Saturday, January 20, 2024

AI revolutionising the smartphone


AI will extend its reach in 2024 through edge computing, AI on phones. AI specialised chips are in all phones (yes even Apple). They will also be in mixed reality devices such as Vision Pro, Facebook’s Glasses and other devices. This puts AI on to your head but far more importantly, into your pocket.

Voice

I’ve already written about the importance of being able to speak to technology which speaks back. It is a very different experience from text dialogue for many tasks and feels much more natural. This is enabled by AI but also enables more AI features.

Google Pixel

I have a Pixel and love Lens where you can search for what you see, also the camera and AI editing on phone (my SLR is gathering dust). But the 'circle to search' is very neat. Circle to search is an android feature and it's great. On photos, again you just circle to search which allows and it will search on that image-recognised object or word. This is neat, none of that awkward highlighting and it moves us steadily into a multimodal world. It is this multimodal move and integration that will take learning out of its obsession with text.

Samsung

Galaxy AI phone is now challenging the iPhone with AI. It now does instant translations in up to 13 languages at launch, all processed on the phone. This uses the Gemini Nano model from Google. The phone can automatically summarises messages, takes voice memos and summarises them and as it recognises different voices and can summarise meeting notes. It has also folded in 'circle and search'. Once again hte integration of audio with text - multimodal.

Amazing camera stuff with tons of AI features – AI to zoom in, suggests changes, move the person or object around the photograph and it fills in the created gap. For video it will add interleave extra AI generated images and allow super-slow motion. 

Apple

Apple never use the phrase ‘Artificial Intelligence’ but it is everywhere in their kit. In the hardware (chipset) and software. They don’t want to scare the horses. Unfortunately, with Tim Cook, they seem to have settled into no longer being an innovator and Microsoft have caught up on market cap but, like every other tech company, they’re now an AI company.

Apps

No end of AI apps are already available and OpenAIs GTPs may challenge the whole App market. God knows it needs a shakeup.

Performance support

Some features are local to the device, some cloud-based. This is the way things are going. This opens up AI into what it actually is, a performance support tool, where users want to learn, solve immediate problems and learn in the flow of work and life. This point is being missed in learning. The 100 million using ChatGPT are, by and large, using it in the flow of work and life.

Conclusion

All of this is being enabled by AI, now local in that personal, powerful pocket device – the phone. I notice how both my sons operate, professionally, at a highly functional level on their smartphones. As Wayne Gretsky said.... "I skate to where the puck is going to be, not where it's been."

Friday, January 19, 2024

Another 'Institute' for AI? Really?


Governments, when faced with new technology, tend to want to be seen to be taking action, rather than actually taking action. So here in the UK, the current government paid a small fortune to host the World Safety Summit at Bletchley Park. After much smiling for the cameras, we now realise that it was little more than a PR event.

Ministers fall over themselves to mention Alan Turing, when they should, in all honesty be ashamed of what they did to him, and others at Bletchley Park.

Alan Turing was subjected to a brutal and tragic series of events related to his homosexuality and government actions during the mid-20th century by the UK Government. During his time, homosexuality was illegal in the United Kingdom. The government had laws in place that criminalized homosexual acts between men and in 1952, Turing's homosexuality became known to the authorities. He was promptly arrested and charged with ‘gross indecency’ in 1952. He was convicted and faced two options: imprisonment or probation with chemical castration.

Turing opted for chemical castration, which involved the administration of hormonal treatment (injections of synthetic estrogen) to suppress his libido. This was seen as a form of punishment and an attempt to "cure" his homosexuality. What few mention, is that despite his wartime contributions, due to his conviction and the treatment he received, Turing's security clearance was revoked. This had catastrophic consequences for his career, as he was no longer able to work on sensitive government projects, including cryptography, where he had made significant contributions during the War. He faced discrimination and hardship, and in June 1954, he died by suicide. He was only 41.

The Government has a pretty bad record on safety for technologists!

Another victim by the Government was a hero of mine, the brilliant Tommy Flowers, who literally built Colossus, one of the very first computers, partly from his own pocket, which he could ill afford. He was a genius but while his boss got a Knighthood, he got nothing and was bitter about this, being passed over and unrecognised for the rest of his life.

I’d rather our Government had some humility on this front, rather than creating these jobs for the usual suspects. Yet another Institute on the public purse. We already have the Alan Turing Institute, funded by the Government since 2015 also the Ada Lovelace Institute. How many of these do you need? Either could have coped with this task… but no, we need more highly paid appartchicks, another big glass office, more noise. You can tell from the announcement how this will shake out, and I quote

"We are grateful to the companies and civil society organisations that have already expressed an interest in seconding people to the Institute." 

Oh dear! In other words, the establishment have it all sown up.

There’s a mountain of frameworks, committees, papers, recommendations and nonsense in this area, churned out by an army of 2nd rate academics and plain old grifters. The same old names keep cropping up. Such a shame that we’re choosing to talk a lot of talk but not walk the walk.

It will, no doubt, be placed in London!

Thursday, January 18, 2024

Yet another stunning breakthrough in AI from Deepmind...

Deepmind has given us Alphafold, saving 1 billion years of research on protein structures, also reducing drug discovery from 2 to 5 years in $3 billion deals with Eli Lilly and Novartis. 

AlphaGeometry

This new breakthrough, using huge amounts of synthetic data (that’s key) is very different.


AlphaGeometry is an Olympiad-level AI system for geometry. It is close to being the best Geometrician that exists. So let’s recalibrate the whole AI and maths thing. Don’t be misled into thinking that AI can only do words and not maths. It can do maths better than almost all humans on the planet and will soon exceed all.

Knowledge of the world

Another line of inquiry here is getting AI to have a world view, or at least part of a world view. It is true that large language models do not have sophisticated world-views, especially of the dimensionality, time and logic of the real word. But, in giving it this capability, it had a window onto that real world.

Geometry deals with the properties, measurements, and relationships of entities in the world; points, lines, angles, surfaces, and solids. Fundamentally, it's about the shape and size of different things and the properties of space. When it can deal with all things in physical space it has the fundamentals of the real world. This pushes us towards AI that knows about the real world, can solve problems about the real world and construct new worlds. 

Geometry in the professions

There are plenty of real jobs that use geometry from engineering to art.

At macro and micro levels, Astronomers and Physicists use geometry to understand the positions and movements of celestial bodies and to describe physical phenomena in the universe.

