Skip to main content

Hinton, a warning that should attract attention

 


Elon Musk warning about the dangers of AI, and calling for a six month pause in its development can be easily dismissed, after all he threatened to launch his own 'truth' anti-GPT4. Even, with all due respect, can the calls of the Apple cofounder Steve Wozniak. But when Dr Hinton, 'the godfather of AI' highlights the dangers of AI, his work in the field deserves respect, and subsequently so do the warnings he provides

Hinton's research investigates ways of using neural networks for machine learning, memory, perception, and symbol processing. He has authored or co-authored more than 200 peer reviewed publications. 

Hinton has claimed that Google had previously acted as a steward of AI development, carefully testing its work before launching a product into the public domain. Due to Microsoft's involvement with Open AI, and the subsequent launch of a GPT agent into its Edge browser, Google have launched Bard as it's early foray into the field. It won't be the last product it launches this year. 

In 2021 Google announced it's development of MUM, which it described as 'A new AI milestone for understanding information' 

MUM not only understands language, but also generates it. It’s trained across 75 different languages and many different tasks at once, allowing it to develop a more comprehensive understanding of information and world knowledge than previous models. And MUM is multimodal, so it understands information across text and images and, in the future, can expand to more modalities like video and audio.

From it's position as a leader in generative AI Google has suddenly found it was playing second fiddle to Microsoft. Hinton has stated as companies improve their AI systems, he believes, they become increasingly dangerous. “Look at how it was five years ago and how it is now,”. “Take the difference and propagate it forwards. That’s scary.” 

Hinton, quite rightly, has warned about the dangers of the misinformation / propaganda inherent in the generative tools, as have been repeatedly mentioned in this blog, he states the average person will “not be able to know what is true anymore.”

Indeed, elections will never be the same again.

In the interview Hinton warns about the dangers of AI to the job market, there are various estimates from the likes of PwC about this. By the mid 2030's most predictions state that between 30% to 50% of all jobs will be affected if not lost. 

Considering a future beyond this, Hinton, a long time advocate against the military development of AI said: “The idea that this stuff could actually get smarter than people — a few people believed that,”  “But most people thought it was way off. And I thought it was way off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.”

This needs to be understood in the light of Google's (and others) robot developments:


Given the totally inadequate governmental responses to legislation as a means of ensuring AI safety, which as even Bard said of the UK's white paper are, 'too slow and too bureaucratic' then the public conversation is acutely necessary. 





Comments

Popular posts from this blog

OpenAI's NSA Appointment Raises Alarming Surveillance Concerns

  The recent appointment of General Paul Nakasone, former head of the National Security Agency (NSA), to OpenAI's board of directors has sparked widespread outrage and concern among privacy advocates and tech enthusiasts alike. Nakasone, who led the NSA from 2018 to 2023, will join OpenAI's Safety and Security Committee, tasked with enhancing AI's role in cybersecurity. However, this move has raised significant red flags, particularly given the NSA's history of mass surveillance and data collection without warrants. Critics, including Edward Snowden, have voiced their concerns that OpenAI's AI capabilities could be leveraged to strengthen the NSA's snooping network, further eroding individual privacy. Snowden has gone so far as to label the appointment a "willful, calculated betrayal of the rights of every person on Earth." The tech community is rightly alarmed, with many drawing parallels to dystopian fiction. The move has also raised questions about ...

What is happening inside of the black box?

  Neel Nanda is involved in Mechanistic Interpretability research at DeepMind, formerly of AnthropicAI, what's fascinating about the research conducted by Nanda is he gets to peer into the Black Box to figure out how different types of AI models work. Anyone concerned with AI should understand how important this is. In this video Nanda discusses some of his findings, including 'induction heads', which turn out to have some vital properties.  Induction heads are a type of attention head that allows a language model to learn long-range dependencies in text. They do this by using a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. For example, if a model is given the sequence "The cat sat on the mat," it can use induction heads to predict that the word "mat" will be followed by the word "the". Induction heads were first discovered in 2022 by a team of researchers at OpenAI. They found that induction heads were present in ...

Prompt Engineering: Expert Tips for a variety of Platforms

  Prompt engineering has become a crucial aspect of harnessing the full potential of AI language models. Both Google and Anthropic have recently released comprehensive guides to help users optimise their prompts for better interactions with their AI tools. What follows is a quick overview of tips drawn from these documents. And to think just a year ago there were countless YouTube videos that were promoting 'Prompt Engineering' as a job that could earn megabucks... The main providers of these 'chatbots' will hopefully get rid of this problem, soon. Currently their interfaces are akin to 1970's command lines, we've seen a regression in UI. Constructing complex prompts should be relegated to Linux lovers. Just a word of caution, even excellent prompts don't stop LLM 'hallucinations'. They can be mitigated against by supplementing a LLM with a RAG, and perhaps by 'Memory Tuning ' as suggested by Lamini (I've not tested this approach yet).  ...