is tempting, but this approach faces several problems,” Moody’s said.[…]

Read more: www.investmentexecutive.com

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AI Investment Highlights and Strategic Developments – SwissCognitive AI Investment Radar

The latest edition of the SwissCognitive AI Investment Radar is here again, to bring you the updates from the AI funding world.

We begin with Elon Musk’s $5 billion xAI investment proposal, raising eyebrows over potential conflicts of interest with Tesla. Venture capital investment saw a 16% increase in Q1 2024, primarily driven by AI funding needs, with the U.S. leading this surge. Singapore’s Temasek Holdings plans a substantial $30 billion injection in the U.S. tech market, focusing on AI, semiconductors, and data centers over the next five years.

Meanwhile, Tiger Brokers’ AI chatbot, TigerGPT, is gaining traction despite some inaccuracies, proving its utility in financial data analysis. UK startup Fractile has exited stealth mode with $15 million in funding to develop AI chips that promise to revolutionize speed and cost efficiency.

Saudi Arabia’s ambitious $40 billion AI investment fund aims to partner with U.S. firms, supporting a diverse range of tech startups. In Canada, a significant portion of the population, particularly Gen Z, is turning to AI for financial management.

Google’s CEO defends its massive investments in AI data centers, while Silicon Valley’s tech giants are making a trillion-dollar leap of faith on AI infrastructure, betting on its transformative potential despite uncertain financial returns.

Join and explore these exciting developments and more, keeping you informed and ahead in the  evolving world of Artificial Intelligence investments.

Previous SwissCognitive AI Radar: AI Investment Booms and Market Shifts.

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.

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Copyright: cio.com – “How Guardrails Allow Enterprises To Deploy Safe, Effective AI”

AI guardrails are the technical tools companies use to ensure their systems conform to evolving policies and responsible practices. But with increasing options now available from big providers, startups, and the open-source community, finding the right solution isn’t always straightforward.

Google has finally fixed its AI recommendation to use non-toxic glue as a solution to cheese sliding off pizza. “Glue, even non-toxic varieties, is not meant for human consumption,” says Google Gemini today. “It can be harmful if ingested. There was a bit of a funny internet meme going around about using glue in pizza sauce, but that’s definitely not a real solution.”

Google’s situation is funny. The company that invented the very idea of gen AI is having trouble teaching its chatbot it shouldn’t treat satirical Onion articles and Reddit trolls as sources of truth. And Google’s AI has made other high-profile flubs before, costing the company billions in market value. But it’s not just the AI giants that can get in hot water because of something their AIs do. This past February, for instance, a Canadian court ruled that Air Canada must stand behind a promise of a discounted fare made by its chatbot, even though the chatbot’s information was incorrect. And as gen AI is deployed by more companies, especially for high-risk, public-facing use cases, we’re likely to see more examples like this.

According to a McKinsey report released in May, 65% of organizations have adopted gen AI in at least one business function, up from 33% last year. But only 33% of respondents said they’re working to mitigate cybersecurity risks, down from 38% last year. The only significant increase in risk mitigation was in accuracy, where 38% of respondents said they were working on reducing risk of hallucinations, up from 32% last year.

However, organizations that followed risk management best practices saw the highest returns from their investments. For example, 68% of high performers said gen AI risk awareness and mitigation were required skills for technical talent, compared to just 34% for other companies. And 44% of high performers said they have clear processes in place to embed risk mitigation in gen AI solutions, compared to 23% of other companies.[…]

Read more: www.cio.com

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Copyright: cio.com – “AI is driving productivity and wage increases: Report”

Business sectors using artificial intelligence are seeing significant gains in productivity while AI skills are commanding higher wages, according to a new PwC report.

Industries such as financial services, information technology, and professional services are seeing labor productivity growth nearly five times greater than industries with less AI integration, the consulting firm said in a statement.

The report also highlights that jobs requiring AI expertise are associated with a considerable wage premium in major labor markets, including the US, the UK, Canada, Australia, and Singapore.

