Lifeboat Foundation

The remarkable zero-shot learning capabilities demonstrated by large foundation models (LFMs) like ChatGPT and GPT-4 have sparked a question: Can these models autonomously supervise their behavior or other models with minimal human intervention? To explore this, a team of Microsoft researchers introduces Orca, a 13-billion parameter model that learns complex explanation traces and step-by-step thought processes from GPT-4. This innovative approach significantly improves the performance of existing state-of-the-art instruction-tuned models, addressing challenges related to task diversity, query complexity, and data scaling.

The researchers acknowledge that the query and response pairs from GPT-4 can provide valuable guidance for student models. Therefore, they enhance these pairs by adding detailed responses that offer a better understanding of the reasoning process employed by the teachers when generating their responses. By incorporating these explanation traces, Orca equips student models with improved reasoning and comprehension skills, effectively bridging the gap between teachers and students.

The research team utilizes the Flan 2022 Collection to enhance Orca’s learning process further. The team samples tasks from this extensive collection to ensure a diverse mix of challenges. These tasks are then sub-sampled to generate complex prompts, which serve as queries for LFMs. This approach creates a diverse and rich training set that facilitates robust learning for the Orca, enabling it to tackle a wide range of tasks effectively.

In one sense, it is undeniably new. Interactions with ChatGPT can feel unprecedented, as when a tech journalist couldn’t get a chatbot to stop declaring its love for him. In my view, however, the boundary between humans and machines, in terms of the way we interact with one another, is fuzzier than most people would care to admit, and this fuzziness accounts for a good deal of the discourse swirling around ChatGPT.

When I’m asked to check a box to confirm I’m not a robot, I don’t give it a second thought—of course I’m not a robot. On the other hand, when my email client suggests a word or phrase to complete my sentence, or when my phone guesses the next word I’m about to text, I start to doubt myself. Is that what I meant to say? Would it have occurred to me if the application hadn’t suggested it? Am I part robot? These large language models have been trained on massive amounts of “natural” human language. Does this make the robots part human?

Although I’m swearing off studies as blog fodder, it did come to my attention that Vulcan Cyber’s Voyager18 research team recently issued an advisory validating that generative AI, such as ChatGPT, would be turned into a weapon quickly, ready to attack cloud-based systems near you. Most cloud computing insiders have been waiting for this.

New ways to attack

A new breaching technique using the OpenAI language model ChatGPT has emerged; attackers are spreading malicious packages in developers’ environments. Experts are seeing ChatGPT generate URLs, references, code libraries, and functions that do not exist. According to the report, these “hallucinations” may result from old training data. Through the code-generation capabilities of ChatGPT, attackers can exploit fabricated code libraries (packages) that are maliciously distributed, also bypassing conventional methods such as typosquatting.

The tech world is divided over whether “artificial general intelligence” is imminent — and how worried we should be.

Paul McCartney says he has used artificial intelligence to create “the last Beatles record,” featuring vocals from the late John Lennon.

Come together right now, with AI.

More than 50 years after the group’s final studio album, Paul McCartney says he has used artificial intelligence to create what he called “the last Beatles record.”

Continue reading “The Beatles Come Together Using AI For ‘Last Record,’ Paul McCartney Says” »

Highlights from the latest #nvidia keynote at Computex in Taiwan, home of TSMC and is the world’s capital of semiconductor manufacturing and chip fabrication. Topics include @NVIDIA’s insane H100 datacenter GPUs, Grace Hopper superchips, GH200 AI supercomputer, and how these chips will power generative AI technologies like #chatgpt by #openai and reshape computing as we know it.

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Continue reading “NVIDIA’S HUGE AI Chip Breakthroughs Change Everything (Supercut)” »

Artificial intelligence-based video generation platform Synthesia has raised $90 million from investors including Nvidia, the company told CNBC exclusively.

In the digital age, SaaS businesses have started embracing transformative technologies, such as Artificial intelligence (AI) and cloud computing. According to a research firm, the market for artificial intelligence (AI) is nearly 100 billion USD, which is expected to grow twentyfold by 2030, up to almost 2 trillion USD.

Although AI promises revolutionary advancements and cloud computing enables efficient storage and processing of massive amounts of data, their rapid adoption also raises concerns about cybersecurity. In 2021, the global cost of cybercrime was estimated to be $6 trillion.

Local language data helps automated systems understand and respond to users in their own language and can help businesses reach their target audiences more effectively, said Ganesh Gopalan, founder and CEO of AI startup Gnani.ai, during a panel discussion at the Mint Digital Innovation Summit & Awards on Friday.

“If we don’t have, firstly, content in the local language, if we can’t talk to machines in the local language, then it is not possible for no system to work and, you know, reach the right audience,” said Gopalan.

The panel discussion also included Vivekanand Pani, Co-founder, Reverie Language Technologies, who agrees that to develop an AI tool for any language, the availability of data is crucial.