Major AI Funding Shifts – SwissCognitive AI Investment Radar

The AI investment landscape continues to evolve, with new funding rounds and strategic commitments driving the industry forward. This week, key players across finance, technology, and infrastructure have made major moves to expand AI capabilities, focusing on both software and cloud expansion. Salesforce pledged $1 billion toward AI development in Singapore, while Honor committed $10 billion to integrating AI across its product line.

Investment priorities are shifting from AI chips to software, with analysts predicting that software firms will capture more value in the coming years. Microsoft is expanding its cloud and AI infrastructure in South Africa with a $298 million investment, reflecting the rising demand for AI-driven services. Meanwhile, Barclays analysts note that AI models are evolving from training-based systems to more advanced reasoning engines, signaling a new phase in AI capabilities.

DeepSeek’s breakthrough continues to drive activity in China’s venture capital sector, attracting fresh funding after years of stagnation. Elsewhere, private equity firms are adjusting their investment strategies to keep pace with AI-driven business transformations.

With AI playing a bigger role in stock markets, investor sentiment is shifting as automation takes on a larger role in financial decision-making. The rise of AI-powered fintech solutions, such as Finnomena’s partnership with Google Cloud, further highlights the increasing role of AI in investment strategies.

Stay tuned as we track these developments and more, bringing you the latest insights from the growing AI investment world.

Previous SwissCognitive AI Radar: $100B for AI Chips, $40B for 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.

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Funding, Strategy, and Real-World Impact – SwissCognitive AI Investment Radar

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Breaking Barriers to AI Funding – SwissCognitive AI Investment Radar

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Copyright: cio.com – “Hyperscalers Place Big Bets On AI As Cloud Spending Rolls On”

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SwissCognitive Guest Blogger: Chris Sherrington – “AWS Summit 2024 – Scaling your AI Executive Toys”

This year’s Amazon Web Services (AWS) Summit in London was an excellent opportunity to catch up on the progress in Generative AI (Gen AI). The resounding message was clear: if 2023 was about Gen AI proofs of concept (PoCs), then 2024 is all about scaling up.

Before embarking on the journey of scaling any Gen AI PoC, you need to correctly answer two key questions. First, do we have the right approach to data and cloud infrastructure, the foundations for our AI initiatives? And second, are we actively ensuring that we scale something that truly adds value? Let’s take a look at each in turn.

Cloud and Data Capability

Public Cloud is the obvious place to turn the talking black box that wowed your CEO into something that could do things for a customer. Hence it was no surprise the Summit focused on demonstrating the power of Gen AI in AWS’s ever-expanding toolbox.

AWS Bedrock does a great job mounting several Foundational Models (FMs) in a way that makes them consumable and modular. These digital brains can be transformed into AI agents capable of choosing options and taking action. Native serverless functions like message queues identify providers, and API gateways give these brains the limbs and muscles to do real work.

But things could go awry if your organisation still perceives software as a capital asset. Gen AI is more like a perishable good, subject to evolution and demanding constant adaptation.

Anything you build will be outdated in a few cycles – right now that’s months. Plus, everything in the cloud rots faster to begin with. If your organisation has never built and managed a digital product, you will struggle with the pace of Gen AI – even to just maintain the status quo.

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To get Gen AI right, you need to invest not only in building a product but also a growing capability of people, skills, and tribal knowledge. Think of that investment as the cost of a gym membership. Embrace it as a lifestyle change. And don’t forget the basics of cloud engineering – that’s as big a mistake as skipping leg day at the gym.

Your Gen AI will obviously be powered by your data, but your data may not be ready. I’m not talking about sophisticated and neatly labelled training data. I mean fundamental business data: lists of customers, product catalogues, employee credentials, and so on. Clean, high-quality sources held on suitably performant platforms. Do not underestimate how important that is: the point was made and made again in every use case presented at the Summit.

Useful AI products require useful data products (or as close as you can get with your current architecture). If your data strategy is as basic as a reporting strategy on top of an application strategy, you will struggle to feed your AI capabilities.

