For all the promise and dangers of AI, computers plainly can’t think. To think is to resist – something no machine does

Computers don’t actually do anything. They don’t write, or play; they don’t even compute. Which doesn’t mean we can’t play with computers, or use them to invent, or make, or problem-solve. The new AI is unexpectedly reshaping ways of working and making, in the arts and sciences, in industry, and in warfare. We need to come to terms with the transformative promise and dangers of this new tech. But it ought to be possible to do so without succumbing to bogus claims about machine minds.

What could ever lead us to take seriously the thought that these devices of our own invention might actually understand, and think, and feel, or that, if not now, then later, they might one day come to open their artificial eyes thus finally to behold a shiny world of their very own? One source might simply be the sense that, now unleashed, AI is beyond our control. Fast, microscopic, distributed and astronomically complex, it is hard to understand this tech, and it is tempting to imagine that it has power over us.

But this is nothing new. The story of technology – from prehistory to now – has always been that of the ways we are entrained by the tools and systems that we ourselves have made. — Read More

Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages — systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages. A “wake-sleep” learning algorithm alternately extends the language with new symbolic abstractions and trains the neural network on imagined and replayed problems. DreamCoder solves both classic inductive programming tasks and creative tasks such as drawing pictures and building scenes. It rediscovers the basics of modern functional programming, vector algebra and classical physics, including Newton’s and Coulomb’s laws. Concepts are built compositionally from those learned earlier, yielding multi-layered symbolic representations that are interpretable and transferrable to new tasks, while still growing scalably and flexibly with experience. — Read More

#human

In the search for less energy-hungry artificial intelligence, some scientists are exploring living computers

Artificial intelligence systems, even those as sophisticated as ChatGPT, depend on the same silicon-based hardware that has been the bedrock of computing since the 1950s. But what if computers could be molded from living biological matter? Some researchers in academia and the commercial sector, wary of AI’s ballooning demands for data storage and energy, are focusing on a growing field known as biocomputing. This approach uses synthetic biology, such as miniature clusters of lab-grown cells called organoids, to create computer architecture. Biocomputing pioneers include Swiss company FinalSpark, which earlier this year debuted its “Neuroplatform”—a computer platform powered by human-brain organoids—that scientists can rent over the Internet for $500 a month. — Read More

The operation of the Neuroplatform currently relies on an architecture that can be classified as wetware: the mixing of hardware, software, and biology. The main innovation delivered by the Neuroplatform is through the use of four Multi-Electrode Arrays (MEAs) housing the living tissue – organoids, which are 3D cell masses of brain tissue.

Each MEA holds four organoids, interfaced by eight electrodes used for both stimulation and recording. Data goes to-and-fro via digital analog converters (Intan RHS 32 controller) with a 30kHz sampling frequency and a 16-bit resolution. These key architectural design features are supported by a microfluidic life support system for the MEAs, and monitoring cameras. Last but not least, a software stack allows researchers to input data variables, and then read and interpret processor output. — Read More

A cubic millimeter of brain tissue may not sound like much. But considering that that tiny square contains 57,000 cells, 230 millimeters of blood vessels, and 150 million synapses, all amounting to 1,400 terabytes of data, Harvard and Google researchers have just accomplished something stupendous.

Led by Jeff Lichtman, the Jeremy R. Knowles Professor of Molecular and Cellular Biology and newly appointed dean of science, the Harvard team helped create the largest 3D brain reconstruction to date, showing in vivid detail each cell and its web of connections in a piece of temporal cortex about half the size of a rice grain. — Read More

The Study

AI is a lever that becomes a lens

I remember when I first saw GPT-3 output writing: that line of letters hammered out one by one, rolling horizontally across the screen in its distinctive staccato. It struck both wonder and terror into my heart.

I felt ecstatic that computers could finally talk back to me. But I also felt a heavy sense of dread. I’m a writer—what would happen to me? 

We’ve all had this experience with AI over the last year and a half. It is an emotional rollercoaster. It feels like it threatens our conception of ourselves.  — Read More

It is estimated that 180,000 Americans live with quadriplegia, and each year, an additional ~18,000 suffer a paralyzing spinal cord injury. We live in a digital society where‬‭ much of our work, entertainment, and social lives rely heavily on our use of computers and‬‭ smart devices. People with quadriplegia often find that their needs to engage seamlessly with the digital world go unmet, leading to decreased independence, isolation, and financial challenges. Our goal is to provide a high-performance interface that will enhance the control of digital devices for people with quadriplegia, unlocking their personal and professional potential.

The first step toward this goal was achieved just over 100 days ago at Barrow Neurological Institute in Phoenix Arizona, where Noland Arbaugh, the first participant of the PRIME Study*, received his Neuralink implant (Link). As noted in our last blog post, the surgery went extremely well, and he was able to go home the following day.

The aim of the PRIME Study is to demonstrate that the Link is safe and useful in daily life. We will monitor its technical performance remotely and quantify any benefit it provides by timing the duration of independent use and assessing how it affects study participants’ quality of life. — Read More

In the dynamic field of artificial intelligence, the quest for Artificial General Intelligence (AGI) represents a pinnacle of innovation, promising to redefine the interplay between technology and human intellect. Jensen Huang, CEO of NVIDIA, a trailblazer in AI technology, recently brought this topic to the forefront of technological discourse. During a forum at Stanford University, Huang posited that AGI might be realized within the next five years, a projection that hinges critically on the definition of AGI itself.

According to Huang, if AGI is characterized by its ability to successfully pass a diverse range of human tests, then this milestone in AI development is not merely aspirational but could be nearing actualization. This statement from a leading figure in the AI industry not only sparks interest but also prompts a reassessment of our current understanding of artificial intelligence and its potential trajectory in the near future. — Read More

The first human being to receive a brain chip from Elon Musk’s Neuralink can apparently control a computer mouse just by thinking, according to Musk.

…”Progress is good, and the patient seems to have made a full recovery, with no ill effects that we are aware of,” Musk said. “Patient is able to move a mouse around the screen by just thinking.”

…Last month, Musk shared in a post on X that Neuralink had successfully performed the transplant surgery on a human for the first time on Jan. 28. — Read More