Top 5 Jobs In AI and Key Skills Needed To Help You Land One
You will not struggle to answer, but AI would. So, the first important point to remember is that humans are intelligent in many ways. AI and Human Intelligence HI are not the same and the differences are extremely important, it is true that we have built our AI systems to be intelligent in the way that we perceive value in our human intelligence. I remember in the early days of studying AI, the first grandmaster level Chess-playing Computer had been built and had beaten world champion Garry Kasparov. This seemed an amazing feat and there were people who thought that having cracked chess, which could be described as the pinnacle of human intelligence, intelligent people play chess after all.
It was thought that we had cracked intelligence. And then people realised that the abilities that we take for granted, such as the ability to see, are far harder to achieve than is playing chess.
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Decades later, we have managed to build AI systems that can see, to an extent, but they still have their limitations. What we need if we are to progress and grow our human intelligence, is to make sure that we recognise the need for humans to complement AI, not to mimic and repeat what the AI can do faster and more consistently than we can.
And so, what are the implications: the potential and the reality of AI within education? I believe that it is useful to think about this question from three perspectives:. It is important to recognise that these three elements are not mutually exclusive. In fact, they are far from being mutually exclusive. They are interrelated in important ways. Let us start with using AI in education to tackle some of the big educational challenges.
Challenges such as the achievement gaps we see between those who achieve well educationally and those who do not.
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And challenges, such as those posed by learners with special and particular learning needs. If we start by looking at the reality of the systems that are available here and now, to help us tackle some of these challenges, then we will see the beginnings of the potential for the future. He can explain to you far better than I can exactly what is happening when it comes to data, AI and computing power in education.
Well, you heard it there from Lewis: Data has been a game changer when it comes to educational AI. If we take the London-based Century Tech, they have developed a machine learning platform that can personalise learning to the needs of individual students across curriculum areas to help them achieve their best. Their machine learning is informed by what we understand from neuroscience about the way the human brain learns. A further reality is that, in addition to being able to build intelligent platforms, such a Century, we can build intelligent tutors that can provide individual instruction to students in a specific subject area.
These systems are extremely successful, not as successful as a human teacher who is teaching another human on a one-to-one basis, but the AI can, when well-designed, be as effective as a teacher teaching a whole class of students.
In addition to intelligent platforms and intelligent tutoring systems, there are many intelligent recommendation systems that can help teachers to identify the best resources for their students to use, and that help learners identify exactly what materials are most suitable for them at any particular moment in time. It is not just by learning particular areas of the curriculum that AI can make a big difference. AI can also help us to build our cognitive fitness, so that we have good executive functioning capabilities, so that we can pay attention when needed, remember what we learn and focus on what needs to be done.
This system called MyCognition, for example, enables each person who uses it to complete a personal assessment of their cognitive fitness and then train themselves using a game called Aquasnap. AI helps Aquasnap to individualise training according to the needs of the particular person who is playing. Finally, just in case you thought the reality of AI was only for adults, think again. But what about the potential for the future? And data can also be the power behind human intelligence.
We can collect data in many, many ways, from our interactions with our smartphone to wearable technologies that track our heart rate, temperature, pulse, the speed of our movement and the length of our stillness.
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We can collect data about our interactactions with technologies in traditional ways, we can collect data passively through cameras that observe what is happening, we can collect data from technologies that are embedded in the clothes that we wear. There are, of course, many important ethical implications associated with collecting data on this scale and these need to be addressed, but the scale of data collection is already happening and it is important to think about how this data could power education systems, not just systems designed to influence our spending or voting habits.
If we accept the premise that data is the new oil and we are willing to invest the time in cleaning the data, then the final ingredient that we need to add, if we are to meet the potential that AI can bring to teaching and learning, is that we must design the AI algorithms that we use to process the data in a way that is informed by what we understand from research in the learning sciences, such as psychology, neuroscience, education. If we get this right then we can turn the sea of data that can be generated as people interact in the world into, an intelligence infrastructure that can power all of the educational interactions of an individual.
This intelligence infrastructure can empower what we do with our smartphones, laptops, desktops, robotic interfaces, virtual and augmented reality interfaces and of course when we sit alone reading and working through books or when we interact with another person as part of our learning process. This intelligence infrastructure can tell us about how we are learning, about the process of learning, about where we are struggling and where we are excelling, based on extremely detailed data and smart algorithmic processing informed by what we understand about how people learn.
