The work, from AI startup Altera, is part of a broader field that wants to use simulated agents to model how human groups would react to new economic policies or other interventions. But for Altera's founder, Robert Yang, who quit his position as an assistant professor in computational neuroscience at MIT to start the company, this demo is just the beginning. He sees it as an early step towards large-scale "AI civilizations" that can coexist and work alongside us in digital spaces. "The true power of AI will be unlocked when we have actually truly autonomous agents that can collaborate at scale," says Yang. Yang was inspired by Stanford University researcher Joon Sung Park who, in 2023, found that surprisingly humanlike behaviors arose when a group of 25 autonomous AI agents was let loose to interact in a basic digital world. "Once his paper was out, we started to work on it the next week," says Yang. "I quit MIT six months after that."

This article is a summary of a three-hour discussion at Stanford University in September 2019 among the authors. It has been written with combined experiences at and with organizations such as Zilog, Altera, Xilinx, Achronix, Intel, IBM, Stanford, MIT, Berkeley, University of Wisconsin, the Technion, Fairchild, Bell Labs, Bigstream, Google, DIGITAL (DEC), SUN, Nokia, SRI, Hitachi, Silicom, Maxeler Technologies, VMware, Xerox PARC, Cisco, and many others. These organizations are not responsible for the content, but may have inspired the authors in some ways, to arrive at the colorful ride through FPGA space described here. Field-programmable gate arrays (FPGAs) have been hitting a nerve in the ASIC community since their inception. In the mid-1980s, Ross Freeman and his colleagues bought the technology from Zilog and started Xilinx, targeting the ASIC emulation and education markets.

Accelerating resource-hungry AI applications demands chip performance beyond what mere CPU or GPU can deliver, prompting researchers to turn to sophisticated Application-specific Integrated Circuits (ASIC) and Field Programmable Gate Arrays (FPGA). Chip giant NVIDIA Founder and CEO Jensen Huang created a bit of a stir at yesterday's GPU Technology Conference in Santa Clara, USA, when he appeared to dis one of these chips' appropriateness for autonomous vehicle system development: "FPGA is not the right answer," he said. "FPGA is really for prototyping. If you want the [self-driving] car to be perfect, I would build myself an ASIC because self-driving cars deserve it," says Huang. FPGAs are logic chips best known for their programmability, which gives engineers the flexibility to configure an FPGA for example as a micro-control unit today, and use the same FPGA as an audio codec tomorrow.

Intel's $1 billion investment in the AI ecosystem is one of the well-publicized talking points at the processor company. The Intel empire boasts a breadth of AI technologies it has amassed by acquisition and Intel Capital investments in AI startups. The acquired companies seemingly useful to Intel's AI ambitions thus far include Altera (2015), Saffron (2015), Nervana (2016), Movidius (2016) and Mobileye (2017). Intel Capital has also fattened its AI portfolio with startups Mighty AI, Data Robot, Lumiata, CognitiveScale, Aeye Inc., Element AI and others. Unclear is how Intel is going to stitch all this together.

Chip giant Intel has been talking about CPU-FPGA compute complexes for so long that it is hard to remember sometimes that its hybrid Xeon-Arria compute unit, which puts a Xeon server chip and a midrange FPGA into a single Xeon processor socket, is not shipping as a volume product. But Intel is working to get it into the field and has given The Next Platform an update on the current plan. The hybrid CPU-FPGA devices, which are akin to AMD's Accelerated Computing Units, or APUs, in that they put compute and, in this case, GPU acceleration into a single processor package, are expected to see widespread adoption, particularly among hyperscalers and cloud builders who want to offload certain kinds of work from the CPU to an accelerator. While Intel has GPUs of its own and it puts them in a CPU package or on the CPU die for certain parts of the market – low-end workstations and low-end servers based on the Xeon E3 chip that are used to accelerate media processing and such – Intel is not enthusiastic about offloading work from its Xeon processors to other devices. It created the "Knights" family of parallel X86 processors first as an offload engine and then as a full processor in its own right with the "Knights Landing" Xeon Phi 7200 series that saw initial shipments in late 2015 and formally launched in the summer of 2016.

As we have been writing for a while now, artificial intelligence will transform pretty much everything we do in our lives in the next five years. AI is actually in use today helping us to match faces, identify photos, videos, the spoken word, doing our taxes, improving collaboration, and even help in healthcare diagnosis, and soon, will help drive our cars and trucks for us. While AI has been around for a while, the big breakthrough was machine learning using deep neural networks that actually got smarter with more information you threw at it. GPUs, with NVIDIA being the biggest recent beneficiary, have become the most recent standard for cutting edge deep neural network training, and inference today is spread across CPUs, GPUs, FPGAs, ASICs and even DSPs. AI is a quick moving target and I think it's unwise to think the engines today will be static in the future.

As we have been writing for a while now, artificial intelligence will transform pretty much everything we do in our lives in the next five years. AI is actually in use today helping us to match faces, identify photos, videos, the spoken word, doing our taxes, improving collaboration, and even help in healthcare diagnosis, and soon, will help drive our cars and trucks for us. While AI has been around for a while, the big breakthrough was machine learning using deep neural networks that actually got smarter with more information you threw at it. GPUs, with NVIDIA being the biggest recent beneficiary, have become the most recent standard for cutting edge deep neural network training, and inference today is spread across CPUs, GPUs, FPGAs, ASICs and even DSPs. AI is a quick moving target and I think it's unwise to think the engines today will be static in the future.

This shift was underlined on Monday when Intel said it would pay $15.3 billion to acquire Mobileye, an Israeli company that makes chips and cameras for cars and trucks, including the self-driving variety. The purchase will be Intel's second largest ever, following its $16.7 billion billion acquisition of chip-maker Altera in 2015. The Altera buy was also driven, in part, by the recent rise of machine learning, where machine learn can discrete tasks on their own. These are enormous acquisitions in many respects. After acquiring Mobileye, Intel will move its autonomous driving team to the Mobileye's headquarters, not vice versa.

This week, Microsoft researcher Doug Burger received more than his usual share of email. On Monday, Intel told the world it was spending $16.7 billion in cash to acquire a company called Altera. And perhaps more than anyone, Burger understands why this deal makes sense for the world's largest chip maker. At Microsoft, he cooked up a new way of powering the company's Bing search engine using the low-power programmable chips sold by Altera, pairing them with traditional microprocessors from Intel. Asked how he views the Intel acquisition, Burger is understandably coy.

Today: Google strikes deal with Johnson & Johnson to make robots that can assist surgeons, launching a new challenge to Sunnyvale's Intuitive Surgical. Also: Intel, Altera soar after reports of merger. Google's newest push into health care technology involves surgical robots, a partnership with a pharmaceutical giant and competition with one of Silicon Valley's largest medical-device companies. Google and a Johnson & Johnson unit announced Friday that they will be working together to make robots that can assist in surgeries, a strategic collaboration with no price tag announced. The aim of the project appears to be similar to the da Vinci robots manufactured and sold by Sunnyvale's Intuitive Surgical, the third largest public Silicon Valley company in the biotech/health care sector.