Your data supply chain is the bottleneck that will limit your scaling. Try to first scale in an area with teams that care about providing good data directly from their functions – and help them do it better still. But there is a caveat.

The trap of scaling Low value

The path of least resistance often leads to the place of lowest value. Many Gen AI PoCs were done at the margins of their enterprises due to safety concerns and the organisational inertia that any intrapreneur knows too well. There is a real risk of scaling something that is not worth it.

For example, I see a lot of HR-related use cases: chatbots that help employees understand policies or book time off. Using your employees avoids the discomfort of subjecting your customers to the new technology.

But that is also exactly what every HR system SaaS provider is working on. Unless you are an HR system SaaS provider, you are not learning anything useful from that Gen AI experiment. Something closer to how you create value for your customers would be a better choice.

Do not entertain sideshows just because they tick the right tech boxes. If you cannot describe the customer-relevant value streams, you lack sufficient strategic situational awareness. And if you don’t know when you are winning, why are you playing?

Key take-aways

Mature customers stand to gain a lot by productising FMs with AWS Bedrock. However, realising that potential requires a technical focus on modern data strategy. And alongside, you need a clear connection between your enterprise strategy and real-world value streams to see which areas are worth pursuing. Without that focus and clarity, your Gen AI PoCs will remain in the toybox.

About the Author:

Chris Sherrington has 20 years of experience in Enterprise Architecture, Technology Strategy and Innovation Leadership, making new tech work with old tech.

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Digital Summit Event Wrap-up – “Digital Summit beleuchtet die KI-Revolution” (Digital Summit Sheds Light on the AI Revolution)

The Digital Summit is a flagship event of the digital-liechtenstein.li location initiative. Its seventh edition, themed “From Algorithms to Applications – The AI Revolution,” highlighted the opportunities and challenges of Artificial Intelligence from various perspectives. Over 350 decision-makers and digital enthusiasts attended the event to engage in high-level presentations and informative discussion rounds.

Opportunity for State and Economy

On behalf of the host, Board President Lothar Ritter provided insights into the current activities of digital-liechtenstein.li and emphasized the importance of the Digital Roadmap for Liechtenstein. digital-liechtenstein.li had published the strategy paper four months ago, outlining over 50 measures to position Liechtenstein among the most modern countries in terms of digitalization by 2030. Subsequently, Markus Mayer, Deputy Head of the Office of Informatics, presented ongoing digitalization projects and e-government initiatives from the government’s perspective.

The first keynote speaker, Dalith Steiger, an AI expert and serial entrepreneur, guided the audience through the complex world of new technologies, discussing their possibilities and limitations with concrete examples. She explained how AI will capture every industry, leading companies to automate their processes, enhance efficiency, and develop new solutions.

Huge Potential for Switzerland and Liechtenstein

Data storage in the cloud plays a significant role in the deployment of AI. Christian Keller, head of Amazon Web Services (AWS) Europe, one of the world’s largest providers of cloud solutions, is convinced: “AI is still at the very beginning of a long race – similar to the early days of the internet. However, the impact that AI will have on the way we work is already very significant and will undoubtedly increase.” According to a recent AWS study, every third company in Switzerland already uses AI applications today. This trend is expected to massively increase in the coming years. The study estimates the potential for the Swiss economy at around 127 billion Swiss francs.

Interesting Glimpse into Estonia

After the refreshment break, the audience’s focus shifted to Estonia. The Baltic state has developed into a model country where virtually all administrative processes are digitized. Florian Marcus presented the basic principles for a digital society and explained how governments and administrations can excite their populations for digital services. Marcus, formerly with e-Estonia, now supports other countries in their digital transformation. After his speech, he discussed concrete experiences and examples with Liechtenstein’s Deputy Prime Minister Sabine Monauni and Christian Wolf, partner at BDO Liechtenstein and board member of digital-liechtenstein.li, to ensure the success of digitalization projects.

