Friday, June 05, 2015
WISE in Qatar (think again) – thousands slaughtered for a kick-about?
Monday, November 04, 2013
WISE 2013 – Reinventing education
‘Reinventing education’ was this year’s theme, an admirable goal and badly needed as we know that the Millennium goals will be missed, that the existing model is flawed, costs too high and that demand is exceeds supply. So what happened?
As I said when I blogged the last WISE conference I attended (this is my 3rd), “Education’s a slow learner - it may be more accurate to say that education has learning difficulties. The system is fixed, fossilised and, above all, institutionalised, so the rate of change is glacial. People are, by and large, trapped in the mindset of their institution and sector. In truth, small pools of innovative practice are patchy and stand little chance of wide scale adoption. Many of the speakers repeated platitudes about education being the answer to all of the world’s problems. What they were short on were solutions. Education is always seen as the solution to all problems. The problem with all this utopian talk is that it dispenses with realism.”
Tuesday, November 08, 2011
Education’s a slow learner (lessons from WISE 2011)
Thursday, April 25, 2013
MOOCs: Who’s using MOOCs? 10 different target audiences
Thursday, July 13, 2023
AI is now opening its eyes, like Frankenstein awakening to the world
The AI frenzy hasn’t lessened since OpenAI launched ChatGPT. The progress, widening functionality and competition has been relentless, with what sounds like the characters from a new children’s puppet show - Bing, Bard, Ernie and Claude. This brought Microsoft, Google, Baidu and Anthropic into the race, actually a two horse race, the US and China.
It has accelerated the shift from search to chat. But Google responded with Bard, the Chinese with Ernie’s impressive benchmarks and Claude has just entered the race with a 100k document limit and cheaper prices. They are all expanding their features but one particular thing did catch my eye and that was the integration of ‘Google Lens’ into Bard, from Google. Let’s focus on that for a moment.
Context matters
Large Language Models have focused on text input, as the dialogue or chat format works well with text prompting and text output. They are, after all, ‘language’ models but one of the weaknesses of such models is their lack of ‘context’. Which is why, when prompting, it is wise to describe the context within your prompt. It has no world model, doesn’t know anything about you or the real world in which you exist, your timelines, actions and so on. This means it has to guess your intent just from the words you use. What it lacks is a sense of the real world, to see what you see.
Seeing is believing
More than meets the eye
OK, so large language models can now see and there’s more than meets the eye in that capability. This has huge long-term possibilities and consequences, as this input can be used to identify your intent in more detail. The fact that you are pointing your phone at something is a strong intent, that the object or place is of real, personal interest. That, with data about where you are, where you’ve been, even where you’re going, all fills out your intention.
This has huge implications for learning in biology, medicine, physics, chemistry, lab work, geography, geology, architecture, sports, the arts and any subject where visuals and real world context matters. It will know, to some degree, far more about your personal context, therefore intentions. Take one example, healthcare. With Google Lens one can see how skin, nails, eyes, retinas, eventually movements can be used to help diagnose medical problems. It has been used to fact check images, to see if they are, in fact, relevant to what is happening on the news. One can clearly see it being useful in a lab or in the field, to help with learning through experiments or inquiry. Art objects, plants, rocks can all be identified. This is an input-output problem. The better the input, the better the output.
Performance support
Just as importantly, learning in the workplace is a contextualised event. AI can provide support and learning relevant to actual workplaces, airplanes, hospital wards, retail outlets, factories, alongside machines, in vehicles and offices - the actual places where work takes place - not abstract classrooms.
In the workplace, learning at the point of need for performance support can now see the machine, vehicle, place or object that is the subject of your need. Problems and needs are situated and so performance support, providing support at that moment of need, as pioneered by the likes of Bob Mosher and Alfred Remmits, can be contextualised. Workplace learning has long sought to solve this problem of context. We may well be moving towards solving this problem.
Moving from 2D to 3D virtual worlds
Moving into virtual world, my latest book, out later this year, argues that AI has accelerated the shift from 2D to 3D worlds for learning. Apple may not use the words ‘artificial’ or ‘intelligence’ but its new Vision Pro headset, which redefines computer interfaces, is packed full of the stuff, with eye, face and gesture tracking. Here the 3D world can be recognised by generative AI to give more relevant learning in context, real learning by doing. Again context will be provided.
