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Weve compiled a list of phrases and concepts useful to understanding artificial intelligence, in particular the new breed of A.I.-enabled chatbots like ChatGPT, Bing and Bard.

If you dont understand these explanations, or would like to learn more, you might want to consider asking the chatbots themselves. Answering such questions is one of their most useful skills, and one of the best ways to understand A.I. is to use it. But keep in mind that they sometimes get things wrong.

Bing and Bard chatbots are being rolled out slowly, and you may need to get on their waiting lists for access. ChatGPT currently has no waiting list, but it requires setting up a free account.

For more on learning about A.I., check out The New York Timess five-part series on becoming an expert on chatbots.

Anthropomorphism: The tendency for people to attribute humanlike qualities or characteristics to an A.I. chatbot. For example, you may assume it is kind or cruel based on its answers, even though it is not capable of having emotions, or you may believe the A.I. is sentient because it is very good at mimicking human language.

Bias: A type of error that can occur in a large language model if its output is skewed by the models training data. For example, a model may associate specific traits or professions with a certain race or gender, leading to inaccurate predictions and offensive responses.

A brave new world. A new crop of chatbotspowered by artificial intelligence has ignited a scramble to determine whether the technology could upend the economics of the internet, turning todays powerhouses into has-beens and creating the industrys next giants. Here are the bots to know:

ChatGPT. ChatGPT, the artificial intelligence language model from a research lab, OpenAI, has been making headlines since November for its ability to respond to complex questions, write poetry, generate code, plan vacationsand translate languages. GPT-4, the latest version introduced in mid-March, can even respond to images(and ace the Uniform Bar Exam).

Bing. Two months after ChatGPTs debut, Microsoft, OpenAIs primary investor and partner, added a similar chatbot, capable of having open-ended text conversations on virtually any topic, to its Bing internet search engine. But it was the bots occasionally inaccurate, misleading and weird responsesthat drew much of the attention after its release.

Ernie. The search giant Baidu unveiled Chinas first major rival to ChatGPT in March. The debut of Ernie, short for Enhanced Representation through Knowledge Integration, turned out to be a flopafter a promised live demonstration of the bot was revealed to have been recorded.

Emergent behavior: Unexpected or unintended abilities in a large language model, enabled by the models learning patterns and rules from its training data. For example, models that are trained on programming and coding sites can write new code. Other examples include creative abilities like composing poetry, music and fictional stories.

Generative A.I.: Technology that creates content including text, images, video and computer code by identifying patterns in large quantities of training data, and then creating original material that has similar characteristics. Examples include ChatGPT for text and DALL-E and Midjourney for images.

Hallucination: A well-known phenomenon in large language models, in which the system provides an answer that is factually incorrect, irrelevant or nonsensical, because of limitations in its training data and architecture.

Large language model: A type of neural network that learns skills including generating prose, conducting conversations and writing computer code by analyzing vast amounts of text from across the internet. The basic function is to predict the next word in a sequence, but these models have surprised experts by learning new abilities.

Natural language processing: Techniques used by large language models to understand and generate human language, including text classification and sentiment analysis. These methods often use a combination of machine learning algorithms, statistical models and linguistic rules.

Neural network: A mathematical system, modeled on the human brain, that learns skills by finding statistical patterns in data. It consists of layers of artificial neurons: The first layer receives the input data, and the last layer outputs the results. Even the experts who create neural networks dont always understand what happens in between.

Parameters: Numerical values that define a large language models structure and behavior, like clues that help it guess what words come next. Systems like GPT-4 are thought to have hundreds of billions of parameters.

Reinforcement learning: A technique that teaches an A.I. model to find the best result by trial and error, receiving rewards or punishments from an algorithm based on its results. This system can be enhanced by humans giving feedback on its performance, in the form of ratings, corrections and suggestions.

Transformer model: A neural network architecture useful for understanding language that does not have to analyze words one at a time but can look at an entire sentence at once. This was an A.I. breakthrough, because it enabled models to understand context and long-term dependencies in language. Transformers use a technique called self-attention, which allows the model to focus on the particular words that are important in understanding the meaning of a sentence.

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Artificial Intelligence Glossary: AI Terms Everyone Should Learn - The New York Times

"It's not inconceivable."Nonzero Chance

Geoffrey Hinton, a British computer scientist, is best known as the "godfather of artificial intelligence." His seminal work on neural networks broke the mold by mimicking the processes of human cognition, and went on to form the foundation of machine learning models today.

And now, in a lengthy interview with CBS News, Hinton shared his thoughts on the current state of AI, which he fashions to be in a "pivotal moment," with the advent of artificial general intelligence (AGI) looming closer than we'd think.

"Until quite recently, I thought it was going to be like 20 to 50 years before we have general purpose AI," Hinton said. "And now I think it may be 20 years or less."

AGI is the term that describes a potential AI that could exhibit human or superhuman levels of intelligence. Rather than being overtly specialized, an AGI would be capable of learning and thinking on its own to solve a vast array of problems.

For now, omens of AGI are often invoked to drum up the capabilities of current models. But regardless of the industry bluster hailing its arrival or how long it might really be before AGI dawns on us, Hinton says we should be carefully considering its consequences now which may include the minor issue of it trying to wipe out humanity.

"It's not inconceivable, that's all I'll say," Hinton told CBS.

Still, Hinton maintains that the real issue on the horizon is how AI technology that we already have AGI or not could be monopolized by power-hungry governments and corporations (see: the former non-profit and now for-profit OpenAI).

"I think it's very reasonable for people to be worrying about these issues now, even though it's not going to happen in the next year or two," Hinton said in the interview. "People should be thinking about those issues."