In the US, for example, these positions can offer an average of 25% higher salaries than non-AI jobs. The wage premium varies across professions, reaching 18% for accountants, 33% for financial analysts, 43% for sales and marketing managers, and 49% for lawyers.

This wage disparity is consistent across all analyzed markets, with AI skills consistently valued higher.

The report, analyzing over half a billion job advertisements across 15 countries, indicates that AI could enable many nations to overcome long-standing low productivity growth. This could lead to economic development, higher wages, and improved living standards, PwC added.

Upskilling imperative

The report also pointed out that job postings for AI-related positions are increasing 3.5 times faster than the overall job market. For every AI job listed in 2012, there are now seven.

However, the situation also demands more effort in skill development. Occupations significantly exposed to AI are experiencing a 25% faster change in skill requirements compared to those less exposed to it.[…]

Read more: www.cio.com

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The AI World Tour – SwissCognitive AI Investment Radar

Welcome to this week’s SwissCognitive AI Investment Radar, where we span the globe to bring you the latest and most exciting developments in AI investments. This edition celebrates the international reach of AI, showcasing how innovation and capital are intertwining across continents to fuel the AI revolution.

From Microsoft’s substantial $2.9 billion initiative in Japan to the ambitious plans of Nvidia and Indosat in Indonesia and Italy’s strategic commitment through its state lender, the world is witnessing a unification in the pursuit of AI excellence. In the US, investors are rallying to support Elon Musk’s xAI, potentially raising $3 billion, while in Saudi Arabia, a $40 billion investment aims to position the nation at the forefront of AI advancements.

We’re observing an interesting blend of fear and optimism driving tech giants and governments alike to invest heavily in AI startups and infrastructure. However, amidst this rush, cautionary tales emerge about the authenticity of AI claims, highlighting the importance of integrity in this booming sector.

Canada’s proactive AI investment measures further emphasize the global recognition of AI’s transformative potential, from enhancing private equity investing to reshaping the infrastructure landscape with AI-driven energy transitions.

As AI’s impact transcends borders, it’s clear that the pursuit of AI innovation is a collective journey towards a smarter, more connected world.

Let us navigate you through these exciting global developments in AI investments, reminding us of the boundless potential that lies in collaborative innovation.

Previous SwissCognitive AI Investments Radar: The Billion-Dollar AI Investment Race.

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.

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SwissCognitive Guest Blogger: Arek Skuza  – “From Startup to Industry Leader: Drizly’s Success Story Through Data Analytics and Innovation – Beyond Efficiency: AI’s Creative Potential”

Drizly is an online drink delivery service that has seen great success capitalizing on technology and making shopping for drink products easier than ever before. Founded in 2012, Drizly has since become a leader in online drink delivery, offering over 4,000 beers, wines, and spirits from local retailers in more than 70 cities across the United States and Canada. 

The company’s innovative approach to online drink delivery has made them a leader in the food delivery industry, and its focus on customer convenience has paid off. In 2021, the company was acquired by Uber, the largest ridesharing, food delivery, and transportation network company in the United States, for 1.1 billion USD. During the COVID-19 pandemic, the company’s sales grew 800% due to the enhanced demand to buy drinks with limited exposure to the virus. Of course, the idea behind Drizly was innovative and popular. However, their business model and use of data analytics made the company stand out in a competitive market with many other product delivery and e-commerce startups.

The graphic below highlights Drizly’s success timeline. Companies often take years and years until they reach the so-called “breakthrough,” if they’re lucky. As you can see, Drizly was able to achieve this in just nine short years, which further highlights the effectiveness of data analytics.

Business Model and Strategy

Drizly’s business model revolves around providing customers with a convenient, fast, and easy way to purchase drinks online. Unlike traditional retail stores or licensed premises, Drizly has no physical locations. Instead, they partner with local retailers who carry the necessary licenses to be able to provide delivery services. Customers can order from Drizly and deliver their orders to their homes, offices, or designated pickup locations. 

