Keeping up with an industry as fast-moving as\u00a0AI<\/a>\u00a0is a tall order. So until an AI can do it for you, here\u2019s a handy roundup of recent stories in the world of machine learning, along with notable research and experiments we didn\u2019t cover on their own.<\/p>\n This week in AI, OpenAI held the first of what will presumably be many developer conferences to come. During the keynote, the company showed off<\/a> a slew<\/a> of new products<\/a>, including an improved version of GPT-4<\/a>, new text-to-speech models and an API for the image-generating DALL-E 3<\/a>, among others.<\/p>\n But without a doubt the most significant announcement was GPTs<\/a>.<\/p>\n OpenAI\u2019s GPTs provide a way for developers to build their own conversational AI systems powered by OpenAI\u2019s models and publish them on an OpenAI-hosted marketplace called the GPT Store. Soon, developers will even be able to monetize GPTs based on how many people use them, OpenAI CEO Sam Altman said onstage at the conference.<\/p>\n \u201cWe believe that if you give people better tools, they will do amazing things,\u201d Altman said. \u201cYou can build a GPT \u2026 and then you can publish it for others to use, and because they combine instructions, expanded knowledge and actions, they can be more helpful to you.\u201d<\/p>\n OpenAI\u2019s shift from AI model provider to platform has been an interesting one, to be sure \u2014 but not exactly unanticipated. The startup telegraphed its ambitions in March with the launch of plugins<\/a> for ChatGPT, its AI-powered chatbot, which brought third parties into OpenAI\u2019s model ecosystem for the first time.<\/p>\n But what caught this writer off guard was the breadth and depth of OpenAI\u2019s GPT building \u2014 and commercializing \u2014 tools out of the gate.<\/p>\n My colleague Devin Coldewey, who attended OpenAI\u2019s conference in person, tells me the GPT experience was \u201ca little glitchy\u201d in demos \u2014 but works as advertised, more or less. GPTs don\u2019t require coding experience and can be as simple or complex as a developer wishes. For example, a GPT can be trained on a cookbook collection so that it can ask answer questions about ingredients for a specific recipe. Or a GPT could ingest a company\u2019s proprietary codebases so that developers can check their style or generate code in line with best practices.<\/p>\n GPTs effectively democratize generative AI app creation \u2014 at least for apps that use OpenAI\u2019s family of models. And if I were OpenAI\u2019s rivals \u2014 at least the rivals without backing from Big Tech \u2014 I\u2019d be racing to the figurative warroom to muster a response.<\/p>\n GPT could kill consultancies whose business models revolve around building what are essentially GPTs for customers. And for customers with developer talent, it could make model providers that don\u2019t<\/em> offer any form of app-building tools less attractive given the complexities of having to weave a provider\u2019s APIs into existing apps and services.<\/p>\n Is that a good thing? I\u2019d argue not necessarily \u2014 and I\u2019m worried about the potential for monopoly. But OpenAI has first-mover advantage, and it\u2019s leveraging it \u2014 for better or worse.<\/p>\n Here are some other AI stories of note from the past few days:<\/p>\n We\u2019ve seen in many disciplines how machine learning models are able to make really good short term predictions for complex data structures after perusing many previous examples. For example it could extend the warning period for upcoming earthquakes, giving people a crucial extra 20-30 seconds to get to cover. And Google has shown that it\u2019s a dab hand at predicting weather patterns as well.<\/p>\n Several figured from the post showing how MetNet integrates data into its ML-based predictions. Image Credits:<\/strong> Google<\/p>\n<\/div>\n MetNet-3 is the latest in a series of physics-based weather models that look at a variety of variables, like precipitation, temperature, wind, and cloud cover, and produce surprisingly high-resolution (temporal and spatial) predictions for what will likely come next. A lot of this kind of prediction is based on fairly old models, which are accurate some times but not others, or can be made more accurate by combining their data with other sources \u2014 which is what MetNet-3 does. I won\u2019t get too far into the details, but they put up a really interesting post on the topic<\/a> last week that gives a great sense of how modern weather prediction engines work.