The rise of artificial general intelligence — now seen as inevitable in Silicon Valley — will bring change that is “orders of magnitude” greater than anything the world has yet seen, observers say. But are we ready?

AGI — defined as artificial intelligence with human cognitive abilities, as opposed to more narrow artificial intelligence, such as the headline-grabbing ChatGPT — could free people from menial tasks and usher in a new era of creativity.

But such a historic paradigm shift could also threaten jobs and raise insurmountable social issues, experts warn.

LoRA: Low-Rank Adaptation of Large Language Model Introducing ChatLLaMA: Your Personal AI Assistant Powered by LoRA! We’re excited to announce that you can now create custom personal assistants that run directly on your GPUs! ChatLLaMA utilizes LoRA, trained on Anthropic’s HH dataset, to model seamless convos between an AI assistant & users. Plus, the RLHF version of LoRA is coming soon! Know any high-quality dialogue-style datasets? Share them with us, and we’ll train ChatLLaMA on them! ChatLLaMA is currently available for 30B and 13B models, with the 7B version coming soon. Have questions or need help setting up ChatLLaMA? Join our Discord group & ask! Let’s revolutionize AI-assisted conversations together! Disclaimer: — trained for research, — no foundation model weights, — the post was ran through gpt4 to make it more coherent.

Language models (LMs) have been extensively utilized for various aided writing activities, including text summarization, code completion, and paraphrasing. LMs are effective tools for creating both natural and programming languages. Most LMs must be able to develop the next token from the sequence of earlier tokens to be useful in a wide range of applications. Due to the significance of this operation, pretraining has concentrated on improving the model’s perplexity in predicting the next token given the last tokens. However, they do have extra information that they are not using during pretraining.

For instance, they entirely disregard the following tokens while training the model to predict one token and only condition on the prefix (prior tokens) (suffix). There are alternative approaches to include the suffix in pretraining that have yet to be discussed in the literature, even though it cannot be utilized as an input to the model. They want to increase the pretraining data’s usefulness while maintaining the underlying LM’s autoregressive properties. Their strategy calls for more modeling, which at first glance could appear useless. After all, an autoregressive left-to-right LM is a primary artifact created during pretraining, and the pretraining aim closely resembles how the LM is used.

Yet, there are two reasons to explore different training objectives. Data efficiency is discussed in the first. The LM is trained using a sparse, inexpensive signal that generates a probability distribution over all potential next-token selections. However, it is only supervised using the actual next token from the training set. What if a more intense kind of supervision was used during training, where the probability distribution for the next tokens was compared to a different probability distribution? The second justification relates to other connected responsibilities. For instance, the user may prefer to fill in or edit an existing sequence of tokens in many real-world settings rather than creating text entirely from scratch.

Hundreds of books created by artificial intelligence (AI) tool ChatGPT are flooding Amazon, showing the way the technology can be adopted to produce books at scale.

Nearly 300 titles that claim to be written solely by or in collaboration with ChatGPT are listed on the online bookseller’s website, across a range of genres including non-fiction, fantasy and self-help.

Many of the books appear to be published using Amazon’s Kindle Direct Publishing tool, which allows users to quickly create, publish and promote their work using a modern-day equivalent of the self-publishing model.

Society has a limited amount of time “to figure out how to react” and “regulate” AI, says Sam Altman.

OpenAI CEO Sam Altman has cautioned that his company’s artificial intelligence technology, ChatGPT, poses serious risks as it reshapes society.

He emphasized that regulators and society must be involved with the technology, according to an interview telecasted by ABC News on Thursday night.

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This process of harvesting energy from rain is new.

Researchers in Italy have engineered an artificial leaf that can be embedded within plants to create electricity from raindrops or wind. It functions extremely well under rainy or windy conditions to light up LED lights and power itself, according to a report by IEEE Spectrum.

Fabian Meder, a researcher studying bioinspired soft robotics at the Italian Institute of Technology (IIT) in Genoa, Italy, told the science news outlet that the system could be practical for agricultural applications and remote environmental monitoring in order to observe plant health or monitor climate conditions.

Coldsnowstorm/iStock.

It functions extremely well under rainy or windy conditions to light up LED lights and power itself, according to a report by IEEE Spectrum published on Wednesday.