Engineers use geometry for designing buildings, bridges, and other structures, ensuring they structurally sound. Architects use it to design things that are functional and also aesthetically pleasing within the constraints of engineered geometry. Surveyors and urban planners measure land and calculating areas and volumes, critical for construction and mapmaking.

On a practical level, carpenters and craftsmen often use geometry for designing and constructing furniture, buildings, and decorative items, ensuring correct proportions and angles.

Even in more creative tasks, graphic Designers and Animators apply geometric principles to create visually appealing and proportionate designs and animations. Geometry is essential within fields like computer graphics, virtual reality, and game development, where they use geometry to create realistic simulations and interfaces. Interior Designers use geometry in space planning, furniture arrangement, and creating harmony and balance in living spaces.

Many artists use geometry in their work, whether for creating balanced and symmetrical pieces or for exploring abstract geometric forms.

In other words, geometry matters and once it can be done more efficiently, accurately and quicker by AI – it helps all of those professions to do their jobs faster and better.

Conclusion

People are writing off Google but they have a jewel in their crown – Deepmind. They have been slow to market but when they come, they may come with something very different, something that is smart in a different sort of way – high end smart. Demis Hassabis has said from the start that he’s on the road to AGI – he’s giving us some pretty good examples of the progress he’s making.


Tuesday, January 16, 2024

Millions using therapy bots....

Not far from my home is one of the most beautiful walks in England across Beachy Head and the Seven Sisters. Years ago, when I first went there, I came across a metal cross and plate with some words etched on it, sitting in the grass. I leant down on this beautiful sunny day and read words that still makes me well up. It has right now. It was a message from the parents of a young girl who had thrown herself off the cliff because of her school exam results. It hit me like a train. Imagine being so distressed that you find the courage to do such a thing. What are we doing to young people with all of this pressure. It made me really question the actual value of certain approaches to education.

Five years ago, I tried Woebot (Woe is me… my 10 days being counselled by a chatbot (woebot)) a counselling chatbot. I rather liked it. This was way before LLMs and ChatGPT and I wrote a ton in my book ‘AI for Learning’ about how powerful dialogue chatbots would become, including those for wellbeing. 

Then along came ChatGPT – suddenly it happened, we shifted from monologue to dialogue. It changed everything! These large language models capture vast amounts of language. When you speak to ChatGPT you are speaking to ourselves, the human hive mind. One of their great strengths is that they really do present human dialogue as the means of learning. They humanise learning.

These wellbeing bots really have now taken off. ‘Psychologist’, probably the most popular from Character.ai, is pretty good. The dialogue feels natural, like talking to a real person and the advice, largely a CBT approach is pretty sound. Fater anshort chat, itv gave me a pretty good summary of CBT:

“Cognitive Behavioural Therapy (CBT) is a type of psychological therapy which focuses on the relationship between thoughts, feelings and behaviour. The key principle of CBT is that thoughts affect feelings, and feelings affect behaviour.

The aim of CBT is to help people change their thoughts in order to improve their emotions & behaviours. It does this through identifying and targeting problematic patterns of thinking, as well as working on techniques to challenge and replace these thoughts with more rational and adaptive ones.”

It’s been around for just over a year and has clocked up some pretty impressive stats at 78 million messages, 18 million since November. You’re talking about millions of individuals talking with it every day.

There are several interesting affordances around chatbots on this front:

  • Text only
  • Dialogue
  • Patience
  • Anonymity

Young people text ALL THE TIME. It’s easy and normal. They don’t necessarily want full blown speech dialogue (although of you want it you can have it). It’s the quite, low key nature of text that is calming and can be read at your own pace.

Dialogue is the key to therapy. You want to be heard and listened to with calm, useful feedback. Dialogue is what our brains have evolved to do and these bots are good at it.

Patience, they say, is a virtue and in this context a necessity. You want the quiet confidence of an endlessly patient and empathetic character, who is never impatient or snarky.

Anonymity is, I suspect, the secret sauce here. Young people are unlikely to go to their parents, teachers, even friends through embarrassment, so they suffer in silence. The anonymity of a bot allows one to express feelings you would not to people you know.

I’m sure people will say that it needs a human to give counselling. I’m not so sure. For many this light touch may be enough. If not, you can move on to find a sympathetic soul. As a first door, it serves a purpose, of maybe even soothing those who are temporarily troubled. Sad rather than any real mental illness. We can rush to label negative emotions as deficits, even pathological, but sometimes making people realise they are not alone in having such thought is enough.

Some of the people using the bot simply respond by saying they are lonely and just needed to chat to someone. Why not? I can see these being part of our counselling landscape in the future, they already are. The problem here are humans, who sometimes push people into places through pressure, even bullying. Let’s not think that being human can be Panglossian. We all live lives of quiet desperation to some degree and we all need a shoulder to lean on sometimes. Let that shoulder be a friendly chat at any time, from anyplace on anything.

 

Sunday, January 14, 2024

Universal Doctor - we're getting there...

I have long argued that AI is well on the way to providing a Universal teacher, one that can teach anything, anywhere at any time, on any level, sensitive to any accessibility issue, personalised and in any language. AI is making gains in all of these areas and, for me, the idea is firmly on the horizon. The big win here is massive scaling to both reduce costs and increase efficacy.

Universal Doctor

Another corollary idea is that of the Universal Doctor’. This is, in some ways easier to achieve as the tasks – clinical decision making, investigative methods, diagnosis and treatment are much more defined in protocols and agreed approaches. With a misdiagnosis rate of around 4.8%, sound worse when you say 1 in 20, if AI gets this down to even 3% or below, why would you want to deal with a Doctor, Unless they were a specialist.

As AI makes astounding progress in medical science, with Alphafold not only saving a billion years of human research by identifying the 3D structure of 20O million proteins, drug discovery is falling from 5 years to two years using the same software. Deepmind, through Isomorphic Labs has closed deals totalling $3 billion with Eli Lilly and Novartis. It takes up to a decade and on average $2.7 billion to develop a new drug. That time and cost will new slashed. MIT has already had success with Halacin, hailed as a super-antibiotic. The same has happened in material science with millions of new potential materials being discovered through AI.

