Last week was a relatively quiet one in the artificial intelligence (AI) universe. I was grateful \u2014 honestly, a brief respite from the incessant stream of news was more than welcome. <\/p>\n
As I rev up for all things AI in 2023, I wanted to take a quick look back at my favorite stories, large and small, that I covered in 2022 \u2014 starting with my first few weeks at VentureBeat back in April. <\/p>\n
In April 2022, emotions were running high around the evolution and use of emotion artificial intelligence (AI), which includes technologies such as voice-based emotion analysis and computer vision-based facial expression detection.\u00a0<\/p>\n
For example, Uniphore, a\u00a0conversational AI<\/a>\u00a0company enjoying unicorn status after announcing $400 million in new funding and a\u00a0$2.5 billion valuation<\/a>, introduced its Q for Sales solution back in March, which \u201cleverages computer vision, tonal analysis, automatic speech recognition and natural language processing to capture and make recommendations on the\u202ffull emotional spectrum of sales conversations to boost close rates and performance of sales teams.\u201d\u00a0<\/p>\n Intelligent Security Summit On-Demand<\/span><\/p>\n Learn the critical role of AI & ML in cybersecurity and industry specific case studies. Watch on-demand sessions today.<\/span><\/p>\n<\/div>\n <\/body><\/p>\n But computer scientist and famously fired,\u00a0former Google employee<\/a>, Timnit Gebru, who\u00a0founded<\/a>\u00a0an independent AI ethics research institute in December 2021, was\u00a0critical<\/a>\u00a0of Uniphore\u2019s claims on Twitter.\u00a0 \u201cThe trend of embedding pseudoscience into \u2018AI systems\u2019 is such a big one,\u201d she said.\u00a0\u00a0<\/p>\n This story dug into what this kind of pushback means for the enterprise? How can organizations calculate the risks and rewards of investing in\u00a0emotion AI<\/a>? <\/p>\n In early May 2022, Eric Horvitz, Microsoft\u2019s chief scientific officer,\u00a0testified<\/a>\u00a0before the U.S. Senate Armed Services Committee Subcommittee on Cybersecurity, he emphasized that organizations are certain to face new challenges as cybersecurity attacks increase in sophistication \u2014 including through the use of AI.\u00a0<\/p>\n While AI is improving the ability to detect cybersecurity threats, he explained, threat actors are also upping the ante. <\/p>\n \u201cWhile there is scarce information to date on the active use of\u00a0AI in cyberattacks<\/a>, it is widely accepted that AI technologies can be used to scale cyberattacks via various forms of probing and automation\u2026referred to as offensive AI,\u201d he said.\u00a0<\/p>\n However, it\u2019s not just the military that needs to stay ahead of threat actors using AI to scale up their attacks and evade detection. As enterprise companies battle a growing number of major security breaches, they need to\u00a0prepare<\/a>\u00a0for increasingly sophisticated\u00a0AI-driven cybercrimes<\/a>, experts say.\u00a0<\/p>\n In June, thousands of artificial intelligence experts and machine learning researchers had their weekends upended when Google engineer Blake Lemoine told the\u00a0Washington Post<\/a>\u00a0that he believed\u00a0LaMDA<\/a>, Google\u2019s conversational AI for generating chatbots based on large language models (LLM), was sentient.\u00a0<\/p>\n The Washington Post article pointed out that \u201cMost academics and\u00a0AI practitioners<\/a>\u00a0\u2026 say the words and images generated by artificial intelligence systems such as LaMDA produce responses based on what humans have already posted on Wikipedia, Reddit, message boards, and every other corner of the internet. And that doesn\u2019t signify that the model understands meaning.\u201d\u00a0<\/p>\n That\u2019s when AI and ML Twitter put aside any weekend plans and went at it. AI leaders, researchers and practitioners shared long, thoughtful threads, including AI ethicist\u00a0Margaret Mitchell<\/a>\u00a0(who was famously\u00a0fired from Google<\/a>, along with Timnit Gebru, for criticizing large language models) and machine learning pioneer\u00a0Thomas G. Dietterich<\/a>.\u00a0<\/strong><\/p>\n In June, I spoke to Julian Sanchez, director of emerging technology at John Deere, about John Deere\u2019s status as a leader in\u00a0AI innovation<\/a>\u00a0did not come out of nowhere. In fact, the agricultural machinery company has been planting and growing data seeds for over two decades. Over the past 10-15 years, John Deere has invested heavily on developing a data platform and machine connectivity, as well as GPS-based guidance. <\/p>\n \u201cThose three pieces are important to the AI conversation, because implementing real AI solutions is in large part a data game,\u201d he said. \u201cHow do you collect the data? How do you transfer the data? How do you train the data? How do you deploy the data?\u201d\u00a0<\/p>\n These days, the company has been enjoying the fruit of its AI labors, with more harvests to come.