Powerful tech companies keep LLMs like GPT-4 shrouded in secrecy. But a new wave of open-source LLMs is giving the power of chatbots to the people.

Examining the view that mind and body are separate substances.

Note at 7:08 A reductio ad absurdum argument (one which attributes a machine with thought purely for the sake of argument, to demonstrate that genuinely absurd / contradictory consequences follow) would be valid. We can see immediately that Plantinga’s thought experiment doesn’t achieve this: failure to discern how a thinking machine is thinking indicates only lack of comprehension, not a genuine absurdity / contradiction.

But his use of Leibniz’ scenario isn’t valid. Leibniz doesn’t just propose a thinking machine, but one we can enter and inspect. If physical thinking things are impossible — as Plantinga claims — then whatever machine we conjure up in our imagination to enter and inspect, it can’t be a genuine physical thinking thing, just as it would be impossible to inspect a machine that prints square circles. (Besides, if there’s truly nothing we could be faced with inside the machine that would signal thought, it makes no sense to ask us to inspect it, since no inspection could help us discern thinking machines from non-thinking ones anyway.) It is this sense in which Plantinga cannot use thinking machines to show machines can’t think. His argument is incoherent. It is certainly not a valid reductio ad absurdum.

Selected Resources:

Humanoid robot Asimo demonstration:

Descartes, R — Discourse on the Method (1637)

This post is also available in: עברית (Hebrew)

What is the invisible cost of AI technology? Experts express their worries about privacy breaches.

AI-powered technology is constantly growing, and with it grows the amount of personal information collected and processed. But how do AI-powered apps impact user privacy?

PhotoGuard, created by researchers at MIT, alters photos in ways that are imperceptible to us but stops AI systems from tinkering with them.

Remember that selfie you posted last week? There’s currently nothing stopping someone taking it and editing it using powerful generative AI systems. Even worse, thanks to the sophistication of these systems, it might be impossible to prove that the resulting image is fake.

The good news is that a new tool, created by researchers at MIT, could prevent this.

Since the first High-Bandwidth Memory (HBM) stacks were introduced in 2013, these stacked memory chiplets have carved out a new, high-performance niche for DRAM in the memory hierarchy. The first products to incorporate HBM started to appear in 2015. You will now find HBM stacks in high-end CPUs, GPUs, and FPGAs, where performance matters more than cost to the end customer. Although HBM is not as fast as SRAM, it is faster than bulk DDR memory, and it’s getting even faster. Micron has just announced what the company is calling a “second-generation” (Gen 2) HBM3 DRAM that adheres to the semiconductor industry’s “bigger, better, faster” mantra. Because it integrates higher-density DRAM die, Micron’s HBM3 Gen 2 can be 50 percent bigger (higher capacity) than existing HBM3 DRAM available from other memory vendors, with 2.5x better performance/power ratings and 50 percent faster speed (1.2Tbytes/second). That’s the DRAM industry’s equivalent of a trifecta.

HBM is akin to a chiplet skyscraper. Each HBM stack consists of a logic die, which contains a system interface and DRAM control, with multiple DRAM die stacked on top of the logic die. The entire stack is designed to be incorporated into an IC package along with larger semiconductor die and other, smaller chiplets. You’ll find earlier HBM generations incorporated into server CPUs from AMD and Intel, FPGAs from AMD and Intel, and GPUs from AMD, Intel, and Nvidia. The most recent announcement of a GPU containing HBM is AMD’s MI300X GPU, announced by the company’s CEO Lisa Su in June. (See “AMD Hops On The Generative AI Bandwagon With Instinct MI300X.”) Intel previously announced HBM use in the company’s Data Center GPU Max Series, the data center GPU formerly known as Ponte Vecchio. Nvidia’s H100 Hopper GPU also incorporates HBM memory stacks.

Micron has now entered the HBM3 race with what the company is calling a “second-generation” HBM3 design, because Micron’s version of an HBM3 stack is arriving about a year after competitive products, but is based on a faster, denser, more advanced semiconductor process node. The DRAM die in this Micron HBM3 stack is being fabricated with the company’s 1β process technology, which the company announced last year. During that announcement, the company claimed that the 1β process increased DRAM bit density by 35 percent and decreased power consumption by 15 percent, when compared to the company’s 1α node. At the time, Micron announced plans to use its 1β process node for manufacturing DDR5 and LPDDR5 DRAM. The company has now stated that they will use the same process node for manufacturing DRAM die for its HBM3 stacks.

Alphabet’s Q2 2023 earnings call highlighted the growing adoption of generative AI across the company’s cloud and product offerings.

CEO Sundar Pichai emphasized how over 70% of generative AI startups rely on Google’s cloud infrastructure and AI capabilities. This shows the traction for next-gen technology among emerging companies looking to build new services powered by Google Bard and other models.

“Our AI-optimized infrastructure is a leading platform for training and serving generative AI models. More than 70% of gen AI unicorns are Google Cloud customers, including Cohere, Jasper, Typeface, and many more,” he said.