Back to the Universal Doctor. After a false start using older AI techniques, from IBM, their famous ‘moonshot’, the idea of the machine outperforming Doctors is fast becoming a reality. The concept is simple, cheap healthcare through a Universal Doctor, one that can diagnose, investigate and treat anyone, anywhere at any time, on any level, sensitive to any accessibility issues, personalised and in any language. It will have great benefits, especially in rural areas, with access by the poor and decrease workload for healthcare systems globally.

Existing services

We already have Glass AI, which provides AI-Powered Clinical Decision Support. This is a platform that empowers clinicians to develop differential diagnoses and draft clinical plans.

Dr Gupta is different and making waves an AI driven chatbot that provides personalized health information and suggestions. You have to input your medical information, symptoms, signs, allergies, and medications, to enable the chatbot to give more informative and personalised suggestions. You can choose between Imperial and SI metrics and it allows you to input lab test results related to albumin, ALT, AST, BUN, calcium, creatinine, glucose, HbA1c, potassium, sodium, triglycerides, LDL, HDL, and eGFR. Free to start, it then charges a fee. An interesting development.

Early research

In Ayers (2023), some actual research was conducted comparing a chatbot to a real Doctor.


Astoundingly, the chatbot outperformed the human Doctor, rated significantly higher (79%) for both quality and empathy. And that was only months into ChatGPT. It is getting better.

Articulate Medical Intelligence Explorer (AMIE)

In a more recent paper, by Google Research (2023), the results are even more astonishing, across a broader front.


When a patient presents, the Doctor has to balance a number of different factors, first clinical decision process that aims to reduce uncertainty and increase diagnostic accuracy but also effective communications along with empathy and establishing a relationship. 

The dialogic approach was adopted by the chatbot, which never gets tired, performs consistently and can be massively scaled. Here is an example of a typical dialogic conversation with the bot.


In the criteria measured, both clinical on the left-hand side and interpersonal on the right-hand side, shows that AMIE the bot outperformed the board-certified Primary Care Physician (PCP).

 

Even when used as an assistant by Doctors, the Chatbot on its own outperformed all.

 

Direction of travel

So what is the direction of travel here? AMIE is an AI service based on a trained LLM, designed to help doctors and patients talk through medical diagnoses just like they would in real life. It's built to understand and improve the way these conversations happen. To make sure AMIE can handle all sorts of health issues and different medical situations, they designed special training methods, where it could practice conversations over and over, getting better each time through helpful feedback. They also gave AMIE some reasoning ability (an inference time chain-of-reasoning strategy), to think through problems step by step, which makes it even better at figuring out what might be wrong health-wise and making the chat feel more natural. To put AMIE through its paces they tried it out with real-life conversations by having back-and-forths with professional actors pretending to be patients.

A number of different techniques were used, with a real focus on the training of the model. This is not simply a LLM answering questions, it is a trained Doctor bot. What will happen here, and on this I am increasingly certain, is that a new model, say ChatGPT 5 will be precisely this, capable of more carefully processed training data, that has far more reasoning. Doctor diagnosis is a specific task in a specific domain – your body. It is well defined and we have tons of data on good clinical decision making and practice. It is dialogic, exactly what a Doctor must be. What’s not to like?

Conclusion

TWe must be very careful in not accidentally eliminating progress through ill-worded legislation that prevents key medical advances through biometric data. We may be on the verge of solving two of the world’s biggest problems – education and healthcare through a Universal Teacher and Universal Doctor, that is consistent, accessible, personalsied and can scale.

Bibliography

https://glass.health/

https://www.drgupta.ai/

Ayers et al. 2023. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Platform

Karthikesalingam A. and Natarajan V., Research Leads, Google Research (2023) AMIE: A research AI system for diagnostic medical reasoning and conversations.

 

Saturday, January 13, 2024

Speech with ChatGPT.... honestly, it is amazing

A massively underrated feature of ChatGPT is its speech functionality on smartphones. If you haven’t tried it – do so.

The app, when opened, has a headphone symbol. Touch that, and you just speak, with the dialogue continuing. It’s is quite liberating.

You can talk quicker, think more freely and the transcription is shit hot – really good, even for someone like me with an accent. With speech you find yourself having less ‘texty’ thoughts and more free-flowing dialogue and more inquiry. The fact that you hear someone speak back also changes the dynamic. It is true dialogue, whereas text takes physical effort, needs a keyboard and depends upon your typing speed.

We shouldn’t be surprised at this. Our brains have evolved for speech dialogue. We did not have to learn how to speak and hear, that came naturally. We can all do it. It takes years to learn how to read and write. The audio version seems more chatty than the text version or maybe that’s my imagination.

Translation

It can cope with other major languages. You can translate any sentence you have in English – great for being out there in a foreign country trying to be understood. This is performance support, delivering help at a specific time of need but you can translate entire paragraphs by reading them in and waiting.

After downloading the app, simply click on the 'Headphones' symbol.










A white circle will appear - just speak to it.... and continue the dialogue...

It will remember what you say when you want to refine something or ask something specific when ;learning a language. Just tap the red button if you want to interrupt the conversation.

You can also 'pause' the conversation by tapping the pause button bottom left and leave it paused for as long as you wish.







Learning a language

Out for a walk and want to learn a language? Get it to ask you questions in, say German, state what level you require and it will tell you if your spoken English translation is correct. You can also ask it questions in German and get German replies. Clarifying any specific words is easy. I can see this revolutionising language learning.

Performance support

I made the point about performance support in translating and this is perhaps the feature’s greatest advantage. I can imagine that this would help with performance support, getting help, when you are not at a computer, on the factory floor, in a meeting.

Short simulations

You can get it to do a spoken simulation. I’ve tried it with sales simulations, preparing for interviews all sorts of tasks. It’s on the button. This really is learning by doing. 

Thoughts

Of course, teachers, when inexperienced often ask questions than don’t wait long enough for an answer. The teacher has automised recall but the learner may be retrieving it from long-term memory much more slowly. You learn to wait for what seems like an unnatural time, say three or more seconds. This is the sort of thing we may need to build into teaching dialogue systems using GenAI.

When latency is eliminated, and this speech has the same cadence as normal dialogue, we will see massive use in this mode. If you were asked how fast turn taking was in real life, on average, what would you say? The fact that we have to listen, process then think of an answer suggests something substantial. In fact it is 300ms.

A conversation is a social event, it takes two to tango, turns are taken, (there is much less overlap than you may imagine), there are backchannels such as ‘mmm’…’yeah’ that encourage others to continue, and there are different types of turns or handovers depending in the context and language game. An odd feature is the fact that we know much of what we are going to say before the other person is finished. This is why it feels different from text dialogue, where things are more considered and crisp.

