Lifeboat Foundation

Today marks nine months since ChatGPT was released, and six weeks since we announced our AI Start seed fund. Based on our conversations with scores of inception and early-stage AI founders, and hundreds of leading CXOs (chief experience officers), I can attest that we are definitely in exuberant times.

In the span of less than a year, AI investments have become de rigueur in any portfolio, new private company unicorns are being created every week, and the idea that AI will drive a stock market rebound is taking root. People outside of tech are becoming familiar with new vocabulary.

Continue reading “A ‘people-first’ view of the AI economy” »

The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

KEYWORDS: Artificial intelligence, clinical decision support, electronic health record systems.

Artificial intelligence (AI) and related technologies are increasingly prevalent in business and society, and are beginning to be applied to healthcare. These technologies have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer and pharmaceutical organisations.

Every seven minutes a cyber-attack is reported in Australia.

Millions of Australians have had their data stolen in malicious attacks, costing some businesses tens of millions of dollars in ransom. The federal government is warning the country must brace for even more strikes as cyber gangs become more sophisticated and ruthless.

Continue reading “How cyber-crime has become organised warfare | Four Corners” »

Fake images and misinformation in the age of AI are growing. Even in 2019, a Pew Research Center study found that 61% of Americans said it is too much to ask of the average American to be able to recognize altered videos and images. And that was before generative AI tools became widely available to the public.

AdobeADBE +0.5% shared August 2023 statistics on the number of AI-generated images created with Adobe Firefly reaching one billion, only three months after it launched in March 2023.

In response to the increasing use of AI images, Google Deep Mind announced a beta version of SynthID. The tool will watermark and identify AI-generated images by embedding a digital watermark directly into the pixels of an image that will be imperceptible to the human eye but detectable for identification.

Continue reading “Google Launches Tool That Detects AI Images In Effort To Curb Deepfakes” »

Large language models (LLMs) are ushering in a revolutionary era with their remarkable capabilities. From enhancing everyday applications to transforming complex systems, generative AI is becoming an integral part of our lives.

However, the surge in demand for AI-powered solutions exposes a critical challenge: the scarcity of computational resources required to meet the growing appetite for logic and voice-based interfaces. This scarcity leads to a pressing need for cost-efficient platforms that can support the development and deployment of LLMs.

Industrializing AI software development will require transforming the processes for developing, deploying and maintaining AI systems from a research or ad-hoc approach into a structured, systematic and scalable industrial process. By focusing on cloud cost optimization and platform engineering, businesses can foster growth, profitability, and innovation in the field of AI.

Less than a year into the AI boom and startups are already grappling with what may become an industry reckoning.

Take Jasper, a buzzy AI startup that raised $125 million for a valuation of $1.5 billion last year — before laying off staff with a gloomy note from its CEO this summer.

Now, in a provocative new story, the Wall Street Journal fleshes out where the cracks are starting to form. Basically, monetizing AI is hard, user interest is leveling off or declining, and running the hardware behind these products is often very expensive — meaning that while the tech does sometimes offer a substantial “wow” factor, its path to a stable business model is looking rockier than ever.

“Personalize or Perish.” One of the leading newspapers aptly summarizes the critical nature of personalization 2.0, or hyper-personalization for businesses.

We live in an era where customers expect businesses to understand their wants and needs. Today, companies must meet customers’ needs and anticipate and exceed them. And for this, they must pivot to a digital-first mindset to create stronger, more authentic customer interactions.

How do they do this? Through a hyper-personalized, AI-powered business strategy where products, ads, and interactions are tailor-made for each customer or a group of customers.

The statement that may not be obvious is that the sleeping giant, Google has woken up, and they are iterating on a pace that will smash GPT-4 total pre-training FLOPS by 5x before the end of the year. The path is clear to 100x by the end of next year given their current infrastructure buildout. Whether Google has the stomach to put these models out publicly without neutering their creativity or their existing business model is a different discussion.

Today we want to discuss Google’s training systems for Gemini, the iteration velocity for Gemini models, Google’s Viperfish (TPUv5) ramp, Google’s competitiveness going forward versus the other frontier labs, and a crowd we are dubbing the GPU-Poor.

Access to compute is a bimodal distribution. There are a handful of firms with 20k+ A/H100 GPUs, and individual researchers can access 100s or 1,000s of GPUs for pet projects. The chief among these are researchers at OpenAI, Google, Anthropic, Inflection, X, and Meta, who will have the highest ratios of compute resources to researchers. A few of the firms above as well as multiple Chinese firms will 100k+ by the end of next year, although we are unsure of the ratio of researchers in China, only the GPU volumes.

Aude Oliva is a prominent Cognitive and Computer Scientist directing the MIT Computational Perception and Cognition group at CSAIL while also leading the MIT-IBM Watson AI Lab and co-leading the MIT AI Hardware Program. With research spanning computational neuroscience, cognition, and computer vision, she pioneers the integration of human perception and machine recognition. Her contributions extend across academia, industry, and research, making her a distinguished figure at MIT.

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Continue reading “Building Tools To Learn Human Brain Processes” »