Drizly’s strategy is focused on customer convenience and providing a seamless online shopping experience. They offer a wide selection of beverages from local retailers, ensuring customers get the freshest products. The company also has a comprehensive delivery network, ensuring that orders are delivered quickly and efficiently. Additionally, Drizly offers a variety of promotional codes that customers can use to get discounts on orders. It’s no wonder that Uber was eager to make the acquisition. With the success Uber Eats, Uber’s food delivery service, has had, adding Drizly to its arsenal was a strategic business decision to maintain its position as the leading food and now beverage delivery company in the United States.

Drizly’s revenue model can be seen in the graphic below. As you’ll see in the next section, advanced analytics fuels this model.

Data Analytics

Data analytics has been a key part of Drizly’s success. Data has allowed them to create an effective and efficient supply chain, optimize prices based on customer demand, and ensure that they provide customers with the best possible products. 

Drizly consulted Hashpath, a data analytics consulting company, to speed up their time-to-market. Hashpath helped ease and speed processes like authentication and onboarding. The advantage of utilizing Hashpath’s services was that it ensured the longevity and long-term success of these processes for users and administrators. In the case of Drizly, we can see that it’s often advantageous to outsource the incorporation of data analytics to another firm. Startups should not feel intimidated by the need to adopt analytics-based tools. In today’s business landscape, data analytics is at the forefront of success. As seen in the graph below, the advanced analytics market is growing at a compound annual growth rate (CAGR) of 15%, which is very fast. Companies like Amazon and Meta are industry giants due to their use of advanced analytics. Furthermore, the jobs with the highest demand are all data-oriented positions. Therefore, outsourcing data analytics-based approaches to outside firms is an effective way startups can integrate these profitable techniques. 

While Hashpath’s services were extremely valuable to Drizly, the firm was not working alone. They consulted Google’s Looker, a data exploration and discovery company. Looker partnered with Hashpath to help Drizly create new revenue streams, including direct monetization. Drizly is able to take advantage of direct monetization to sell its customer reports to vendors. The company receives loads of Big Data about customers, which is a highly valuable asset to various companies. 

Another advantage of Big Data is to evaluate customer behavior and preferences, analyze sales data, and track delivery times. This analysis helps the company make informed decisions about how to expand its service offering, determine what products customers are looking for, and ensure that orders are delivered on time. 

In addition to using Big Data for tactical decision-making, Drizly also uses data insights to inform its marketing strategies. The company can quickly determine what types of customers respond best to certain promotions and tailor messages and offers to meet the needs of each customer segment. This level of personalization has been a key factor in the tremendous success that Drizly has seen over the past few years. 

Drizly uses data from customer orders to make informed decisions about product selection and pricing. This information allows them to stock the products customers are looking for and ensure they have competitive pricing. The company also uses customer feedback to make product and service improvements, which further boosts customer loyalty. 

Conclusion

Since its founding in 2012, Drizly has become the leader in online drink delivery due to its innovative approach to convenience and data analytics and customer-centric focus. The company has created an efficient delivery system and optimized pricing strategies by leveraging the services of Hashpath and Google’s Looker. It now stands as a major player in the food industry thanks to Uber’s acquisition of the firm in 2021. With its unique data-driven approach, Drizly will continue to grow and expand its operations in the future. 

Without question, Drizly has proven that adopting an analytics-driven approach is essential for startups to succeed in today’s crowded business landscape. Startups can benefit from the use of data analytics and should not be afraid to outsource these services to experienced firms. By utilizing advanced data tools, companies like Drizly are sure to remain at the top of their industry and maintain a competitive edge.  

References

https://cloud.google.com/customers/drizly

https://cloud.google.com/looker/

https://drizly.com/

https://www.contentstack.com/blog/all-about-headless/use-predictive-analytics-augmented-analytics-make-most-of-data/

https://www.apptunix.com/blog/drizly-business-model/

Arek will speak at the SwissCognitive World-Leading AI Network AI Conference focused on Beyond Efficiency: AI’s Creative Potential on 5th September.