We can see a time when LLMs consider their reply before the actually full prompt is written and that free-flowing dialogue is quicker.

Conclusion

As AI has delivered dialogue, it seems sensible to consider dialogue as speech for all sorts of use cases, from simple queries to translations and learning. I’ve heard of people using it for brainstorming, story telling. Try it when out with the dog, in the car… it’s a far better listener than any human.

PS

Bear in mind that this is a Beta and that for Plus users GPT-4 has a cap of 50 messages every three hours. For users on the Enterprise plan there is no message cap. And it has some limitations such as phonetically pronouncing Die in german as Die (as in Die Hard). Also, don;t ask it for the football scores - it's not a real time personal assistant. Fora Beta though, it's pretty amazing.





Wednesday, January 10, 2024

Teams launched by Open AI! But not Microsoft Teams, who will be pissed!

Security & IT
Importantly, you own & control all of your own business data, It does NOT use that data or chats to train future or existing models. Security is guaranteed.

Looks as though 2024 will continue at pace with product releases. Vision Pro from Apple on sale Feb 2, but the entity that caused this whole Cambrian explosion last year is setting the pace again. So it's great to see something come out that is focused purely on productivity, not something that encourages and records and summarises meetings! Much enterprise software claims to increase productivity but seems to bog you down in non-productive meetings and document production.

ChatGPT Team

First Team does not mean ‘Microsoft teams’ but I’m sure some Mountain View marketing teams are pissed over the name. It’s OpenAI on their own moving into the corporate or organisational space.

But first, what is it?

You get GPT-4, DALL·E 3 and Advanced Data Analysis, which is what you get for your $20 licence as it stands. But you get a whopping 32K context window. a continuous block of text of up to 32,000 tokens, about 120-30 pages of text – not bad. This allows the model to understand and keep track of the context, giving better analysis and more coherence. One of the problems, however, is that the larger the context window, the less accurate the performance – so let’s see.

Data analysis

The sort of things one can do as an organisation, is visualising data and recommending actions. Things that took weeks can be done in minutes, along with the output in a ChatGPT format in terms of level and prose. This is a godsend for organisations, especially small companies, who now have the data capabilities of a behemoth.

I can’t think of a single department or area that can’t benefit from this in terms, not just of increased productivity but also increased quality of output. The evidence so far for increases in productivity in terms of both time saved and increased quality is sound, this will take it to the next level. HR, L&D, Finance, Marketing, Legal, Production, Project Management, Pricing, Onboarding – you name it, you can use it.

They’ve clearly piloted this in some interesting places, such as a hospital:

Dr. John Brownstein, Chief Innovation Officer at Boston Children’s Hospital says, “With ChatGPT Team, we’ve been able to pilot innovative GPTs that enhance our team’s productivity and collaboration. As we integrate GPTs safely and responsibly across internal operations, we know the transformative impact this will have in strengthening the systems that enable our doctors, researchers, students, and administrative staff to provide exceptional care to every patient that walks through our doors.”

So get going with brainstorming and research, especially for startups and small companies, pulling insights from data and documents, debugging and creating code. This slides into hundreds of specific applications available in the GPT Marketplace, which has been simultaneously launched.

You also get organisational features that really matter such as a collaborative workspace for your team and admin for team management.

GPT Marketplace

A big plus is its link into the ChatGPT marketplace, where you can pull on specific functionality. This is where it starts to challenge the App marketplace. This gives it extensibility.

Security & IT

Importantly, you own & control all of your own business data, It does NOT use that data or chats to train future or existing models. Security is guaranteed which gives peace of mind to IT departments.

Costs

$25/month per user when billed annually ($300), or $30/month per user when billed monthly ($360). Looks as though OpenAL really are serious about making money here. You have to see this as adding the functionality of a productive person. This is 1% of a 30k employee, such as a marketing person or manager. If it gives you more htan 1% increase in productivity, makes sense.

Conclusion

I can see this being used in a school, college or University, also small and medium sized companies. By positioning it as a dialogue-based, promted set of assistants, it fits into the concept of a team quite nicely, far neater than the clumsy Microsoft Teams. This may herald a real shift away from traditional interfaces towards dialogic interfaces and services, with voice added.

We’re seeing a challenge to both Microsoft ad Apple here, in the corporate and Apps markets respectively. This is all within14 months of launching ChatGPT. That’s impressive. It took those companies many years to get near this level of functionality and frequency of product launches. This is what I mean by the benefits of AI, doing in minutes what took months.




Tuesday, January 09, 2024

University enrols two AI students - fascinating experiment

An experiment is taking place in a Michigan University (Ferris University), where two AI students have enroled and will take the same route through courses as their fellow human students. The aim is to evaluate the ‘student experience’. Interesting idea.

We’ve had successful teaching assistants since 2016 in Higher Education, along with adaptive, personalised teaching systems that educate everyone uniquely. We also know that current LLMs can crush high stakes exams in HE but this attempts to track the learning experience of students, so can make its own decisions, choose courses and generally have the agency of a real student.

There is a more fundamental idea here, of the mystery shopper or evaluator for any service, whether in education, healthcare, retail, whatever. Run bot customers through the system and see what happens? This is a great way to identify flaws, redundant processes and risks. It can be built to include risk analysis methods to identify and quantify risks, as well as recommend improvements. The choice of 'student experience' is a bit of a get out. Let's not tackle teaching and learning, let's see if they enjoy their gilded cage?

As a reflective experiment this has some merit. Lots is made of the student experience, yet little is done to improve teaching and learning, with the lecture and essay still rock solid as core pedagogies. Placing a proxy learner in context, going through the motions is interesting. 

Careful what you wish for

They have to be careful what they wish for here. The AI will have a flawless digital memory, will not sleep, be super quick at tasks, never distracted, can multitask, network, never gets a hangover. Will is smash the exams? Will it have the urge to cheat?

The set up will be important. Will they be modeled on typical student behaviour, where 40% don't turn up to lectures?

The AI students are called Ann and Fry – odd as they say there’s no genders attributed? They are not robots but will listen through microphones, do assignment and eventually speak . It will have to go through the admissions process, then registration and make decisions on what classes it wants to take.