Do Not Miss the Connection

To conclude the event, Akshay Krishnaswamy, Chief Architect of Palantir, a leading global software company in the AI sector, took the stage. Flying in from San Francisco, he explained to the audience how data-driven decisions based on AI work. Governments, companies, and even intelligence agencies worldwide rely on software from Palantir to make critical decisions in real-time using their wealth of information and data. Krishnaswamy encouraged attendees to be open to the possibilities and challenges of new technologies to not miss the AI revolution.

AI Photobooth Draws Interest

Following the event, the over 350 guests had the opportunity to network and share their experiences during the networking apéro. A crowd favorite was the AI photobooth from the University of Applied Sciences Eastern Switzerland. Numerous participants took the opportunity to have a picture of their face taken and inserted into a background of their choice based on AI.

The Digital Summit is organized by the location initiative digital-liechtenstein.li. The initiative supports Liechtenstein in its digital transformation and is supported by around 60 companies and organizations as well as by the government and the princely house.

Original article in german.

Contact: Ministry of Interior, Economy and Environment
Patrick Stahl,
Event Agency Skunk AG
T +423 231 18 28
stahl@skunk.li

Gallery

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Copyright: builtin.com – “6 Ways Your Small Business Can Benefit From Machine Learning Solutions”

As AI and big data-powered innovations keep disrupting the way we live and work, the meaning of the term machine learning (ML) may remain opaque to many people despite its increased prevalence and business applicability.

At one time, implementing machine learning algorithms and integrating the necessary underlying technology was out of reach for many businesses because of the prohibitive costs and advanced knowledge necessary. Now, however, this is no longer the case.

Large tech companies like Google, Amazon, and Microsoft have found that sharing their cutting-edge ML tools can be a win-win proposition for the giants and SMEs (small and medium enterprises) alike. Doing so democratizes access to these advanced capabilities and their potential benefits.

Popular solutions like Google Cloud AutoML (which includes Vertex AI), Amazon SageMaker Autopilot, and Azure AutoML, in addition to open-source Python packages, have leveled the field. Business owners can incorporate these game-changing tools into their businesses and capitalize on all the benefits that they have to offer. These benefits range from increased visibility into their sales funnels and marketing and customer acquisition strategies to optimization of their human resources practices.

If you’re not sure what these benefits are, here are six of them to get started.

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Copyright: gradientflow.com – “Specialized Hardware for AI: Rethinking Assumptions and Implications for the Future” by Assaf Araki and Ben Lorica

Specialized hardware is enabling artificial intelligence models to operate at faster speeds and handle larger, more complex applications. As a result, several firms, including Habana, Graphcore, Cerebras, and SambaNova, have emerged since 2015 to capitalize on this trend. In May 2016, Google introduced the inaugural version of its Tensor Processing Unit (TPU) for inference, and Amazon Web Services (AWS) introduced Inferentia at the end of 2019 and Trainium at the end of 2020.

The market for AI semiconductors is expected to reach $76.7 billion by 2025, growing at a compound annual growth rate of 28.2%. The driving force behind this growth is data centers, which have been fostering innovation in AI hardware start-ups and hyperscalers. In fact, start-ups specializing in specialized AI hardware for data centers have raised more than $2.5bn since 2015:

Figure 1: A representative sample of specialized hardware for AI.

In a much cited 2018 report, Open AI documented the exponential increase in the amount of compute utilized in the largest artificial intelligence training runs since 2012. During the same time frame, a doubling period of 3.4 months was observed, a significantly shorter time period than the 2-year doubling period seen with Moore’s Law. The authors speculated that various hardware startups, focused on developing AI-specific chips, would drive this trend forward in the near future, and that new hardware would lead to significant increases in FLOPS/Watt and FLOPS/$ over the subsequent years.

Specialized Hardware for AI: A Quick Recap

We begin by re-examining some assumptions around hardware for AI. Several years ago, the industry developed expectations around AI hardware accelerators, and we revisit those assumptions and determine which ones still apply today.