Since the recent announcements of OpenView’s ChatGPT, Google’s Bard, and Baidu’s ChatBot, the industry has been in a frenzy advancing Generative AI products and solutions. Brainy Insights estimates that the generative AI market will grow from USD $8.65 billion in 2022 and reach USD 4188.62 billion by 2032. This translates to over 36% CAGR making generative AI one of the next hottest areas to elevate AI innovations. The software segment will account for the highest revenue share of 65.0% in 2021 and is expected to retain its position over the forecast period.

What is Generative AI?

Generative AI is a form of AI that produce various types of content including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds. Although not a new technology, the introduction of generative adversarial networks, or GANs which is a type of machine learning algorithm has advanced the innovations in using this form of AI.

COQUI — Generative AI will Revolutionize Voice

The exciting news is that former Mozillians have just raised $3.3M for Coqui, generative AI speech synthesis for all creatives. Prior to founding COQUI, the CEO Kelly Davis led the Mozilla Machine Learning Group, which focused on speech technology. Before that, he worked at the Max Plank Institute for Gravitational Physics and also did his Ph.D. work in Superstring Theory.

ChatGPT launched a tide wave of interest in AI. For many consumers, AI is finally living up to long overdue expectations. The accomplishments of ChatGPT in a short period of time are phenomenal. But what is yet to come when AI is combined with robotics will change everything.

I have been promoting the advances in robotics for several years. I even called 2022 the year of robotics, partially because of the growing need to overcome shortages in labor and to handle tasks beyond the physical or mental capability of humans, and partially because of the continued advances that AI, accelerated processing, semiconductor, sensors, wireless connectivity, and software technologies are enabling to develop advanced, autonomous machines. Robots are no longer just for the manufacturing floor. They are hazardous material handlers, janitors, personal assistants, food preparers, food deliverers, security guards, and even surgeons that are increasingly autonomous. Essentially, they are AI in the physical world. As a result, robot competitions are heating up from middle schools to Las Vegas.

As seen at CES, robotics technology is advancing rapidly with advances in technology. My favorite examples were the multi-configurable Yarbo outdoor robot and the John Deere See & Spray. Yarbo can be a mower, a leaf blower, or a snow blower. If it could dispose of animal excrement and the annoying neighbor, it would be perfect yard tool. On the other end of the spectrum was the John Deere See & Spray Ultimate, a tractor with up to a 120-foot (36.6m) reach that uses AI/ML to detect weeds smaller than the size of a smart phone camera and spray herbicide accordingly. John Deere also offers self-drive tractors.

The new field of science has been growing with the many advances of the technology.

The surge of available AI tools has seen the introduction of a growing field called prompt engineering, as reported by Canadian Broadcasting Corporation (CBC)

Their goal is to improve the results from companies’ AI tools.

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The news outlet spoke to Simon Willison, a developer, and researcher who has studied prompt engineers. He claimed that today experts are being used for “communicating with these things” because they specialize in crafting detailed prompts to get better outputs from AI tools.

GPT-4 is here, and you’ve probably heard a good bit about it already. It’s a smarter, faster, more powerful engine for AI programs such as ChatGPT. It can turn a hand-sketched design into a functional website and help with your taxes. It got a 5 on the AP Art History test. There were already fears about AI coming for white-collar work, disrupting education, and so much else, and there was some healthy skepticism about those fears. So where does a more powerful AI leave us?

Perhaps overwhelmed or even tired, depending on your leanings. I feel both at once. It’s hard to argue that new large language models, or LLMs, aren’t a genuine engineering feat, and it’s exciting to experience advancements that feel magical, even if they’re just computational. But nonstop hype around a technology that is still nascent risks grinding people down because being constantly bombarded by promises of a future that will look very little like the past is both exhausting and unnerving. Any announcement of a technological achievement at the scale of OpenAI’s newest model inevitably sidesteps crucial questions—ones that simply don’t fit neatly into a demo video or blog post. What does the world look like when GPT-4 and similar models are embedded into everyday life? And how are we supposed to conceptualize these technologies at all when we’re still grappling with their still quite novel, but certainly less powerful, predecessors, including ChatGPT?

Over the past few weeks, I’ve put questions like these to AI researchers, academics, entrepreneurs, and people who are currently building AI applications. I’ve become obsessive about trying to wrap my head around this moment, because I’ve rarely felt less oriented toward a piece of technology than I do toward generative AI. When reading headlines and academic papers or simply stumbling into discussions between researchers or boosters on Twitter, even the near future of an AI-infused world feels like a mirage or an optical illusion. Conversations about AI quickly veer into unfocused territory and become kaleidoscopic, broad, and vague. How could they not?