Der Beitrag From Startup to Industry Leader: Drizly’s Success Story Through Data Analytics and Innovation – Beyond Efficiency: AI’s Creative Potential erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

“Generative AI: A New Frontier for VC Investments” Q&A with Beny Rubinstein, Head of BV Israel, TIGER 21 Chair, TLV

Your questions reflected our curiosity, engagement, and the thirst for more knowledge, and we couldn’t leave them unanswered. Therefore, we’ve reached out to one of our esteemed speakers, Beny Rubinstein, to provide clarity and delve deeper into this fascinating subject.

In this Q&A article, we present Beny’s thoughtful and insightful responses to your questions. Covering a broad array of topics—from uses cases of Generative AI and venture capitalism through bias to LLM’s effect on education, this Q&A provides information for anyone keen to understand this dynamic technology and its role in shaping our future.

Whether you attended the event or are just catching up, we invite you to dive into the following article. Consider this a continuation of the conversation started at our event, an opportunity to revisit the frontier of Generative AI and deepen our understanding of this revolutionary technology. Let’s continue to explore, question, and pioneer together.

And this is just the beginning. We’re preparing yet another enlightening Q&A piece featuring insights from our other accomplished speakers. They’ll be addressing even more of your queries, shedding light on further complexities, and painting a broader picture of the Generative AI landscape.

Stay tuned for our next article, where we’ll be delving into more intricate facets of Generative AI, its potential impact on various sectors, and how it’s shaping VC investment strategies. Your curiosity fuels this journey, and together, we’re continuing to explore the breadth and depth of this transformative technology.

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

Q: Christopher Mott: If we are going to talk about productivity and operational use of AI INSIDE VCs, can we be concrete about what AI services and use cases — maybe using a lifecycle approach from POV of the VC? General benefit statements aren’t as useful as those we can read in the media or find on Google. Likewise, if APIs are important for the interoperability of operations in enterprises or other areas, what use cases and applications are being integrated to create a new and better way than the past?

[Beny Rubinstein]: Certainly! AI can provide valuable benefits to venture capital firms across various stages of the investment lifecycle. Here are some concrete use cases of AI in the context of venture capital:

Deal Scouting: AI-powered algorithms can analyze vast amounts of data from various sources such as news articles, social media, industry reports, and startup databases to identify potential investment opportunities. Natural Language Processing (NLP) techniques can extract relevant information and identify emerging trends, helping VCs discover promising startups more efficiently.

Due Diligence: AI can assist in the due diligence process by automating data analysis and pattern recognition. Machine Learning algorithms can analyze financial statements, market trends, and customer feedback to provide insights into a startup’s financial health, market potential, and competitive positioning. This helps VCs make more informed investment decisions and identify potential risks.

Market Analysis: By analyzing large datasets, AI algorithms can identify market trends, consumer behavior patterns, and competitive landscapes. This information helps VCs assess market opportunities and potential risks associated with a particular investment.

Portfolio Management: AI-powered dashboards and predictive analytics can help VCs track the progress and performance of their portfolio companies, identify areas for improvement, and make data-driven decisions regarding resource allocation and strategy adjustments.

Risk Assessment: By leveraging historical data, AI models can identify patterns and signals that may indicate risks related to financial stability, operational efficiency, or market dynamics. This enables VCs to proactively address risks and take appropriate measures to protect their investments.

Investor Relations: AI-powered chatbots and virtual assistants can enhance communication and engagement with limited partners (LPs).

It’s important to note that while AI can bring significant benefits to venture capital, human expertise and judgment remain essential throughout the investment process. AI should be seen as a tool to augment and assist VC professionals rather than replace them.

