Just came across this piece by McKinsey: ‘What is psychological safety?’ Rang a few bells for me – in the context of change, post COVID, business reorg, major projects. And also in the context of pursuits outside work e.g. finding your level in a cycling group, coaching a football team, building new relationships. Psychologicial safety at work is just a sine qua non.
Definition
‘Psychological safety means feeling safe to take interpersonal risks, to speak up, to disagree openly, to surface concerns without fear of negative repercussions or pressure to sugarcoat bad news‘. Seems a reasonable definition for many different settings. I think often overlooked is the responsibility of the manager or supervisor to be available to facilitate ‘speaking up’ – in different situations. Maintaining a very busy status all the time is tantamount to killing the safe psychological space.
What is the reality?
Per McKinsey: ‘Psychological safety is not a given and it is not the norm in most teams’. If you believe that psychological safety is important for the individual and important to the development and sustainability of the organisation then this assessment should be of great concern to any organisation finding itself in this status.
Leadership development
I have always thought the first basic requirement for any effective manager is to take an interest in team members. There should be time to ask how are things going, how was the weekend, how are the family – or whatever works for some genuine interaction and listening. Think McKinsey right on the requirement for ongoing leadership development:
Go beyond one-off training programs and deploy a scaled system of leadership development.
Invest in leadership development experiences that are emotional, sensory, and create moments of realisation.
Build mechanisms to make development a part of leaders’ day-to-day work.
Again, if people are your number one asset, if providing a psychologically safe environment/ experience is a priority, then failing to invest in development of these skills across the leadership team is, simply, failure.
Mental health
Now seems to be on everyone’s agenda. Some of the stigma associated with talking about mental health challenges seems to be dissipating (but far from gone). McKinsey identify a number of practical steps – and I think ongoing changes post Covid, change in hybrid work and impact of AI will all drive greater requirements to understand and manage mental health.
Lower earners
Lots of good sense in this paper from McKinsey. But this last piece really caught my attention. Work is made up of people of different abilities, education, age, career direction and earnings. But all need psychological safety – all are needed to make the business work. And perhaps in the lower earning group there are greater challenges and insecurities – need to be aware of this and act accordingly. In a different environment the backs may not be making the money the forwards make but you need the whole team. In fact when the backs let you down the cookie crumbles pretty quickly.
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.
To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behaviour or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.
Functional
Always active
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
Preferences
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
Statistics
The technical storage or access that is used exclusively for statistical purposes.The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
Marketing
The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.