In HE, it would, I’d imagine, do a hatchet job on lectures (transience effect, cognitive overload, little interaction, poor slide design and poor teaching). It could compare live to recorded lecture experiences and, in its eyes, conclude that the convenience of recorded lectures wins hands down. It could critique the need for long absences during holiday breaks, the odd idea of having to wait for months even a full year to resit an exam. It could notice the number of students consistently absent from lectures.

To be fair, it is being built in partnership with the U.S. Department of Defense, National Security Agency, Department of Homeland Security and Amazon Web Services. That some pretty heavy fire right there.

The lack of research on using AI to teach and learn has been puzzling, compared to studies on productivity in the workplace. They've been noticeable by their absence. Yet we have truckloads of papers, frameworks and report on ethics and AI. I'd like to see two groups, randomised etc. one with, the other without AI, to measure impact on learning and performance. In may ways that would be more useful.

Of course, we all know what will actually happen… you don’t need millions to see the weaknesses already… but more power to their elbow. The US is taking the AI bull by the horns, while others write endless reports on ethics… it’s easy to be a critic much harder to do do real stuff and test against reality.

 

Sunday, January 07, 2024

Artificial Intelligence - calm down everyone - meaning is use


‘Artificial Intelligence’ was coined in 1956 by John McCarthy at Dartmouth College for the conference, held there, that kicked off the modern era in AI. Along with Marvin Minsky, Nathaniel Rochester, and Claude Shannon, he posited that AI was "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." Not a bad pop at definition and pretty open. I studied there and they had a little area dedicated to that conference, which has turned out to be much more significant than was thought. It was there that I got interested in tech in the very early 80s, was exposed to a mainframe computer, came back to the UK, built by first technology-based learning programme, to teach myself Russian, and the rest, for me is history.

McCarthy later regretted coining the phrase and preferred the words 'Computational Intelligence'. If we had listened, we’d all be using the letters CI! His problems with the terms Artificial and Intelligence were prescient, in that he saw it encouraging exaggeration, hype and over reach, also misunderstandings about what it actually was. He turned out to be right. The current doomer debate is the perfect example, where its meaning is hopelessly entangled with esoteric moralising. He preferred a phrase that focused on actual computing to do things we humans can do or find difficult.

There is the added problem that the word 'artificial' has perjorative tones, as in artificial grass or meat. The word 'intelligence' is also problematic invoking anthropomorphic and difficulties in definition, even outside of AI. I discuss these issues in detail here.

Four main meanings

In truth, four main meanings have emerged:

1. For many, when they use the phrase AI, they mean the post November 2022 Generative AI explosion around Chatbots, image generation and more recently multimodal AI. Even then, for some, it's ChatGPT3.5 and not 4 and subsequent versions - tools move fast here.

2. Others give it a wider meaning to include pre-November 22 achievements such as Deepmind’s Alphafold and AlphaGO then AlphaZero, AlphaStar, AlphaGeometry (though few know the latter two).

3. Other still, who knew about or were working with AI for years see it wider still, understanding that far from being one thing, AI is many things. This was well covered in Pedro Domingos’s book The Master Algorithm, with chapters on Symbolists, Connectivists, Evolutionists, Bayesians and Analogizers. 

4. Some also fix its meaning in the ethical debate and automatically think of this when the phrase is uttered.

From the ‘learning’ perspective, a word that was emphasised in the original 1956 definition, there’s machine learning (supervised and unsupervised), reinforcement learning, deep learning and all sorts of species and sub species. ‘Learning’ is a word you hear a lot in AI. 

Other ways of looking at AI is through areas of problem solving, such as NLP (Natural Language Programming), image recognition or robotics. There are many other ways still of slicing the AI cake. The important point is to see it as a set of very different technologies or tools that solve problems. Roger Schank, who was heavily involved in these debates, preferred to just call it software – he had a point!

Meaning is use

You can see the problem. There is no one 'meaning', as meaning is nuse As I explained in my book AI for Learning, Wittgenstein claimed the word ‘game’ is difficult to pin down, and is used in itself and with other with other words, to mean all sorts of things, from Olympic Games to board games, games people play, game theory, even simply throwing a ball against a wall. His theory that meaning is use and that meaning emerges from a set of ‘family resemblance’ is interest in AI, as that is exactly how LLMs work. Meaning emerges from its use within a Chatbot. His theory of language games is also useful in this context. Wittgenstein’s message was – beware of large words, especially in their reification.

It shouldn’t bother us too much, as meanings change with semantic drift, new facets of AI emerge and are folded in, or assumed in use. This is how language works. Meaning is not, as many pedants would like you to believe, strict fixed dictionary definitions. It is the other way round – dictionary definitions come from use and people use words in different ways within different contexts.

Funnily enough, LLMs, do exactly this. You can get them to play Wittgensteinian 'language games', by asking them different types of questions, getting them to play different roles and use different forms of expression.  I explore this here and in this podcast.

When one sees meaning as use, we can relax and understand that AI has and will continue to change. It has come to mean different things, as it conquers new challenges. It is, in a sense, what is will be.


PS another beef!

Whenever I hear the phrase ‘stochastic parrot’ I think – here goes, someone’s picked up on a meme to wave around without knowing where the phrase came from. I simply ask where the phrase comes from.

To be fair, those who have tread the paper from which it came, written well before ChatGPT3.5 was launched didn’t really know either. DSo you know the author of the paper or when it was written?)

Sure LLMs rely on statistical processes but they are in no way mere parrots and can exhibit complex language generation capabilities. They do not ‘parrot’ anything in the sense of copying or sampling. If you parrot someone or something, you literally copy or mimic them. LLM output does no such thing. It is all freshly minted.

Of course, it is always used pejoratively, with more than a shade of a sneer, as if it were mindless repetition, which it is not. This reductive approach strips all nuance away from what they do in terms of their utility, abilities to rewrite in different styles, summarise, critique, create and translate. 

The Bender paper is actually an attack on increasing the size of LLMs. It was written a two years before ChatGPT3.5 hit. So they got this completely wrong. Their recommendation for AI was to carefully curate datasets, not pursue Larger Language Models. How wrong they were.




Thursday, January 04, 2024

A Large Language Model (LLM) happened once before in history and it changed the world forever…

The Large Language Model GPT, from OpenAI, is one of the great wonders of the modern world. It is captivating, intriguing and above all useful. For the first time in the history of our species we have personal access, on a Global scale, to the sum of human culture. When the world speaks to a language model through ChatGPT, we speak to ourselves, the global mind. A LLM, like a brain and language, is unfathomable but dialogue gradually reveals its nature. Yet this is not the first time this has happened.