\u00a0<\/p>\n In July, it was becoming clear that OpenAI\u2019s DALL-E 2 was no AI flash in the pan. <\/p>\n When the company expanded beta access to\u00a0 its powerful\u00a0image-generating<\/a>\u00a0AI solution to over one million users via a paid subscription model, it also offered those users full usage rights to commercialize the images they create with\u00a0DALL-E<\/a>, including the right to reprint, sell and merchandise.<\/p>\n The announcement sent the tech world buzzing, but a variety of questions, one leading to the next, seem to linger beneath the surface.\u00a0For one thing, what does the commercial use of DALL-E\u2019s AI-powered imagery mean for creative industries and workers \u2013 from graphic designers and video creators to PR firms, advertising agencies and marketing teams? Should we imagine the wholesale disappearance of, say, the illustrator?\u00a0Since then, the debate around the legal ramifications of art and AI has only gotten louder. <\/p>\n In summer 2022, the\u00a0MLops<\/a>\u00a0market was still hot when it comes to investors. But for enterprise end users, I addressed the fact that it also seemed like a hot mess.\u00a0<\/p>\n The MLops ecosystem is highly fragmented, with hundreds of vendors competing in a global market that\u00a0was estimated<\/a>\u00a0to be $612 million in 2021 and is projected to reach over $6 billion by 2028. But according to Chirag Dekate, a VP and\u00a0analyst<\/a>\u00a0at Gartner Research, that crowded landscape is leading to confusion among enterprises about how to get started and what MLops vendors to use.\u00a0<\/p>\n \u201cWe are seeing end users getting more mature in the kind of operational AI ecosystems they\u2019re building \u2013 leveraging Dataops and MLops,\u201d said Dekate. That is, enterprises take their data source requirements, their cloud or infrastructure center of gravity, whether it\u2019s on-premise, in the cloud or hybrid, and then integrate the right set of tools.\u00a0But it can be hard to pin down the right toolset. <\/p>\n In August, I enjoyed getting a look at a possible AI hardware future \u2014 one where analog AI hardware \u2013 rather than digital \u2013 tap fast, low-energy processing to solve\u00a0machine learning<\/a>\u2019s rising costs and carbon footprint. <\/p>\n That\u2019s what Logan Wright and Tatsuhiro Onodera, research scientists at NTT Research and Cornell University,\u00a0envision: a future<\/a>\u00a0where machine learning (ML) will be performed with novel physical hardware, such as those based on photonics or nanomechanics. These unconventional devices, they say, could be applied in both edge and server settings.\u00a0<\/p>\n Deep\u00a0neural networks<\/a>, which are at the heart of today\u2019s AI efforts, hinge on the heavy use of digital processors like GPUs. But for years, there have been concerns about the monetary and environmental cost of machine learning, which increasingly limits the scalability of deep learning models.\u00a0<\/p>\n The New York Times reached out to me in late August to talk about one of the company\u2019s biggest challenges: striking a balance between meeting its latest target of 15 million digital subscribers by 2027 while also getting more people to read articles online.\u00a0<\/p>\n These days, the multimedia giant is digging into that complex cause-and-effect relationship using a causal machine learning model, called the Dynamic Meter, which is all about making its paywall smarter. According to Chris Wiggins, chief data scientist at the\u00a0New York Times<\/em>, for the past three or four years the company has worked to understand their user journey and the workings of the paywall.<\/p>\n Back in 2011, when the Times began focusing on digital subscriptions, \u201cmetered\u201d access was designed so that non-subscribers could read the same fixed number of articles every month before hitting a paywall requiring a subscription. That allowed the company to gain subscribers while also allowing readers to explore a range of offerings before committing to a subscription.\u00a0<\/p>\n I enjoy covering anniversaries \u2014 and exploring what has changed and evolved over time. So when I realized that autumn 2022 was the 10 year anniversary of groundbreaking\u00a02012 research<\/a>\u00a0on the ImageNet database, I immediately reached out to key AI pioneers and experts about their thoughts looking back on the deep learning \u2018revolution\u2019 as well as what this research means today for the future of AI. <\/p>\n Artificial intelligence (AI) pioneer Geoffrey Hinton, one of the trailblazers of the deep learning \u201crevolution\u201d that began a decade ago, says that the rapid progress in AI will continue to accelerate. Other AI pathbreakers, including\u00a0Yann LeCun<\/a>, head of AI and chief scientist at Meta and Stanford University professor Fei-Fei Li, agree with Hinton that the results from the groundbreaking\u00a02012 research<\/a>\u00a0on the ImageNet database \u2014 which was built on previous work to unlock significant advancements in computer vision specifically and deep learning overall \u2014 pushed deep learning into the mainstream and have sparked a massive momentum that will be hard to stop.\u00a0<\/p>\n But Gary Marcus, professor emeritus at NYU and the founder and CEO of Robust.AI, wrote this past March about deep learning \u201chitting a wall<\/a>\u201d and says that while there has certainly been progress, \u201cwe are fairly stuck on common sense knowledge and reasoning about the physical world.