This intelligence infrastructure can also be used to power technologies to support people with disabilities and in so doing to help improve equality and social justice.
We will be able to build intelligent exoskeletons, we can build intelligent glasses that can help the blind to see, and we will be able to tap in to what processing is happening in the brain allowing people to think what they want to happen on the computer screen and see it happen. But we need to remember as I highlighted earlier, that there are ethical Implications here.
The potential for good is great, but unfortunately so is the potential for bad. So, what about the second implication of AI education? This implication is about educating people about artificial intelligence, so that they can use it safely and effectively.
What Skills Do I Need to Get a Job in Artificial Intelligence?
This tree diagram summarises the three key areas that I believe we need to educate people about when it comes to AI. We need everyone to have a basic understanding of AI, so that they have the skills and the abilities to work and live in an AI enhanced world. This is not coding, this is understanding why data is important to AI and what AI can and cannot achieve. We also need everyone to understand the basics of ethics, but we need a small percentage of the population to understand a great deal more about this so that they can take responsibility for the regulatory frameworks that will be necessary to try and ensure that ethical AI is what we build and use.
Again, a small percentage of the population will need this kind of expert subject knowledge. I would like to dwell for a moment on the ethical aspect of that tree diagram. There are many organisations exploring ethics and AI, or ethics and data. I find it useful when thinking about ethics to break down the problem into different elements. Firstly, there is the data that powers AI. Here, we need to ask questions such as: who decided that this data should be collected?
Has that decision been driven by sound ethical judgement? Who knows that this data is being collected and who has given informed consent for this data to be collected and used? What is the purpose of this data collection, is it ethical.? What is the justification for collecting this data, is it sound? We must always remember that we can say no. Next, we need to consider the processing that happens when the machine learning AI algorithms get to work.
Have these algorithms been designed in a way that has been informed by a sound understanding of how humans learn? Have the AI algorithms been trained on datasets that are biased, or are they representative of the population for whom processing is being done? And finally, there is the output — the results of the processing we have done through our AI algorithms. Is the output suitable to the audience? Is it genuine or is it fake? Are we collecting more data about their reactions to this output? We simply cannot keep up with those who want to do harm through the use of AI.
We must therefore ensure that everyone is educated enough to keep themselves safe. Finally, we come to the third category of implications from AI and education: changing education so that we can focus on human intelligence and prepare people for an AI world. Many people, including the World Education Forum are telling us that we are now entering the Fourth Industrial Revolution — the time when many factors across the globe, including the way that AI is powering workplace automation, are changing the workplace and our lives for ever.
There is much media attention to this Fourth Industrial Revolution with some coverage making such positive predictions as these from Australia that suggest that we will have two hours more time each week, because some of the more tedious aspects of our jobs will be automated. Our workplaces will be safer, and jobs will be more satisfying as we learn more.
Not everyone is as optimistic and there are an increasing number of reports that consider the consequences for jobs of the increased automation taking place in the workplace.
Knowledge-Based AI: Cognitive Systems
We can see from this graph from the report that transportation and storage appear to be the areas of the economy where most job losses will occur. Education will be the least prone to automation. We could interpret that as meaning that education will not change. However, I believe that education will change dramatically.
It will change as we use more AI and it will change as what we need to teach changes in order to ensure that our students can prosper in an AI augmented world. And if we look at the second chart, it is perfectly clear that the impact will not be felt by everyone equally. Of course, those with higher education levels will be least vulnerable when it comes to automation and job loss. We therefore need to provide particular support for those with lower levels of education.
Personally, I do not think all these reports are that useful, interesting as they are. As a race, we humans are rather poor at prediction and the differences of opinion across the different reports indicates the complexity of predicting anything in such fast-changing circumstances. In these circumstances, a map about the road ahead has limited use.
What we really need is to know that we have a car that is well-equipped, we have brakes that work, lights that work. A huge truck thundering towards us, for example. This is a subject that I have studied and written about quite a lot and a subject that is covered in this book: Machine Learning and Human intelligence: the future of education 21 st century.