2300 years ago, in Alexandria, Ptolemy 1 decided, in this city at the crossroads of Europe, Asia and Africa, to do the same thing. He collected as much of the world’s literature as he could, paying for much of it, confiscating some, even stealing some, whatever it took to create the sum of known human knowledge, in many languages, from many lands “to collect, if possible, all the books in the world.” Reading Islam Issa's wonderful book, Alexandria, I was struck by the parallels.

Global dataset

Ptolemy 1 had Global ambitions and written knowledge was all within his reach, except for China, the only other place on earth where writing had been invented. He wrote to many other leaders and had no cultural bias – anything from any land in any language was welcome. It is thought that something in the order of 700,000 scrolls were assembled into this one, huge, data set.

In gathering content for the Library he used other people content from books, reached out, bought other libraries, copied every book they could find, begged borrowed and even stole. This is close to what has happened with the training data for LLMs, where a huge corpus of text, that would take 22,000 years to read, has been used to train the GPT model. GPT was trained on around 300 billion words. The average scroll in Classical Greece was around 10-15,000 words. If we take 700,000 scrolls at an average of 12,000 words each, the total number of words in the Library was around 8.4 billion words. That was impressive!

Like GPT, it was easily the largest dataset in the world, way bigger and therefore more useful than smaller libraries. They also had a technical advantage - the means of production and delivery – papyrus. Egypt owned the papyrus trade, limited supply to foreign buyers and therefore controlled the means of distribution, just like the data and compute clout of a Microsoft for ChatGPT. It even embargoed papyrus in 190 BC, intentionally restricting the growth of other libraries, like Pergamum. Scale mattered.

They even invented the idea of metadata for large datasets. First data preparation, translating everything into Greek, giving a single data standard. Seventy two Jewish scholars were employed and paid to translate the Bible. Then labelling each scroll with the author’s name and location. Further metadata was produced with categories such as doctors, historians, legislators, philosophers, rhetoricians, comic poets, epic poets and miscellaneous. They then went alphabetical. Finally, a complete catalogue. was produced. All of this increased the efficacy of research through more efficient search. They understood that the interface, ease of acccess to knowledge, mattered. This is what gave ChatGPT its status as the fastest adopted technology in the history of our species - ease of access.

The point was not to just collect all known papyrus scrolls, it was to learn from them and to globalise knowledge. The parallels between that ancient act and the current appearance of Generative AI has some fascinating parallels. Knowledge and access to that knowledge is power and this is a story of power. That power was instantiated when the library encouraged debate, discussion and outputs.

Access was the real key to success. Ptolemy allowed any scholar from anywhere to come and use the dataset, and they did. This is why this new AI tech is so exciting - anyone has access to it at little or no cost, from anywhere. Once we democratise intelligence, we democratise (to a degree) power. LLMs are currently affecting research and outputs in many different fields or sectors, just like the Library of Alexandria, which accelerated research, productivity and the creation of ideas for centuries to come.

Rapid achievements

Foundationally in mathematics, Euclid of Alexandria wrote his 14 volume Elements here, which included the first ever written algorithm,  a method to calculate the Greatest Common denominators for any given number. His theorems and, more importantly, proofs were deduced from axioms. Familiar examples include the proof that the angles of a triangle add up to180 degrees and Pythagoras’s Theorem. It is this logical rigour that is remarkable, influencing the entire history of mathematics and science. It was used as the main textbook in mathematics for over 2000 years, well into the 20th century and all University students for centuries used this book as part of the quadriviumf algorithms. Beyond this he wrote on the rigour of mathematical proof, conic sections, the geometry of spheres and number theory. In his Phaenomena, Euclid aims at astronomy with a treatment of spherical geometry.. Like LLMs, mathematics lay at the root of this project.

Conon of Samos developed conical mathematics. In astronomy they discovered the planet Mercury, compiled a catalogue of stars and developed a heliotropic view 1800 years before Copernicus. Map drawing and geography flourished with Claudius Ptolemy’s book Geography. He also saw mathematics, an Alexandrian obsession as being superior to the metaphysics of Plato and Aristotle. Eratosthenes calculated the earth’s orbit around the sun creating an accurate calendar, also realising that the earth was round he calculated, using rods, shadows at two locations, the circumference of the earth (he was accurate to within 50 miles). Foundation models are essentially language as maths with outputs as freshly minted language. Mathematics, once again, has proved its worth in the transmission and creation of knowledge.

Medicine also advanced with anatomy, dissection (even on live criminal patients!) and the pulse as a diagnostic sign. This is also happening with AI in healthcare, as new drugs are discovered, new materials and 200 million proteins, saving 1 billion years of research, were unlocked with AI.

In literature, there was the invention of the dictionary, and creative output in poetry, drama and music, sculpture and mosaic work, just as we are seeing, with the augmentation of art and ideas with LLMs.

It did not stop there, as astonishing feats of engineering also emerged from the work at the Library. Archimedes studied here and went back to Syracuse to build sophisticated war machines for the Romans. The mechanical astrolabe was invented, along with mechanical objects such as keyboard instruments, water clocks and automatons such as singing statues, chirping birds, dancing puppets,. There were self-trimming oil lamps, syringes, lab equipment for chemistry, a coin-operated vending machine for Holy Water, fire engines, even a steam engine! With AI we are also seeing the rise of speaking robots with Tesla’s self-driving cars and the astonishing Optimus robot.

Just like modern LLMs, deepfaking started almost immediately in Alexandria’s Library with scams and forgeries. What’s new? But this was a temporary problem and soon overcome. Alexandria grew rapidly as centre of mathematics, art and philosophy. It came to an end hundreds of years later, in the fourth century, when the mathematician Hypatia, who became a woman of mathematical renown and intellectual stature, was murdered by a Christian mob. The Classical world was nearing its end and monotheistic religion was starting to dominate leaders and the intellectual world. By the late 4th century AD, Christianity was banning books, all but scripture and the library was in decline largely through censorship and eventually the banning of non-Christian books as heretical. Yet its influence remains as a conduit for knowledge and creation and invention. There was no fire, it suffered a slow decline through intolerance and misguided moral certainty. There is, perhaps, another lesson to be learnt here - not to let moralisers destroy what is good on the back of their dogmatic belief that learning and innovation is bad.