Conclusion
Generative AI was launched as a text service but it quickly moved into media generation. It is now opening its eyes, like Frankenstein awakening to the world. There is often confusion around whether Frankenstein was the creator or created intelligence. With Generative AI, it is both, as we created the models but it is our culture and language that is the LLM. We are looking at ourselves, the hive mind in the model. Interestingly, if AI is to have a world view we may not want to feed it such a view, like LLMs, we may want it to create a world view from what it experiences. We are making steps towards that exciting, and slightly terrifying, future.
Friday, December 15, 2023
Google has several cards up their sleeves...
What can we expect from Google’s Gemini?
Google have several cards up their sleeve on Generative AI:
- They invented it!
- Awesome AI resources
- Google Search
- Google Scholar
- Google books
- YouTube
- Google Translate
- Google Maps
- Global reach, data centres and delivery
- Above all they have DeepMind
Gemini is their big response to OpenAI and has a family of foundational models, at three sizes:
Ultra – professional version of Bard
Pro – through Bard and Google products
Nano (on device - mobile version)
An important word here is ‘multimodal’, as their model has been trained on and is capable of processing most media types, including text, images, video, audio and code. So far Generative AI has largely been a 'calculator for words'... but this lifts it into another realm. It is multimodal from the ground up (input not necessarily output).
However, one must be careful in making the assumption that this moves it more towards how humans think. Our multimodality is quite different, especially the ways we deal with sight and sound. There is little crossover.
They make claims about “sophisticated reasoning and its ability to process complex information in different formats, also the analysis and extraction of data from research articles, being able to really distinguish the relevant from irrelevant. On benchmarking, it has already outperformed human specialists in MMLU (massive multitasking language comprehension on topics like math, history, law and ethics, at 90.0% against 86.4% of GPT-4.
This is all very exciting, as it shows how competition is accelerating progress. Google has taken a cautious approach by rolling out these products across next year. Despite their ability to read multimedia, the initial Gemini models will not – at least initially – produce images or videos.
Unlike OpenAI, they have an advertising business to protect and need to be careful. They know that the writing is on the generative wall for search. As usual, they are going for integration into products approach, a rising tide rather than a single flood. Seems wise. After a tumultuous year, we are entering a more stable product development phase. Google will not see any Board bun-fights. Although there will be some surprises in store for sure.
Google have a universal, global mission, to harness our cultural information and make it available to all. Year one was revolution, year two will be evolution.
Watch video here.
Tuesday, June 25, 2013
MOOCSs: 20 ways to monetise
Tuesday, August 28, 2018
How I got blocked by Tom Peters - you must bow to the cult of Leadership or be rejected as an apostate
So far, so good. But I wasn’t blaming him and Collins for the crash. What I was actually saying is that a cult of Leadership, sustained, as Jeffrey Pfeffer showed, by a hyperactive business book publishing and training industry, produced a tsunami of badly researched books full of simplistic bromides and silver bullets, exaggerating the role of Leaders and falsely claiming to have found the secrets of success. This, I argued, as I had personally seen it happen at RBS and other organisations, eventually led us up the garden path to the dung-heap that was the financial disaster in 2008, led by financial CEOs who were greedy, rapacious and clearly incompetent. They had been fattened on a diet of Leadership BS and led us to near global, financial disaster.
Hold on Tom, I wasn’t saying you two were singularly responsible. I was making a much wider point about the exponential growth in publishing and training around Leadership, like Pfeffer in his book Leadership BS, showing that it had, arguably, led to near disaster.
And on it went, our Leadership guru descending into sarcasm and abuse. This is exactly what I have been writing about for the last ten years, the hubris around Leadership. Is this what Leadership is really about - going off in a hissy fit when you are challenged? It sort of confirms what I have always thought – that this Leadership movement is actually a Ponzi scheme – write a book, talk at conferences, make a pile of cash…. lead nothing but seminars, then take absolutely no responsibility when your data turns out to be wrong or the consequences are shown to be disastrous.







