Lifeboat Foundation

Not without human level hands. and should be 1. on list. and i dont see it til 2030 at earliest.

Robots are making their first tentative steps from the factory floor into our homes and workplaces. In a recent report, Goldman Sachs Research estimates a $6 billion market (or more) in people-sized-and-shaped robots is achievable in the next 10 to 15 years. Such a market would be able to fill 4% of the projected US manufacturing labor shortage by 2030 and 2% of global elderly care demand by 2035.

GS Research makes an additional, more ambitious projection as well. “Should the hurdles of product design, use case, technology, affordability and wide public acceptance be completely overcome, we envision a market of up to US$154bn by 2035 in a blue-sky scenario,” say the authors of the report The investment case for humanoid robots. A market that size could fill from 48% to 126% of the labor gap, and as much as 53% of the elderly caregiver gap.

Continue reading “Humanoid Robots: Sooner Than You Might Think” »

Lawrence Livermore National Laboratory has long been one of the world’s largest consumers of supercomputing capacity. With computing power of more than 200 petaflops, or 200 billion floating-point operations per second, the U.S. Department of Energy-operated institution runs supercomputers from every major U.S. manufacturer.

For the past two years, that lineup has included two newcomers: Cerebras Systems Inc. and SambaNova Systems Inc. The two startups, which have collectively raised more than $1.8 billion in funding, are attempting to upend a market that has been dominated so far by off-the-shelf x86 central processing units and graphics processing units with hardware that’s purpose-built for use in artificial intelligence model development and inference processing to run those models.

Cerebras says its WSE-2 chip, built on a wafer-scale architecture, can bring 2.6 trillion transistors and 850,000 CPU cores to bear on the task of training neural networks. That’s about 500 times as many transistors and 100 times as many cores as are found on a high-end GPU. With 40 gigabytes of onboard memory and the ability to access up to 2.4 petabytes of external memory, the company claims, the architecture can process AI models that are too massive to be practical on GPU-based machines. The company has raised $720 million on a $4 billion valuation.

Notion wants to write you a poem. The popular note-taking and database app has released Notion AI in private alpha today, becoming the latest consumer technology company to incorporate generative artificial intelligence.

In response to a request from a user, the new functionality can create functional scaffolds for blogs, social media posts, and other assets. A meeting agenda, press release, brainstorm, or poem can also be generated by Notion AI.

Aside from that, Notion AI can speed up the research and editing process for writers. The AI can, for example, analyze and summarize articles, pulling out critical points and action items.

Continue reading “Notion Releases Alpha of Generative AI Copywriting Tool” »

Scientists from the Department of Mechanical Engineering at Osaka University introduced a method for manufacturing complex microrobots driven by chemical energy using in situ integration. By 3D-printing and assembling the mechanical structures and actuators of microrobots inside a microfluidic chip, the resulting microrobots were able to perform desired functions, like moving or grasping. This work may help realize the vision of microsurgery performed by autonomous robots.

As medical technology advances, increasingly complicated surgeries that were once considered impossible have become reality. However, we are still far away from a promised future in which microrobots coursing through a patient’s body can perform procedures, such as microsurgery or cancer cell elimination.

Continue reading “3D-printing microrobots with multiple component modules inside a microfluidic chip” »

For the first time, researchers implemented a type of AI called a deep neural network into a photonic (light-based) device. In doing so, they’ve come closer to making a machine that processes what it “sees” like humans do, very quickly and efficiently.

For instance, the photonic deep neural network can classify a single image in less than 570 picoseconds, or nearly 2 billion images per second. To put things into perspective, the frame rate for fluid footage sits between 23 and 120 frames per second.

“Direct, clock-less processing of optical data eliminates analog-to-digital conversion and the requirement for a large memory module, allowing faster and more energy-efficient neural networks for the next generations of deep learning systems,” wrote the authors from the University of Pennsylvania.

As the US government begins to consider some of the legal implications for copyright in connection with the development and deployment of artificial intelligence, it is important to first step back to ensure that we are properly guided by context and a proper understanding of our goals — grounded in an informed grasp of the relationship of copyright to the development of AI, and a fair observation of the state of legal developments around the world. Far too many observers have oversimplified how various countries have addressed the relationship between copyright and AI. The reality is that all who have done so have rejected the notion that copyright is not implicated, and have developed legal norms which carefully limit the scope of any exceptions with an eye towards facilitating licensing, even when they seek to expand the development of AI as a national economic imperative.

I have written about the approach taken by the EU in the updated Copyright Directive, and note here that despite claims about Japan’s legislation, even their provisions — as manifested in the 2018 amendments, are designed to avoid conflict with the legitimate interests of copyright owners. While I don’t necessarily agree with Japan’s approach, it is important to highlight that even its exceptions, as I understand them: recognize that text and data mining/machine learning does in fact implicate copyright; apply only to materials that have been lawfully acquired; require that the use of each work is “minor” relative to the TDM effort; and provide that license terms must be honored. While it remains unclear to me that Japan’s goal of respecting copyright as required by international law has been achieved, it is important to understand that claims that Japan has removed copyright as an issue that must be addressed in the development of AI are inaccurate.

Machine learning programs mean even encrypted information can give cybercriminals insight into your daily habits.

Smart technology claims to make our lives easier. You can turn on your lights, lock your front door remotely and even adjust your thermostat with the click of a button.

But new research from the University of Georgia suggests that convenience potentially comes at a cost—your personal security.

Researchers solved a differential equation behind the interaction of two neurons through synapses, creating a faster AI algorithm.

Artificial intelligence uses a technique called artificial neural networks (ANN) to mimic the way a human brain works. A neural network uses input from datasets to “learn” and output its prediction based on the given information.

Continue reading “MIT reveals a new type of faster AI algorithm for solving a complex equation” »