<\/p>\n In other highly specific sciences news, researchers from the University of Kansas have made a detector for AI-generated text\u2026 for journal articles about chemistry<\/a>. Sure, it isn\u2019t useful to most people, but after OpenAI and others hit the brakes on detector models, it\u2019s useful to show that at the very least, something more limited is possible. \u201cMost of the field of text analysis wants a really general detector that will work on anything,\u201d said co-author Heather Desaire. \u201cWe were really going after accuracy.\u201d<\/p>\n Their model was trained on articles from the American Chemical Society journal, learning to write introduction sections from just the title and just the abstract. It was later able to identify ChatGPT-3.5-written intros with near-perfect accuracy. Obviously this is an extremely narrow use case, but the team points out they were able to set it up fairly quickly and easily, meaning a detector could be set up for different sciences, journals, and languages.<\/p>\n There isn\u2019t one for college admission essays yet, but AI might be on the other side of that process soon, not deciding who gets in but helping admissions officers identify diamonds in the rough. Researchers from Colorado University and UPenn showed that an ML model was able to successfully identify passages in student essays that indicated interests and qualities<\/a>, like leadership or \u201cprosocial purpose.\u201d<\/p>\n Students won\u2019t be scored this way (again, yet) but it\u2019s a much-needed tool in the toolbox of administrators, who must go through thousands of applications and could use a hand now and then. They could use a layer of analysis like this to group essays or even randomize them better so all the ones who talk about camping don\u2019t end up in a row. And the research exposed that the language students used was surprisingly predictive of certain academic factors, like graduation rate. They\u2019ll be looking more deeply into that, of course, but it\u2019s clear that ML-based stylometry is going to stay important.<\/p>\n It wouldn\u2019t do to lose track of AI\u2019s limitations, though, as highlighted by a group of researchers at the University of Washington who tested out AI tools\u2019 compatibility with their own accessibility needs. Their experiences were decidedly mixed, with summarizing systems adding biases or hallucinating details (making them inappropriate for people unable to read the source material) and inconsistently applying accessibility content rules.<\/p>\n At the same time, however, one person on the autism spectrum found that using a language model to generate messages on Slack helped them overcome a lack of confidence in their ability to communicate normally. Even though her coworkers found the messages somewhat \u201crobotic,\u201d it was a net benefit for the user, which is a start. You can find more info on this study here<\/a>.<\/p>\n Both preceding items bring up thorny issues of bias and general AI weirdness in a sensitive area, though, so it\u2019s not surprising that some states and municipalities are looking at establishing rules for what AI can be used for in official duties. Seattle, for instance, just released a set of \u201cgoverning principles\u201d<\/a> and toolkits that must be consulted or applied before an AI model can be used for official purposes. No doubt we\u2019ll see differing \u2014 and perhaps contradictory \u2014 such rulesets put into play at all levels of governance.<\/p>\n Inside VR, a machine learning model that acted as a flexible gesture detector helped create a set of really interesting ways to interact with virtual objects.<\/a> \u201cIf using VR is just like using a keyboard and a mouse, then what\u2019s the point of using it?\u201d asked lead author Per Ola Kristensson. \u201cIt needs to give you almost superhuman powers that you can\u2019t get elsewhere.\u201d Good point!<\/p>\n You can see in the video above exactly how it works, which when you think about it makes perfect intuitive sense. I don\u2019t want to select \u201ccopy\u201d then \u201cpaste\u201d from a menu using my Image Credits:<\/strong> EPFL<\/p>\n<\/div>\n\n
More machine learnings<\/h2>\n
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<\/p>\nmouse<\/del> finger. I want to hold an object in one hand, then open the palm of the other and boom, a duplicate! Then if I want to cut them, I just make my hand into scissors?! This is awesome!<\/p>\n![\"\"](\"https:\/\/techcrunch.com\/wp-content\/uploads\/2023\/11\/couper.jpg\")
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