Lifeboat Foundation

Early PDP-11 models were not overly impressive. The first PDP-11 11/20 cost $20,000, but it shipped with only about 4KB of RAM. It used paper tape as storage and had an ASR-33 teletype printer console that printed 10 characters per second. But it also had an amazing orthogonal 16-bit architecture, eight registers, 65KB of address space, a 1.25 MHz cycle time, and a flexible UNIBUS hardware bus that would support future hardware peripherals. This was a winning combination for its creator, Digital Equipment Corporation.

The initial application for the PDP-11 included real-time hardware control, factory automation, and data processing. As the PDP-11 gained a reputation for flexibility, programmability, and affordability, it saw use in traffic light control systems, the Nike missile defense system, air traffic control, nuclear power plants, Navy pilot training systems, and telecommunications. It also pioneered the word processing and data processing that we now take for granted.

And the PDP-11’s influence is most strikingly evident in the device’s assembly programming.

Only a year ago, ChatGPT woke the world up to the power of foundation models. But this power is not about shiny, jaw-dropping demos. Foundation models will permeate every sector, every aspect of our lives, in much the same way that computing and the Internet transformed society in previous generations. Given the extent of this projected impact, we must ask not only what AI can do, but also how it is built. How is it governed? Who decides?

We don’t really know. This is because transparency in AI is on the decline. For much of the 2010s, openness was the default orientation: Researchers published papers, code, and datasets. In the last three years, transparency has waned. Very little is known publicly about the most advanced models (such as GPT-4, Gemini, and Claude): What data was used to train them? Who created this data and what were the labor practices? What values are these models aligned to? How are these models being used in practice? Without transparency, there is no accountability, and we have witnessed the problems that arise from the lack of transparency in previous generations of technologies such as social media.

To make assessments of transparency rigorous, the Center for Research on Foundation Models introduced the Foundation Model Transparency Index, which characterizes the transparency of foundation model developers. The good news is that many aspects of transparency (e.g., having proper documentation) are achievable and aligned with the incentives of companies. In 2024, maybe we can start to reverse the trend.

Fourier Intelligence has been manufacturing exoskeletons and rehabilitation devices since 2017. The Singapore-based company launched its first generation of humanoid robots this year, designated the GR-1.

The humanoid platform includes 40 degrees of freedom distributed throughout its body, which measures 1.65 m (5 ft., 5 in.) in height and weighs 55 kg (121.2 lb.). The joint module that is fitted at the hip of the robot is capable of producing a peak torque of 300 Nm, which allows it to walk at a speed of 5 kph (3.1 mph) and carry goods that weigh 50 kg (110.2 lb.).

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1. AGI could be achieved or we will get even closer. There will OpenAI releasing GPT5 and updates of Google LLM like improved Gemini.

Definition’s for AI AGI = artificial general intelligence = a machine that performs at the level of an average (median) human.

ASI = artificial superintelligence = a machine that performs at the level of an expert human in practically any field.

Body scan companies say they can diagnose cancer faster and that AI can make it cheaper. But there’s a gap between that dream and the medical community’s reality.

ChatGPT may do an impressive job at correctly answering complex questions, but a new study suggests it may be absurdly easy to convince the AI chatbot that it’s in the wrong.

A team at Ohio State University challenged large language models (LLMs) like ChatGPT to a variety of debate-like conversations in which a user pushed back when the chatbot presented a correct answer.

Through experimenting with a broad range of reasoning puzzles, including math, common sense, and logic, the study found that when presented with a challenge, the model was often unable to defend its correct beliefs and instead blindly believed invalid arguments made by the user.

AI influencers are striking six-figure deals with luxury brands trying to cut marketing costs. It’s making human influencers antsy, the FT reported.

Neurons in the brain communicate with each other at their synapses. It has long been understood that this communication occurs through biochemical processes. Here, we reveal that mechanical tension in neurons is essential for communication. Using in vitro rat hippocampal neurons, we find that 1) neurons become tout/tensed after forming synapses resulting in a contractile neural network, and 2) without this contractility, neurons fail to fire. To measure time evolution of network contractility in 3D (not 2D) extracellular matrix, we developed an ultrasensitive force sensor with 1 nN resolution. We employed Multi-Electrode Array and iGluSnFR, a glutamate sensor, to quantify neuronal firing at the network and at the single synapse scale, respectively. When neuron contractility is relaxed, both techniques show significantly reduced firing. Firing resumes when contractility is restored.

This year gave rise to an incredible mix of brain implants that can record, decode, and alter brain activity.

It sounds like déjà vu—brain-machine interfaces also lived rent free in my head in last year’s roundup, but for good reason. Neuroscientists are building increasingly sophisticated and flexible electronic chips that seamlessly integrate machine intelligence with our brains and spinal cords at record-breaking speed. What was previously science fiction—for example, helping paralyzed people regain their ability to walk, swim, and kayak—is now reality.

This year, brain implants further transformed people’s lives. The not-so-secret sauce? AI.