2021 was the calendar year in which the miracles of artificial intelligence stopped currently being a story. Which is not to say that IEEE Spectrum did not protect AI—we covered the heck out of it. But we all know that deep studying can do wondrous factors and that it really is getting swiftly incorporated into lots of industries that’s yesterday’s information. A lot of of this year’s prime content articles grappled with the limits of deep mastering (present-day dominant strand of AI) and spotlighted scientists seeking new paths.
Below are the 10 most well-liked AI content that Spectrum published in 2021, rated by the sum of time men and women spent reading them. A number of arrived from Spectrum‘s Oct 2021 specific problem on AI, The Wonderful AI Reckoning.
1. Deep Learning’s Diminishing Returns: MIT’s Neil Thompson and a number of of his collaborators captured the prime place with a considerate attribute write-up about the computational and energy expenditures of schooling deep understanding methods. They analyzed the advancements of impression classifiers and identified that “to halve the mistake fee, you can count on to want extra than 500 times the computational methods.” They wrote: “Faced with skyrocketing charges, scientists will possibly have to occur up with much more efficient methods to fix these difficulties, or they will abandon doing the job on these difficulties and development will languish.” Their write-up just isn’t a complete downer, although. They finished with some promising strategies for the way ahead.
2. 15 Graphs You Have to have to See to Understand AI in 2021: Each 12 months, The AI Index drops a enormous load of data into the conversation about AI. In 2021, the Index’s diligent curators presented a world wide point of view on academia and industry, using care to spotlight difficulties with diversity in the AI workforce and moral troubles of AI purposes. I, your humble AI editor, then curated that substantial quantity of curated information, boiling 222 webpages of report down into 15 graphs masking positions, investments, and a lot more. You happen to be welcome.
3. How DeepMind Is Reinventing the Robotic: DeepMind, the London-primarily based Alphabet subsidiary, has been driving some of the most extraordinary feats of AI in latest yrs, together with breakthrough do the job on protein folding and the AlphaGo program that conquer a grandmaster at the historical game of Go. So when DeepMind’s head of robotics Raia Hadsell says she’s tackling the long-standing AI issue of catastrophic forgetting in an attempt to create multi-proficient and adaptable robots, folks shell out focus.
4. The Turbulent Previous and Unsure Long term of Artificial Intelligence: This element post served as the introduction to Spectrum‘s unique report on AI, telling the tale of the area from 1956 to current working day while also cueing up the other content articles in the exclusive issue. If you want to realize how we acquired below, this is the short article for you. It pays special consideration to past feuds in between the symbolists who bet on pro systems and the connectionists who invented neural networks, and appears to be like forward to the options of hybrid neuro-symbolic programs.
5. Andrew Ng X-Rays the AI Hype: This small article relayed an anecdote from a Zoom Q&A session with AI pioneer Andrew Ng, who was deeply concerned in early AI efforts at Google Brain and Baidu and now leads a enterprise termed Landing AI. Ng spoke about an AI procedure produced at Stanford University that could spot pneumonia in chest x-rays, even outperforming radiologists. But there was a twist to the tale.
6. OpenAI’s GPT-3 Speaks! (Kindly Disregard Harmful Language): When the San Francisco-dependent AI lab OpenAI unveiled the language-creating procedure GPT-3 in 2020, the first reaction of the AI community was awe. GPT-3 could deliver fluid and coherent textual content on any subject and in any model when presented the smallest of prompts. But it has a dim side. Properly trained on textual content from the world-wide-web, it realized the human biases that are all as well prevalent in certain portions of the online environment, and can as a result has an awful habit of unexpectedly spewing out toxic language. Your humble AI editor (once more, which is me) got pretty fascinated in the businesses that are speeding to combine GPT-3 into their products, hoping to use it for such purposes as shopper guidance, on-line tutoring, psychological overall health counseling, and more. I needed to know: If you’re heading to make use of an AI troll, how do you prevent it from insulting and alienating your buyers?
7. Speedy, Effective Neural Networks Copy Dragonfly Brains: What do dragonfly brains have to do with missile protection? Check with Frances Chance of Sandia Nationwide Laboratories, who research how dragonflies proficiently use their about 1 million neurons to hunt and capture aerial prey with amazing precision. Her work is an appealing distinction to investigation labs making neural networks of at any time-raising size and complexity (recall #1 on this listing). She writes: “By harnessing the pace, simplicity, and performance of the dragonfly anxious technique, we aim to design and style computer systems that complete these capabilities quicker and at a portion of the energy that regular systems eat.”
8. Deep Discovering Isn’t really Deep More than enough Unless of course It Copies From the Brain: In a former life, Jeff Hawkins invented the PalmPilot and ushered in the smartphone period. These days, at the device intelligence enterprise Numenta, he’s investigating the foundation of intelligence in the human brain and hoping to usher in a new era of artificial standard intelligence. This Q&A with Hawkins covers some of his most controversial tips, which includes his conviction that superintelligent AI won’t pose an existential menace to humanity and his contention that consciousness just isn’t truly such a tricky challenge.
9. The Algorithms That Make Instacart Roll: It really is usually entertaining for Spectrum readers to get an insider’s search at the tech companies that allow our lives. Engineers Sharath Rao and Lily Zhang of Instacart, the grocery procuring and supply enterprise, describe that the firm’s AI infrastructure has to forecast the availability of “the solutions in nearly 40,000 grocery stores—billions of various data factors,” while also suggesting replacements, predicting how quite a few consumers will be obtainable to get the job done, and effectively grouping orders and shipping routes.
10. 7 Revealing Methods AIs Fail: Every person loves a listing, correct? Just after all, listed here we are together at product #10 on this listing. Spectrum contributor Charles Choi pulled collectively this entertaining record of failures and spelled out what they reveal about the weaknesses of present day AI. The cartoons of robots receiving themselves into hassle are a awesome bonus.
So there you have it. Retain looking at IEEE Spectrum to see what comes about following. Will 2022 be the 12 months in which scientists determine out options to some of the knotty issues we lined in the calendar year that is now ending? Will they solve algorithmic bias, set an finish to catastrophic forgetting, and come across methods to boost general performance with out busting the planet’s electrical power funds? Almost certainly not all at after… but let us locate out together.
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