I wish I had AI tools to help me and my team do that Back in the days when I started Acelera Partners, a post-accelerator and micro-VC that invested in AI startups!  Hopefully for the next fund I will be raising that will be one of the main areas I will focus on so that VCs can also reap the benefits of AI for better outcomes of their own activities – “walk the talk”!

Regarding APIs (Application Programming Interfaces): they play a crucial role in enabling interoperability and creating new and improved ways of operating across various sectors. Here are some use cases and applications where the integration of APIs is driving innovation and transforming traditional practices:

1. E-commerce and Retail: API integrations have revolutionized the e-commerce and retail industry by enabling seamless connections between different systems. For example:

• Payment Gateway APIs allow businesses to securely process online transactions and accept various payment methods.
• Shipping APIs enable real-time tracking and logistics management, improving order fulfillment and customer experience.

2. Fintech and Open Banking: APIs are reshaping the financial services landscape by promoting interoperability and enabling new services. Key applications include:

• Open Banking APIs allow secure sharing of financial data between banks and authorized third-party providers, empowering users with better financial insights and enabling innovative services. This is a very hot area for banco BV, for example.
• Payment APIs enable easy integration of payment processing into applications, facilitating smooth and secure transactions.
• Investment APIs provide developers access to stock market data, trading capabilities, and investment tools, fostering the development of investment platforms and robo-advisory services.

3. Healthcare and Telemedicine: APIs are transforming healthcare by facilitating data exchange, interoperability, and telemedicine services. Examples include:

• Electronic Health Record (EHR) APIs enable secure and standardized sharing of patient health data across different healthcare systems, improving care coordination and interoperability.
• Telemedicine APIs integrate video consultations, appointment scheduling, and patient management systems, enabling remote healthcare delivery and telehealth applications.
• Health and Fitness APIs connect wearable devices, mobile apps, and health monitoring platforms, providing users with personalized health insights, and encouraging preventive care. I used that back in 2008 when I was the global product manager for Microsoft HealthVault (we were really early in the game and learned a lot!).

These are just a few examples highlighting how API integrations are driving innovation and creating new and improved ways of operating across industries. APIs foster interoperability, collaboration, and the development of novel services and applications, ultimately enhancing user experiences and unlocking new opportunities for businesses.

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

[Beny Rubinstein]: I can share insights on the relationship between bias in venture capital and bias in AI from a few different vantage points.  First, as someone who was employee number 6 in Microsoft Cloud & AI (Azure) global business development team. Second, as a venture capitalist with extensive experience in early-stage startups and AI investments (I founded and led the first partner of Microsoft Ventures in Latin America and invested in a dozen Israeli AI startups accelerated by Microsoft in Israel).  I actively engage with organizations like Women in Tech where I will be on a panel on July 3^rd in Tel Aviv addressing “How AI empowers diversity & inclusion by eliminating biases, fostering fair decision-making, and creating equitable opportunities for all”.

While bias can exist within the venture capital industry, it does not necessarily directly translate into bias in AI. However, it’s important to recognize that venture capital plays a crucial role in shaping the development and deployment of AI technologies. The biases present within the venture capital ecosystem, such as unconscious biases in investment decision-making, can indirectly impact the diversity and inclusivity of AI innovations.

When venture capitalists predominantly invest in startups led by individuals from specific demographics or with similar backgrounds, it can result in a lack of diversity in the teams developing AI technologies. This limited representation can inadvertently lead to biases in the data used to train AI models and the design decisions made during their development.

Bias in AI can emerge from various sources, including biased training data, algorithmic design choices, and the social and cultural context in which AI is deployed. Therefore, addressing bias in AI requires a comprehensive approach that encompasses not only venture capital but also diverse representation in AI research and development teams, inclusive data collection and labeling processes, rigorous testing and validation, and ongoing ethical considerations.

In summary, while bias in venture capital does not directly lead to bias in AI, it can indirectly influence the diversity and inclusivity of AI technologies. Addressing bias in AI requires a multifaceted approach, including diverse representation in AI development teams, inclusive data practices, and ongoing ethical considerations, in which venture capitalists can play a significant role by promoting diversity in their investment decisions and supporting initiatives aimed at addressing bias in AI.