\u201d\u00a0<\/p>\n And Emily Bender, professor of computational linguistics at the University of Washington and a regular critic of what she calls the \u201cdeep learning bubble<\/a>,\u201d said she doesn\u2019t think that today\u2019s natural language processing (NLP) and computer vision models add up to \u201csubstantial steps\u201d toward \u201cwhat other people mean by AI and AGI.\u201d\u00a0<\/p>\n In October, research lab DeepMind made headlines when it unveiled AlphaTensor, the \u201cfirst artificial intelligence system for discovering novel, efficient and provably correct algorithms.\u201d The Google-owned lab said the research \u201csheds light\u201d on a 50-year-old open question in mathematics about finding the fastest way to multiply two matrices.<\/p>\n Ever since the Strassen algorithm was published in 1969, computer science has been on a quest to surpass its speed of multiplying two matrices. While matrix multiplication is one of algebra\u2019s simplest operations, taught in high school math, it is also one of the most fundamental computational tasks and, as it turns out, one of the core mathematical operations in today\u2019s neural networks.\u00a0<\/p>\n This research delves into how AI could be used to improve computer science itself, said Pushmeet Kohli, head of AI for science at DeepMind, at a press briefing.\u00a0\u201cIf we\u2019re able to use AI to find new algorithms for fundamental computational tasks, this has enormous potential because we might be able to go beyond the algorithms that are currently used, which could lead to improved efficiency,\u201d he said.\u00a0<\/p>\n All year I was curious about the use of authorized deepfakes in the enterprise \u2014\u00a0that is, not the well-publicized negative side of synthetic media, in which a person in an existing image or video is replaced with someone else\u2019s likeness. <\/p>\n But there is another side to the deepfake debate, say several vendors that specialize in synthetic media technology. What about authorized deepfakes used for business video production?\u00a0<\/p>\n Most use cases for deepfake videos, they claim, are fully authorized. They may be in enterprise business settings \u2014 for employee training, education and ecommerce, for example. Or they may be created by users such as celebrities and company leaders who want to take advantage of synthetic media to \u201coutsource\u201d to a virtual twin.<\/p>\n Those working in AI and machine learning may well have thought they would be protected from a wave of big tech layoffs. Even after Meta layoffs in early November 2022, which cut 11,000 employees, CEO Mark Zuckerberg\u00a0publicly shared<\/a>\u00a0a message to Meta employees that signaled, to some, that those working in\u00a0artificial intelligence (AI)<\/a>\u00a0and machine learning (ML) might be spared the brunt of the cuts.<\/p>\n However, a Meta research scientist who was laid off\u00a0tweeted<\/a>\u00a0that he and the entire research organization called \u201cProbability,\u201d which focused on applying machine learning across the infrastructure stack, was cut.<\/p>\n The team had 50 members, not including managers, the research scientist, Thomas Ahle, said,\u00a0tweeting<\/a>: \u201c19 people doing Bayesian Modeling, 9 people doing Ranking and Recommendations, 5 people doing ML Efficiency, 17 people doing AI for Chip Design and Compilers. Plus managers and such.\u201d<\/p>\n On November 30, as GPT-4 rumors flew around NeurIPS 2022 in New Orleans (including whispers that details about\u00a0GPT-4<\/a>\u00a0will be revealed there), OpenAI managed to make plenty of news.\u00a0<\/p>\n The company announced a new model in the GPT-3 family of AI-powered large language models<\/a>, text-davinci-003, part of what it calls the \u201cGPT-3.5 series,\u201d that reportedly improves on its predecessors by handling more complex instructions and producing higher-quality, longer-form content.\u00a0<\/p>\n Since then, the hype around ChatGPT has grown exponentially<\/a> \u2014 but so has the debate around the hidden dangers<\/a> of these tools, which even CEO Sam Altman has weighed in on<\/a>. <\/p>\n VentureBeat’s mission<\/strong> is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.<\/a><\/p>\n<\/p><\/div>\nEvent<\/h3>\n
\n Watch Here <\/a>\n <\/p>\n<\/div>\n12. Cripping AI cyberattacks are inevitable: 4 ways companies can prepare<\/span><\/a><\/h2>\n
11. \u2018Sentient\u2019 artificial intelligence: Have we reached peak AI hype? <\/a><\/h2>\n
10. How John Deere grew data seeds into an AI powerhouse<\/a><\/h2>\n
9. Will OpenAI kill creative careers?<\/a><\/h2>\n
8. MLOps: Making sense of a hot mess<\/a><\/h2>\n
7. How analog hardware may one day reduce costs and carbon emissions<\/a><\/h2>\n
6. How machine learning helps the New York Times power its paywall<\/a><\/h2>\n
5. 10 years later, deep learning \u2018revolution\u2019 rages on<\/a><\/h2>\n
4. DeepMind unveils first AI to discover faster matrix multiplication algorithms<\/a><\/h2>\n
3. Why authorized deepfakes are becoming big for business<\/a><\/h2>\n
1. OpenAI debuts ChatGPT and GPT-3.5 series as GPT-4 rumors fly<\/a><\/h2>\n