Figure 2: AI Hardware – assumptions vs. reality.

In a much cited 2018 report, Open AI documented the exponential increase in the amount of compute utilized in the largest artificial intelligence training runs since 2012. During the same time frame, a doubling period of 3.4 months was observed, a significantly shorter time period than the 2-year doubling period seen with Moore’s Law. The authors speculated that various hw startups, focused on developing AI-specific chips, would drive this trend forward in the near future, and that new hardware would lead to significant increases in FLOPS/Watt and FLOPS/$ over the subsequent years.[…]

Read more: www.gradientflow.com

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Copyright: venturebeat.com – “IBM Watson opens up AI opportunities for software vendors with embeddable libraries”

IBM Watson in 2022 is a very different thing and is no longer a single system or even a single service, but rather is the product brand name for a growing set of IBM artificial intelligence (AI) capabilities. Those capabilities include natural language processing (NLP), speech-to-text and text-to-speech functionality.

Over the last several years, IBM has been steadily expanding its portfolio of AI services that run as a service on both the IBM cloud as well as on public cloud platforms from Google, Microsoft and Amazon. IBM Watson-powered AI services are also accessible via an application programming interface (API) that enables developers to remotely make use of the AI capabilities within applications.

What had been missing from IBM’s Watson portfolio, however, was the ability to directly integrate core AI services’ code into applications running locally on-premises — or in an organization’s own cloud deployment — without needing to trigger an API call to IBM.

“There is an entire army of independent software vendors (ISVs) and partners who are looking to embed AI into their software products and offerings,” Sriram Raghavan, vice president of AI research at IBM, told VentureBeat. “We have the opportunity to be the provider of enterprise AI core capability that they can use to focus on innovation.”

To help support ISVs and partners, IBM today announced the availability of three new AI software libraries that can be directly embedded into applications. The services include NLP, text to speech and speech to text.

What IBM Watson’s directly-embedded AI software libraries enable

Raghavan explained that the software library form factor adds an additional level of flexibility for software developers looking to make use of AI.[…]

Read more: www.venturebeat.com

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Copyright: hbr.org – “Robots Need Us More Than We Need Them”

Imagine trying to find a particular image within the National Football League’s historical archive of hundreds of thousands of videos. A single season produces more than 16,320 minutes (some 680 hours) of game footage. If you include coverage of every pregame, halftime, and postgame show, every practice, and every media interview, you have a seemingly endless amount of footage. And that’s just for one season.

To make it easier for staffers to create highlight reels and other media from all this material, the NFL partnered with Amazon Web Services in December 2019 to use artificial intelligence to search and tag its video content. The first step of the process required the NFL’s content creation team to teach the AI what to find. The team created metadata tags for every player, team, jersey, stadium, and other visually recognizable content it wanted to identify within its video collection. It then combined those tags with Amazon’s existing image-recognition AI system, which Amazon had already trained on tens of millions of images. The AI was able to use both sets of data to flag relevant imagery within the video library, and the content creation team was able to approve each tag in just a few clicks. Whereas employees once had to manually search, find, and clip each video, store it in a repository, and then tag the video with metadata, Amazon’s AI automated most of the process.

In a previous HBR article (“Collaborative Intelligence: Humans and AI Are Joining Forces,” July–August 2018), we described how some leading organizations are defying the conventional expectation that technology will render people obsolete—they are instead using the power of human-machine collaboration to transform their businesses and improve their bottom lines. Now several companies are not merely out-innovating their competitors with this approach; they’re turning even more decisively toward human-centered AI technology and upending the very nature of innovation as it was practiced over the previous decade.

In the NFL’s case, for example, AI accelerated the image-recognition process, but the system would have failed without employees determining which data needed to be uploaded and then approved. And the NFL didn’t simply hand the job of making highlight reels over to AI; content creation experts performed that work, but they did it faster and more easily thanks to AI’s unique ability to quickly sort through massive volumes of information. […]

Read more: www.hbr.org

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