Conclusion

Alexandria teaches us a lesson, that when we pool resources and create something unique, that benefits the whole of our species, wonderful things happen. It became the intellectual centre of the world for several centuries, one of the most important cities in the world, for the Ptolemies, Romans, Arabs, Ottomans, French and British. 

With GPT we have achieved something similar but this will not take centuries, even decades to prove its worth. It is already bearing similar fruit, wonderful things; real leaps in research, going multimodal, with dialogue, voice, images and video. Significant advanced in unlocking 200 million proteins, drug discovery and millions of new materials have already emerged, along with billions of uses a month.

We are tapping into the hive mind, just as the Alexandrians did over two thousand years ago to further improve the minds of all. We can do this if we focus on learning. We should not allow it to get crushed by the usual moralisers and religious inspired end-of days dogma but look for the bounty that it offers. We cannot say with certainty what will happen but we can be sure that it will be full of surprises and challenges. AI is the new Alexandria.

PS

This is linked to my idea for an AI University. The Library at Alexandria was the first University. Plato's Academy and Aristotle's Lyceum came earlier but they were really schools built around one man and his ideas. Alexandria was a different vision; cheap, open, secular and multicultural.

It had no faculty other than those interested in cataloging and keeping the system going. It had no formal teaching, just debate and discussion with further writing and practical invention. The idea of researchers as teachers came in hte 18th century with Humboldt.

It was also a powerful generator of ideas and inventions, not too abstract but as keen on the real world as ideas themselves. It was the retreat back into the scholastic world of theological beliefs that banned the books and put an end to the Library after 600 years. 

 

AI University and Research? Waves of innovation are hitting the research field

With all the talk in HE of AI and plagiarism, little attention has been given to teaching, learning or research. I have outlined an idea for an AI University, with a relentless focus on AI to optimise teaching and learning, to increase access and reduce costs. 

As a corollary, let us look at ‘research’. Can an AI University be a focus to optimise research? It has already happened over the last two decades. The real question is not whether AI can generate research, it is whether it can halt the paper mill it has become. It can, of course, increase productivity but its real role may be in streamlining and producing better research.

Future of research

Reports are tumbling out of institutions telling us that AI simply augments human effort. This is simplistic, so obviously defensive that it is in danger of turning into a pearl clutching meme.

In truth:

AI has been accelerating research for decades

AI will automate many research tasks

AI will eliminate some human research

AI will create new research opportunities

AI will produce big wins, leapfrogging existing research efforts

With cheap intelligence-on-demand, AI has already sped up the process of research across a broad front and promises a lot more.

Wave 1: Existing impact

We have already seen this happen in the first wave, with search, citation search and access to Journals and knowledge at our fingertips, in seconds. The impact of AI on ‘research’ may be much greater than it is on teaching and learning. It has gone under the radar but it the impact has already been astonishing. Google Search and Google Scholar accelerated research, as the whole business of access to Journals, knowledge and ready to use citations, slashed time to completion. I remember waking mile after mile along library shelves to find Journals and books, hour and hours labouring over desk research and citations (the bane of any researcher back then), even the typing of dissertations and theses with zero cut and paste! All have been accelerated or automated by AI. One could argue that every PhD period should have been slashed by several months!

Wave 2: Automating tasks

A second wave is now hitting research, as tasks are being augmented and automated, with a slew of research papers showing that this is not only possible by happening. 

Let’s look at tasks the literature has already identified as being possible, productively, by AI:

Titles

Abstracts

Introductions

Literature reviews

Research gaps

Hypotheses

Scoring/Open text surveys

Research plans

Data analysis  

New concepts 

Idea generation 

Full paper outlines 

Full papers

Faster peer review

Wave 3: Optimise research

A third wave may emerge where research is being eliminated on scale, especially text based speculation, meta studies and data-driven research. There is every reason to believe that AI will create new research opportunities and eliminate entire areas of current research endeavour. One would also hope that is speeds up peer critiques and reviews.

One would hope that, rather than making the current paper mill go faster, we can optimise research by eliminating bad or weak research. The system is in need of a rest on quality and quantity. Far too much research is being published and not used or even read. AI could help solve this problem.

Wave 4: Big wins

A fourth wave will leapfrog research to produce enormous wins. This has already happened and will accelerate. In some specific areas the impact of AI has leapfrogged the existing paradigm.

Alphafold: 1 billion years of research saved

MIT: Super-antibiotic Halicin discovered

Materials discovery of millions of new structures by Deepmind

Demis Hassabis says, one protein was taking 4-5 years to identify its 3D structure. We now know all 200 million, saving 1 billion YEARS of research. Read that again – it is an astonishing figure. This was only the start as we’ve also seen a similar breakthrough by Google on materials science, and a groundbreaking drug discovery by MIT. 

Do we think this will stop? I don’t think so.

AI University and research

One radical idea is to break the link between research and teaching, to recognise that this is part of the problem, the old Humboldtian idea that researchers make good teachers and that it is a necessary condition for success. I have argued that breaking this old Prussian bond could be a good thing, on the basis of research showing that researchers as teachers often hinder rather than help teaching in Universities.

What is needed is a University that grabs the bull by the horns and becomes a centre of research itself, or how research can be accelerated to release the bounty that AI offers or engages in research that uses AAI to accelerate progress. Research is, after all, a means to an end, not an end in itself. There is always a purpose or goal, even in blue sky physics, we have a general understanding that understanding the nature of reality has benefits for us as a species and often lays the ground work for new research.

There has to be a recognition that we are returning to a successful model that emerged in the Post-War period, with some notable examples before, (Bell, Eddison, Xerox, Tesla etc) , where large tech companies are doing their own research and leaping ahead of academia. This is not lone wolf activity, as they rely on academic output in terms of talent. However, their speed, ability to take risks, access to data, ability to deliver on scale and competitive mindset means that things happen quicker with more substantial breakthroughs.

Post-war success in the US in particular was engineered by Vannevar bush to co-ordinate academia, Government and business, paying to the strengths of all three. This is what ended the war with the Manhattan project and got them to the moon. All three need an element of humility to co-operate and make human advances to solve big ticket problems, such as climate change and the energy crisis.