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

[Beny Rubinstein]: Absolutely! This is an incredibly fascinating and crucial topic for the future of society!  The rise of LLMs has the potential to bring significant changes (and improvements!) to the education system. These models can provide access to vast amounts of information and assist in automating certain tasks traditionally performed by educators, such as grading and content generation. They also have the potential to personalize learning experiences, offering tailored feedback and adaptive resources to individual students.

However, the integration of LLMs into education also requires rethinking the role of human educators and the need for a balanced approach. While LLMs can provide valuable assistance, they cannot fully replace the essential aspects of human interaction, mentorship, and emotional intelligence that educators bring to the learning process.  To leverage the benefits of LLMs while mitigating their limitations, the education system must adapt. This adaptation may involve integrating LLMs as tools to support educators, emphasizing critical thinking, problem-solving, and creativity in the curriculum, and focusing on developing skills that are uniquely human and complementary to AI capabilities (after all, those will be the skills required soon; I suggest you refer to World Economic Forum’s “Future o Jobs Report 2023” for more details).

Furthermore, attention should be given to ethical considerations surrounding the use of LLMs in education, such as data privacy, algorithmic biases, and the potential for widening educational inequalities. Safeguards and guidelines should be put in place to ensure responsible and equitable deployment of LLMs in educational settings.  In summary, the integration of LLMs into the education system has the potential to revolutionize learning experiences, but careful thought and planning are required to strike the right balance between technology and human involvement, address ethical concerns, and ensure equitable access to education in the age of AI.  My former Professor at the University of Pennsylvania’s prestigious Wharton School of Business, Ethan Mollick, has been doing fascinating work on that space and is not only allowing his students to use ChatGPT, they are required to (see his interview to NPR here: ‘Everybody is cheating’: Why this teacher has adopted an open ChatGPT policy : NPR)

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

The impact of generative AI on the division of labour and potential division within societies is a topic of debate among experts. While it is challenging to predict the exact outcome, here are two perspectives on the subject:

• Potential for Division: Some experts argue that the advancements in generative AI could deepen the division of labour in society. As AI technologies become more capable of performing complex tasks, there is a possibility of job displacement in certain industries. This could lead to a division between those who have the skills and capabilities to adapt to the evolving job market and those who do not, potentially exacerbating existing inequalities.
• Potential for Convergence: On the other hand, some experts believe that generative AI has the potential to converge rather than divide societies. They argue that while AI may automate certain tasks, it can also augment human capabilities and create new opportunities. AI technologies can assist humans in performing complex tasks, enabling them to focus on higher-value work that requires creativity, critical thinking, and interpersonal skills. This could lead to a more inclusive and collaborative labour market, where individuals can contribute their unique strengths and expertise.

It is important to note that the impact of generative AI on the division of labour will depend on various factors, including the pace of AI adoption, the availability of reskilling and upskilling opportunities, government policies, and societal responses. To mitigate potential divisions, efforts such as investing in education and skills development, promoting inclusive AI adoption, and implementing supportive policies can play a crucial role in ensuring that the benefits of AI are accessible to a broader segment of the population.

Ultimately, whether the world becomes more divided or more convergent because of generative AI will depend on the choices made by individuals, organizations, and societies as they navigate the opportunities and challenges presented by AI technologies.  I recommend the Ted Talk “How we can face the future without fear, together” from Rabbi Lord Jonathan Sacks (Z”L) from 2017 on that topic which has more than 2M views as it’s a societal issue more than a technological issue (he is also author of best seller “Morality: Restoring the Common Good in Divided Times” which is extremely helpful to understand the background and context of some the dilemmas we are facing now).

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.)