Luckily, by Hinton's outlook, humanity still has a little bit of breathing room before things get completely out of hand, since current publicly available models are mercifully stupid.

"We're at this transition point now where ChatGPT is this kind of idiot savant, and it also doesn't really understand about truth, " Hinton told CBS, because it's trying to reconcile the differing and opposing opinions in its training data. "It's very different from a person who tries to have a consistent worldview."

But Hinton predicts that "we're going to move towards systems that can understand different world views" which is spooky, because it inevitably means whoever is wielding the AI could use it push a worldview of their own.

"You don't want some big for-profit company deciding what's true," Hinton warned.

More on AI: AI Company With Zero Revenue Raises $150 Million

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Godfather of AI Says There's a Minor Risk It'll Eliminate Humanity - Futurism

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Seedtag Advertising US, LLC, a leading contextual advertising company that specializes in machine learning and artificial intelligences to global brands, has signed a 5,909-square-foot lease at 13-15 W. 27thSt. The advertising company will occupy the entire third floor of the 11-story property in NoMad.The space will serve as the brands general, executive and administrative offices.

13-15 W. 27thSt. is located in a neighborhood that is currently seeing a boom in commercial activity, including new restaurants, hotels and shopping options, said Grant Greenspan, principal of the Kaufman Organization. Seedtag was attracted to the location as well as the buildings expansive floor plans and high quality, pre-built space.

Michael Heaner, Elliot Warren and Grant Greenspan of Kaufman Organization represented the landlord, 13 W 27 Leasehold LLC. Sebastian Infante and Jamie Katcher of Raise Commercial Real Estate represented the tenant. The space was previously occupied by Barstool Sports, who more than quadrupled in size and outgrew the building.

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Machine Learning Ad Agency Commits to Full Floor in NoMad - Connect CRE

Avalo's machine-learning tech speeds the development of new crops.

Climate change is a big problem that requires big solutions, but one place where it might have an unexpected impact is on your dinner plate. By making climate-resilient crops with the help of machine-learning approach, Durhams Avalo looks to keep those plates full.

The companys Chief Scientific Officer, Mariano Alvarez, will present tomorrow (March 29) at this years Venture Connect summit in RTP.

Like many great ideas, Avalo was conceived between a pair of friends over a couple of pints. Scientist-turned-entrepreneur Brendan Collins finally convinced his friend, Duke post-doc researcher Alvarez, that his research on plant genetics could do a lot more good in the real world than in the lab.

Over a series of happy hour beers, he convinced me that it would be way more fun to start a startup and do research in that setting than to continue to apply for faculty positions, Alvarez said. So in 2020, we did just that. And he was totally rightits way more fun.

Alvarez is used to tackling plant genetics in light of climate crises. In the wake of the Deepwater Horizon oil spill crisis in 2010, he completed PhD research at the University of South Florida looking at how plants reacted to the dramatic environmental changes.

This research brought the young scientist to Duke for post-grad study on understanding the relationship between plant genomes and the environment. Guided by Duke computer science professor Cynthia Rudin, Alvarez soon realized that machine learning and computational methods could solve many of the problems in identifying genes that are meaningful to plant environmental resilience.

Around the same time that the Duke duo figured out how to use machine learning as an impactful crop-development tool, Alvarez and Collins began doing market research. The need for faster crop development was urgent, they foundand with climate changes biggest effects just decades away, the time was ripe to launch their startup.

A lot of people dont realize just how long it takes to come up with a new variety of crops, Alvarez said. You go to the store, theres different types of tomatoes, theres different types of cucumbers, and you sort of imagine that theyre all just sitting around. It actually takes a long time for somebody to develop those varieties, anywhere between seven and 15 years. Its roughly a $200 million process to actually get them through trials and into farmers fields.

Collins, who is Avalos CEO as well as co-founder, brought his software-scaling skills from previous startup ventures, allowing them to translate this computational model into a marketable product.

Using its computational model, Avalo can rapidly test for genes that may produce a desired phenotypic outcome in a plant. The companys computational engine allows them to discover the genetic basis of complex traits, even from patchy data.

This not only makes the process of developing new crops much faster, it also makes it cheaper by slashing the number of years needed for research and development.

Traditionally, crop development has focused on traits that will make a process that takes 15 years and $200 million worth their while, usually aiming for genetic variations that lead to high yield or herbicide resistance. With Avalos technology, companies can focus on other traits, like ones that make a crop able to grow effectively in the new temperatures that result from climate change, or even tweaking a crop for better taste. This technology is perfect for a diverse industry with diverse needs.

One thing that was really interesting going into agriculture is just the scope of all of the things that people are looking for, and how diverse the agricultural system is, Alvarez said. And how unique growers needs are.

Avalo offers three buckets of product, Alvarez says. In the computational bucket falls Avalos work in providing their computational tools to companies who know what traits they want but dont have the technology to make it happen. In the second bucket, Avalo transfers specific traits into a plant for a customer.

In the third bucket is Avalos front-to-back operations. The company is currently working on a heat-tolerant variety of Chinese broccoli, for example, but their capabilities are not crop-limited, especially with the help of their three greenhouses spread across the Triangle.

The company is growing fast, but then again, so is the problem it looks to addressclimate change waits for no man, or machine.

We really only have about 30 more years until some of the biggest changes start to take effect, Alvarez said. If development takes 15 years, then we only have two shots, which is just not enough time to develop the varieties that we think were going to need to to adapt an entire agricultural system to a whole new climate.

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Durham's Avalo Uses Machine Learning To Let It Grow - GrepBeat