Interesting recent interview with Geoffrey Hinton (‘father of AI’). ‘they still can’t match us but they are getting close…they can do little pieces of reasoning’. It’s not ‘just autocomplete or statistics’. ‘It’s inventing features and interactions between features to tell what comes next’. Reviews dangers and, in particular, the existential threat.
‘We are just a machine…just a neural net…no reason artificial nets cannot do what we do’. We are much more efficient in terms of use of energy. But the machines more efficient in learning from data.
Differing views – Hinton and Lecun
We are entering a period of huge uncertaintly. Yann Lecun has a different view to Hinton. If they end up smarter then us and decide want to take control – then trouble. Yann Lecun – AI has been built by humans (will have a bias towards good). Per Hinton depends on whether made by good people.
If you send battle robots to attack then becomes easier for rich countries to attack/ invade poorer countries.
Hinton the socialist?
Big Language Models will cause a big increase in productivity. Gave example of answering letters and complaints for a health service. Can do 5 times as much work. If get big increase in productivity – wealth will go to making the rich richer – particularly in a society that does not have strong unions.
Big chatbots – replacing people whose job involves producing text. How do we know more jobs will be produced than lost?
Jobs to survive – where you have to very very adaptable and skillful e.g. plumbing (working in awkward spaces). What about reasoning?
Multimodal AI
Most impactful developments in AI over next 5 years – multimodal language models – to include video (e.g. youtube videos). Yann Lecun would say language is so limited. Soon will be combined with multiple modalities. Attach visual AI to text AI (cf. Gemini at Google).
Thoughts on development of AI
Transformer architecture was invested at Google. Announced in a paper in 2017. Bard was delivered a couple of years later by Google – but Hinton took a couple of years to realise the significance of this.
If we keep training AI based on data created by AI – what will be the impact? Hinton says does not know the answer. Would be much easier if all fake data were marked as fake data.
How could you not love making intelligent things? We want experimentation but we do not want more inequality. How do we limit the potential harms?
Top 6 harms
Bias and discimination – present now, but relatively easy to fix (have a system which is significantly less biased than the system it is replacing. Analyse the bias and correct.
Battle robots – will be built by defince departments. How do you stop them? Some form of Geneva Convention?
Joblessness. Tey to ensure increase n productivity helps people who loase thier jobs. Need some form of socialism
Warring echo chambers – big companies wanting you to click on things and make you more indignant – begin to believe conspiracy theories. This is a problem to to with AI – not LLMs
Existential risk – important to understand this is not just sicence fiction/ fearmongering. If you have something a lot smarter than you which is very good at manipulating people – do people stay in control? We have a very strong sense of wanting to achieve control. AI may derive this as a way of achieving other goals. Jann Lecun argues that the good people will have more resources than the bad people. Hinton not so sure. Not conviced that good IA will win over bad AI.
Fake news – need to be able to makr everything that is fake as fake. Need to look at how to do with AI generated stuff.
Managing the risks
How can we limited the risk? Before the AI becomes super intelligent can do empirical work to understand how it might go wrong/ take control away. Government could encourage companies to put more resources into this. Hinton has left Google to participate in this discussion/ research.
How to make AI more likely to be good than bad? Great uses: medicine, climate change, etc. But need to put effort unto understanding the existential risks.
Interesting to read about developments in high performance computing – and potential impact for current data centres and demands for future data centres. Is it possible we will see low performance computing performed on-prem and high performance in the cloud? Or is this too much of a simplification? Will corporates design hybrid on prem/ cloud where high computing available across both?
Still hampered with paper
And then you think about organisations still (in 2023) bogged down in lots of paperwork? Unable to leverage computing – not to mind high performance computing.
Vested interests
If the high performance computing is to serve a purpose – be that improved/ speedier decision making (perhaps #AI assisted), better analysis of data, improved customer response times- then the organisation itself – perhaps the industry – needs to change. If we have vested interests sitting in key points fo control – making a very good living – are they holding up true transformation (ultimately to be enabled/ accelerated by high performance computing)?
Innovators and disruption
And that’s why we need the innovators – not going to hang around while vested interests frustrate progress. Lots of new technologies, lots of options for deployment and significant disruption. And high performance computing just an element of this.
Will be interesting to see how Elon Musk and Tesla go forward – what changes, if any, getting strategy right. On the face of it looks tricky: slowing global economies, higher finance costs for consumers, significant additional capacity coming on line, competitors catching up, Tesla cars more expensive than previously.
Musk has attracted mixed coverage for his Twitter take over – at the same time as his car company is experiencing these challenges. How much does the Twitter acquisition impact sales of Tesla cars? Are current/ potential Tesla owners concerned that the founder may be distracted? Are Tesla shareholders concerned?
But Musk and Tesla have been and continue to be innovators – and competitors should not underestimate this commitment to getting the tech right, creating new experiences for drivers. Why would Tesla not continue to deliver new ideas, innovations, enhanced experiences?
In some ways reminds me of Ryanair and Michael O’Leary. O’Leary has been the people’s champion – low cost flights, enabling travels for the masses. He has not always been popular – and some of his PR stunts have not always been well received. But he has stayed focused, he seems to have used each recession as an opportunity to strengthen his business. He has created capacity heading into (or during) downturns – only to accelerate in the upswing. He has weathered economic downturns and COVID19.
Will be interesting to see how Tesla moves forward. Reminded me of Rumelt’s ‘Good Strategy Bad Strategy: The core of strategy work is always the same: discovering the critical factors in a situation and designing a way of coordinating and focusing actions to deal with those factors. A leader’s most important responsibility is identifying the biggest challenges to forward progress and devising a coherent approach to overcoming them.
Interesting to read this piece on BBC today: new tool to detect breast cancer.
Read the various inputs from different doctors – fully get it: not a replacement for the doctor and not a replacement for visiting the doctor. But when you read about the shortage of doctors, the pressures on hospitals, the numbers of late detected cancers – we have to look at tools which enable patients/ potential patients work in partnership with their doctors.
Unfortunately thinking about dementia is not too difficult for most of us – having seen the impact in our families and/or close friends. Just listened to a series of three podcasts published by The Conversation.
I was familiar with the ideas about Alzheimers – build up of plaque/ tangles in brain (amyloids, tau). And had read recently about some exciting developments in terms of drugs which may slow down development of Alzheimers.
But found the podcasts very interesting on a number of fronts (as a layperson);
Potential relevance of education and socio economic background to onset of dementia
Possible relevance of untreated high blood pressure during peoples’ 30s and 40s
Research of Common Cold Sore Virus (HSV1) as a possible cause/ instigator of Alzheimers
Impact of sports injuries/ concussion – CTE (Chronic Traumatic Encephalopathy)
Typical clinical diagnoses for Alzheimers
Views of a range of researchers in US and UK
Unfortunately seems like we are going to have to live with Alzheimers and other forms of dementia for some time – and will be more prevalent as the population lives longer. But as one of the researchers reminds us – so little progress on cancer for so long and now we are seeing personalised medicine in cancer treatments. Hopefully we will also see breakthroughs of significance in field of dementia in next few years.
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