[Beny Rubinstein]: Artificial intelligence can be broadly categorized into three main types: artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial superintelligence (ASI). Amongst these, AGI positions artificial intelligence at par with human capabilities. As a result, AGI systems can think, comprehend, learn, and apply their intelligence to solve problems much like humans would for a given situation.  In simpler words, if AGI is achieved, machines would be capable of understanding the world at the same capacity as any human being.

Regarding the timeline for achieving true AGI, opinions vary among experts. Some experts believe that AGI could be achieved sooner than anticipated due to the rapid progress in AI research, while others believe it may still be several decades away. Accurately predicting the timing of AGI is challenging due to the complexity of the problem and the many uncertainties involved.  The next decade will play a crucial role in accelerating the development of AGI. In fact, experts believe that there is a 25% chance of achieving human-like AI by 2030.  However, while there have been significant advancements in narrow AI domains, achieving AGI requires overcoming several technical hurdles, such as building robust generalization capabilities, addressing ethical considerations, and ensuring safety measures are in place.  Personally, I think it will take at least a decade to get there, if not more, due to those several challenges In the way of Artificial General Intelligence, but it could happen sooner with humans organize themselves better and collaborate more efficiently!

The development of AGI has the potential to bring about profound changes in various aspects of society. Here are some potential areas of impact:

• Automation and Labour: AGI could replace certain manual and cognitive tasks, leading to shifts in employment patterns and the need for upskilling and retraining.
• Scientific Advancements: AGI could accelerate scientific research and discovery by analyzing vast amounts of data, identifying patterns, and generating hypotheses. It may facilitate breakthroughs in areas such as medicine, climate change, and fundamental sciences.
• Socioeconomic Considerations: Achieving AGI raises important socioeconomic questions, including distribution of wealth, access to technology, and ethical considerations surrounding AI decision-making and control.
• Human-Machine Collaboration: AGI could enable more effective collaboration between humans and machines, augmenting human capabilities in decision-making, creativity, and problem-solving.

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

[Beny Rubinstein]: The landscape of AI dominance is evolving, with several countries making significant contributions. The United States has long been considered the AI capital of the world, with a robust ecosystem, substantial investments, and leading tech companies. However, recent developments highlight China’s emergence as a strong contender, with significant investments, a focus on AI research, and a national strategy to become a global leader. Israel, on the other hand, has gained prominence in AI startups and innovation, benefiting from a thriving tech ecosystem and strong research and development efforts. Sam Altman, CEO of Microsoft-backed OpenAI and ChatGPT creator took part in a talk at Tel Aviv University in Tel Aviv, Israel on June 5, 2023, and spurred some ideas on how to establish a national policy and strategy for the use and development of AI during his meeting with the country’s President Isaac Herzog (bear in mind that over 400 multinational organizations have R&D Centers in Israel and a big chunk of AI innovation/development for the “Big Tech” – Microsoft, Google, Amazon, META – is already done in Israel).  Other countries like Canada and the United Kingdom are also making noteworthy contributions to the AI landscape. While the United States, China, and Israel currently hold key positions, the competition in AI remains dynamic, with various countries vying for leadership in this rapidly advancing field.

According to the 2019 AI Index Report, published by the Stanford Institute for Human-Centered Artificial Intelligence in California, it is estimated that global private investment in AI in 2019 was more than US$70 billion. The US, China and Europe took the largest share; Israel, Singapore and Iceland were found to invest heavily in per capita terms. Start-ups founded on AI technologies are a major part of the ecosystem, garnering more than $37 billion globally in investments in 2019, up from $1.3 billion raised in 2010, according to the report. (Source: The race to the top among the world’s leaders in artificial intelligence (nature.com))

About Beny Rubinstein:

Beny serves as the Head of Banco BV in Israel and is a Strategic Advisor for Evolution.inc, an AI-for-AI Generator of AI systems. With an MBA from The Wharton School, and as a founding member of Microsoft Cloud & AI (Azure), Beny’s wealth of experience in the field is unparalleled. He’s committed to amplifying the legacy and impact of wealth creators around the world and helping them live a meaningful life.

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