Tag: ai

Mythic

Mythic can do an 8-bit multiply and add in a single transistor

2020-10-17: AI Analog Compute

Mythic is the first and only company that have been able to implement a deep learning model like ResNet 50 in a non-digital architecture: > 50 layers, 1000 fps, 3W total, 9->2ms latency, 8 TOPS/W in 40nm silicon. 10x cost advantage over digital chips.

2023-04-11: Commercialization takes a long time

Mythic’s analog chip uses less power by storing neural weights not in SRAM but in flash memory, which doesn’t consume power to retain its state. And the flash memory is embedded in a processing chip, a configuration Mythic calls “compute-in-memory.” Instead of consuming a lot of power moving millions of bytes back and forth between memory and a CPU (as a digital computer does), some processing is done locally. Mythic’s success on that front has been variable: The company ran out of cash and raised $13 million in new funding and appointed a new CEO.
I asked him whether the state of analog computing today could be compared to that of quantum computing 25 years ago. Could it follow a similar path of development, from fringe consideration to common (and well-funded) acceptance?

It would take a fraction of the time. “We have our experimental results. It has proven itself. If there is a group that wants to make it user-friendly, within 1 year we could have it.” And at this point he is willing to provide analog computer boards to interested researchers, who can use them with Achour’s compiler.

Voice uncanny valley

there are a set of reasons why people want voice to be the new thing. One more that I didn’t mention is that, now that Mobile is no longer the hyper-growth sector, the tech industry is casting around looking for the Next Big Thing. I suspect that voice is certainly a big thing, but we’ll have to wait a bit longer for the next platform shift.

Human capability

A couple of people talked about how the quest for “optimal Go” wasn’t just about one game, but about grading human communities. Here we have this group of brilliant people who have been competing against each other for centuries, gradually refining their techniques. Did they come pretty close to doing as well as merely human minds could manage? Or did non-intellectual factors – politics, conformity, getting trapped at local maxima – cause them to ignore big parts of possibility-space? Right now it’s very preliminarily looking like the latter, which would be a really interesting result – especially if it gets replicated once AIs take over other human fields.

Outrageously Large Neural Networks

We finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to 1000s of feed-forward sub-networks. We present model architectures in which a MoE with up to 137B parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost.

Superhuman Visual Problemsolving

A Northwestern University team has developed a new visual problem-solving computational model that surpasses 75% of adults on a standard intelligence test. It is built on CogSketch. It can solve visual problems and understand sketches to give immediate, interactive feedback. CogSketch also incorporates a computational model of analogy, based on Northwestern psychology professor Dedre Gentner’s structure-mapping engine.

5 technologies for 2022

In 5 years, new imaging devices using hyperimaging technology and AI will help us see broadly beyond the domain of visible light by combining multiple bands of the electromagnetic spectrum to reveal valuable insights or potential dangers that would otherwise be unknown or hidden from view. Most importantly, these devices will be portable, affordable and accessible, so superhero vision can be part of our everyday experiences.

Today, more than 99.9% of the electromagnetic spectrum cannot be observed by the naked eye. Over the last 100 years, scientists have built instruments that can emit and sense energy at different wavelengths. Today, we rely on some of these to take medical images of our body, see the cavity inside our tooth, check our bags at the airport, or land a plane in fog. However, these instruments are incredibly specialized and expensive and only see across specific portions of the electromagnetic spectrum.

Undercover AlphaGo

The account is simply called “Master”, and since the start of the new year it has made a habit out of trashing some of the world’s best Go professionals. It’s already beaten Ke Jie twice, who is currently the highest ranked Go player in the world. AlphaGo, incidentally, is #2. Ke Jie was “a bit shocked … just repeating ‘it’s too strong'”. By January 3, the number of probably-but-we-can’t-officially-say AI sanctioned beatings had risen to 41-zip

2017-01-04: It’s alphago