Lifeboat Foundation

I attended Celesta Capital’s TechSurge Summit on February 13, 2023 at the Computer History Museum. In this piece I will talk about interview with Nic Brathwaite Founder and Managing Partner of Celesta Capital as well as Sriram Viswanathan (Founding General Manager of Celesta and heavily involved in venture investments in India), and a panel discussion by John Hennessy (Chairman of Alphabet).

In a companion article I will talk about my interview with John Hennessy, Chairman of Alphabet (Google’s parent company) and Vint Cerf, also with Google, during the TechSurge Summit.

He also said that the current cost of inference is too high and that Chat GBT is too often busy. He thought that there were opportunities to build AI systems trained and focused on particular uses, which would lead to smaller models and they would be more practical. He thought we are 1–2 years away from useful products, particularly in business intelligence. He also said that the use of AI allows us to program with data rather than lots of lines of code. Google was hesitant to produce something like Chat GBT, they didn’t want the system to say wrong or toxic things. He said that the tech industry needs to be more careful to encourage a civil society and that many tools, such as the Internet, were not anticipated to be used to do evil things.

Continue reading “AI, An Amplifier Of Human Intelligence” »

Snapchat is the latest company to get in on the AI frenzy. The company announced today that it’s launching “My AI,” a new chatbot running the latest version of OpenAI’s GPT technology that it has customized for its users. My AI is now available as an experimental feature for Snapchat+, the social network’s $3.99 a month subscription service.

The new chatbot will be pinned to the top of the Chat tab. My AI can do things like help answer a trivia question or write a haiku. My AI was trained to have a unique voice and personality that plays into its values about “friendship, learning and fun.” It has also been trained to adhere to the app’s trust and safety guidelines.

“My AI can recommend birthday gift ideas for your BFF, plan a hiking trip for a long weekend, suggest a recipe for dinner, or even write a haiku about cheese for your cheddar-obsessed pal,” the company wrote in a blog post. “Make My AI your own by giving it a name and customizing the wallpaper for your Chat.”

Engineers from UNSW Sydney have developed a miniature and flexible soft robotic arm which could be used to 3D print biomaterial directly onto organs inside a person’s body.

3D bioprinting is a process whereby biomedical parts are fabricated from so-called bioink to construct natural tissue-like structures.

Continue reading “3D bioprinting inside the human body could be possible thanks to new soft robot” »

The future of artificial intelligence is the question on all of our minds right now. AI has the potential of replacing us in every conceivable industry, leading to a potential dystopia. Humanity is suddenly gripped with this massive anxiety, but this is also our greatest opportunity.

Will this be the end of meaning?

Continue reading “FUTURE OF AI — The Fate Of Human Value — 4K” »

At the 2023 IEEE International Solid State Circuits Conference (ISSCC) in San Francisco this week, Irvine, Calif.–based Syntiant detailed the NDP200. This is an ultralow-power chip designed to run neural networks that monitor video and wake other systems when it spots something important. That may be its core purpose, but the NDP200 can also mow down the spawn of hell, if properly trained.

The exponentially expanding scale of deep learning models is a major force in advancing the state-of-the-art and a source of growing worry over the energy consumption, speed, and, therefore, feasibility of massive-scale deep learning. Recently, researchers from Cornell talked about Transformer topologies, particularly how they are dramatically better when scaled up to billions or even trillions of parameters, leading to an exponential rise in the utilization of deep learning computing. These large-scale Transformers are a popular but expensive solution for many tasks because digital hardware’s energy efficiency has not kept up with the rising FLOP requirements of cutting-edge deep learning models. They also perform increasingly impressively in other domains, such as computer vision, graphs, and multi-modal settings.

Also, they exhibit transfer learning skills, which enable them to quickly generalize to certain activities, sometimes in a zero-shot environment with no additional training required. The cost of these models and their general machine-learning capabilities are major driving forces behind the creation of hardware accelerators for effective and quick inference. Deep learning hardware has previously been extensively developed in digital electronics, including GPUs, mobile accelerator chips, FPGAs, and large-scale AI-dedicated accelerator systems. Optical neural networks have been suggested as solutions that provide better efficiency and latency than neural-network implementations on digital computers, among other ways. At the same time, there is also significant interest in analog computing.

Even though these analog systems are susceptible to noise and error, neural network operations can frequently be carried out optically for a much lower cost, with the main cost typically being the electrical overhead associated with loading the weights and data amortized in large linear operations. The acceleration of huge-scale models like Transformers is thus particularly promising. Theoretically, the scaling is asymptotically more efficient regarding energy per MAC than digital systems. Here, they demonstrate how Transformers use this scaling more and more. They sampled operations from a real Transformer for language modeling to run on a real spatial light modulator-based experimental system. They then used the results to create a calibrated simulation of a full Transformer running optically. This was done to show that Transformers may run on these systems despite their noise and error characteristics.

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Continue reading “What’s Going to Happen in The Next 40 Years?” »

Greatest beauty on the inside a beautiful mind.

Artificial Intelligence Education History.

Continue reading “Reconstructing Hypatia of Alexandria Using Artificial Intelligence” »