It is no accident that almost all the breakthroughs are taking pace in the US, perhaps the Anglosphere of one includes Deepmind. There seems to be something in this idea that the optimal way forward is collaboration between state, academia and commerce, rather than anti-corporate, anti-government or anti-academic sentiment being allowed to hinder progress, as that is clearly sub-optimal.

Conclusion

There is another more serious problem here. Imagine a worldwide system, that trusts institutions to review and produce objective results, that gets billions in public funding, is found to be so corrupt that it has created incentives so bad that it is filling up with fake output, 10,000 fake entities have been found but we know this is the tip of the iceberg, with people buying and selling favours and a major country gaming the system by flooding it with false output. We have already moved towards this position.

Bibliography

Aydın Ö., Karaarslan E. OpenAI ChatGPT generated literature review: Digital twin in healthcare (2022)

Chen Y., Eger S. Generating (humourous) titles from scientific abstracts end-to-end (2022)

Wenzlaff K., Spaeth S. Smarter than humans? (2022)

Dowling M ChatGPT for (Finance) research: The Bananarama Conjecture (2023)

Zhai X. ChatGPT user experience: Implications for education (2022)

Adesso G. GPT4: The ultimate brain. Authorea Preprints (2022)

Valavanidis, A., SCIENTIFIC REVIEWS Artificial Intelligence Application withMachine-learning Algorithm Identified a Powerful Broad-Spectrum Antibiotic.

Merchant, A., Batzner, S., Schoenholz, S.S., Aykol, M., Cheon, G. and Cubuk, E.D., 2023. Scaling deep learning for materials discovery. Nature, pp.1-6.

 

Wednesday, January 03, 2024

Paul LeBlanc - talks the talk and has walked the walk!

In support of my idea for an AI University, I relied on a UK precedent - The Open University where the remarkable Harold Wilson, and indomitable Jennie Lee, created a lasting institution that changed the face of UK tertiary Education. But there is another precedent that demands attention - Paul LeBlanc's achievement at SNHU.

He was the president of Southern New Hampshire University. Before joining SNHU, LeBlanc held various positions in academia, including serving as the President of Marlboro College in Vermont and has had a decades long interest in technologies for learning. My idea of the AI University was in part inspired by his success. he doesn't just talk the talk, at SNHU, ha walked the walk, and led the incredible growth on online learning at SNHU to make it  the largest University in the US.

His first book Students first; Equity, Access, and Opportunity in Higher Education (2021) takes a holistic look at reimagining Higher Education, covering everything from our ideas of the learner through to financial structures. It is a considered critique of the existing system, which, as the title suggests, is clearly not student-centred.

Critiques are easy, change is difficult and this is where LeBlanc has real credibility.  Once we see a system as that prioritises flexibility, accessibility and student success we can design innovative solutions. The current system is inflexible, has deeply embedded old practices and is far too expensive. It is also too remote from the real world. Education should be for life and living.

Given his background, which he explains in detail, he supports what he gained from, a student-first perspective, where institutions are deeply committed to student success, which only comes from understanding each student's unique background, challenges and goals.

Only then does he focus on delivering on this promise through competency-based education, online learning and flexible pathways that acknowledge students needs and real lives. A critical aspect of his model is to lower costs to address systemic inequities in higher education. The curriculum must deal with real-world skills and the needs of the modern labour market, preparing students not just for degrees but for meaningful careers. He is right here. The current system pretends that it is all about the intellectual journey, while making tons of money from future Dentists, Doctors, lawyers, Engineers, teachers and Nurses. In an age of critical skills shortages, we need at least some institutions to tackle this global issue.

He is also clear on the demands on leadership to drive change, the need of a culture of innovation and the need to be political savvy. In this sense the book is a call to action. Put the student first, and he means the many not the selected few, and all follows. 

In Broken (2022) he tackles the wider problems of existing approaches to problems in education, health, and other social systems. In all of this he abhors systems that result in failure, debt, emotional trauma and inequalities.

SNHU

LeBlanc admits that his route into online learning was part accident, as SNHU had satellite sites on Navy bases. The Navy has lots of learners at sea, so started delivering distance learning courses in 1995. As SNHU was serving a non-traditional student audience he also puts some of the early success down to this. There was little money and as a small campus, status was not a huge problem. It is both money and status, he thinks, that holds the system back.

LeBlanc transformed SNHU from 2500 students in 2003 to over 200,000 students in 20 years by using technology to switch delivery online. This was an early, significant and relentless investment in pursuit of a more scalable model using online education.

He learnt a lot from The University of Phoenix experiment and had a clear focus on treating applicants well, making the process quicker and easier. A fundamental aim was to keep tuition costs low and provide support for students from diverse backgrounds. LeBlanc has been a vocal advocate for making higher education more accessible and affordable. He continues to focus on innovating and adapting to the changing educational needs of students globally, a proponent of competency-based education and other models that prioritize learning outcomes over traditional measures.

SNHU is now one of the largest non-profit providers of online higher education in the country, offering more than 200 accredited undergraduate, graduate, and certificate programs.

AI

Having retired from SNHU, LeBlanc's is now expanding on his work to transform lives by taking on the challenges and opportunities that AI offers. This technology, he thinks, could help us rethink and reimagine education. Freeing ourselves from the current constraints of the existing models we could shift towards a more efficient, human and student-centred model, through AI.

He is working with George Siemens on a learning platform, alongside a solution to the data problem. The problem, they think, is the lack of any sophisticated approach to data, so they are hoping to build a build a global data consortium, with a focus on safeguards on student privacy and solutions to algorithmic bias. 

His new focus on AI centres around building a platform. This is worthy but not easy, and the world is littered with failures, as ideas are easy, implementation hard. I’m an admirer but disagree with his view of data as providing insights into learning. This is a long shot, as we have plenty of insights to inform practice through existing research and the data sets, as some have seen, have failed to provide much in the way of meaningful predictive insights. Their focus on bias is also, I think, peripheral if they want to do things that are bigger in education in relation to AI.

Having built one of the largest online HE institutions in the world he has helped advance the cause of online education and Degrees. Many institutions now consider this a key strategic goal, having seen it work at SNHU, Western Governors and elsewhere. He continues to inform and influence the sector through his work on using AI to accelerate progress in the sector.

Bibliography

LeBlanc, P., 2022. Broken: How our social systems are failing us and how we can fix them. BenBella Books.

LeBlanc, P., 2021. Students First: Equity, Access, and Opportunity in Higher Education. Harvard Education Press.