simple but as i said if they are following the structures the following the process is of the governing party which i have some similarities with the governing african national congress here then it wouldn't really i think be south africa's place and they have certainly been very hesitant today to make any comment on the matter we've spoken to them a couple of times we suppose spoke into the communications of said the south african government said we're going to wait till tomorrow before we say anything when that suppose a deadline will either pass with his resignation or as we're schumer now not and sadek is well they said look we're not going to say anything until tuesday when they're holding an emergency meeting so both big regional powers sort of really taking a back seat and letting the process play out however zimbabweans decide it must. thank you to a new page in years the latest reaction to what has just happened in harare we were expecting the president president robert mugabe to announce his resignation instead

he is saying that he will be presiding over the zanu p.f. congress in a few weeks time in december in harare he wants to be that he wants to be the person in charge of the congress over the moments no resignation he is still the president that is it from me now the next stop is talk to al-jazeera. you can. see. we live in an age of rapid technological advances where also official intelligence

is a reality not a science fiction fantasy every day we rely on algorithms to communicate to all find king book a holiday even introduce us to potential partners driverless cars and robots may be the headline make his but they are being used for everything from diagnosing illnesses to helping police predict crime hotspots algorithms also make important decisions affecting whole lives all be eligible for a credit card which applicant should get the job. critics say the more our personal data is gathered the grace of a threat to our privacy as machines become more advanced how does society keep pace when deciding yet excess and regulations governing technology we find out is steven roberts machine learning professor at the university of oxford talks to al-jazeera . professor stephen roberts thank you for talking to al-jazeera about a fascinating but often confusing subject so perhaps you could start by explaining

exactly what these algorithms artificial intelligence and machine learning actually are and how they're involved already in our everyday lives i think most people have heard of algorithms and many will know something about them these are much more traditional ways of solving a problem with a computer and the algorithm is basically a sequence of mathematical steps which are performed inside a piece of software in principle they could be done with a pencil and paper by you or i but of course for big problems it's much easier to let a computer do them we critically we designed the steps in a traditional solution in an algorithm so we tell the algorithm what the first step in the train is and then it moves on and performs the next operation of the next and so on and at the end the answer is obtained and these are found in everything from our mobile phones to engine management units in our cars to intelligent

toasters and microwave ovens and so on but we wouldn't really regard those years as a i in a more modern sense the very big difference that i am machine learning bring is we don't tell the algorithm the sequence of steps that it should take ahead of time in order to solve a problem we merely give it a lot of data to try things out on and we tell the algorithm very clearly what success or failure looks like and what it would do is it will try various strategies to succeed. those strategies that it finds lead more likely than not to success it will reinforce inside its memory and those strategies which lead to failure it will begin to inhibit and after enough iterations all runs through this procedure the algorithm itself the ai system has learned the likely ways in which you can construct a solution which gives rise to a high chance of a successful outcome and that really is

a learning process the only thing we need to do is tell it the rules of the game what success looks like and provide it with an environment in which it can. iterate and work out these strategies typically by providing it with a lot of data ok well let's deal with the dystopian fear that some people have that's artificial intelligence is going to lead to these robots these machines that can take over the world they can replace human beings is that conceivably remote sleep possible. it sounds much more like science fiction and we certainly nowhere near that particular point where there are going to be swarms of sort of armies of robots that are taking over the world i think we have to remember that automation and autonomy are something that is very deeply embedded within our world already whether it's from algorithms that are trading on global financial markets to smart algorithms that are scanning a miles checking for viruses and working out what

a response might be that we would make predictive text on our mobile phones is another such intelligent algorithm so i think these kind of things with very familiar with and we're not really afraid of the i would say the fear on a much more rational level is that like all automation it would displace us as people from the jobs that we have so you can imagine a what we really are in the midst of a second industrial revolution a technological revolution. and whereby traditional jobs whereby people use their intelligence in order to sort things work through things whether it's accounting working in factories making things robots or ready taking those jobs because they don't tire they're cheaper long term to work with and so on the advances though they've been quite rapid i mean i would say what in the last ten years there's been a tremendous push towards artificial intelligence towards machine learning what

have been the bigger the advances you think that have been happening what's changed dramatically in the last ten years the last five years especially is two things firstly we just have computers that are capable of so much more we have machines that we can go out and by ourselves which have supercomputer like performance from twenty years ago and that's pretty amazing in itself and also the amount of data that is available out there publicly or commercially available on the world wide web is absolutely phenomenal the scale of data is almost beyond belief and you couple those two things together you have the methods that can make smart algorithms that can learn from the world around them to solve a task you have data to feed their investigations as to how to solve that task and you have computers to help you do that in

a reasonable time so you don't have to wait a year for an answer you can now get the answer within an hour or even minutes in some cases so who do you think is actually behind this push to make such big technological advances obviously scientists and people like you with want to innovate but is it also down to business is it down to investors money politicians is almost like a space race it feels to me very much so i mean you're absolutely right previously there have been big methodological advances in machine learning and i. i mean that tends to been done within the academic sector people working on the theory developing efficient algorithms to implement that theory and showcasing how they might work in principle at industrial and commercial scale with the rise of data and the rise of computation it means that industry can take these ideas build upon them and use them to commercial advantage and of course that becomes then

there is enormous business case money gets reinvested back in to make these methods even better make them work on bigger bigger datasets extract more knowledge make the business even more profitable and i think you can see the enormous rise of. in some of the event tech companies to date who are using machine learning as really one of the central pillars of the business all we as a society they equipped to be able to keep up with the pace of these developments i think as consumers we enjoy the benefits of those advances but as a society we are definitely not going behind simple situations where you can think about crowded streets in a city and sat in traffic jams everybody driving there they come a dirty car we love the idea of self driving cars and electric vehicles that smartly charge themselves up autonomously and so on but in order to do that we as

a society need to deal with ethical legal frameworks i was talking to a lawyer quite recently who was worrying about whether you could actually take legal action against an algorithm. it becomes almost bizarre philosophical commentry but as a society we need to address these kind of questions head on if a piece of machinery goes wrong is there a chain of litigation through which we can. bring the legal system to bear should we worry about that even if the systems are performing better than human beings what happens if a robot surgeon gets something wrong who is to blame the hospital the designers of the robot the people who created the algorithm remember the algorithm has developed itself learning from data and that data might be a very public open repository with thousands or millions of stakeholders so it's not clear where the blame or the audit trail of accountability

lies i think as a society this takes us into very new territory we're used to people being the people who are responsible and when algorithms are now responsible it's very difficult to to talk and negotiate with an algorithm for a particular task i mean just on an everyday basis for example when you go to get a mortgage or a new bank account we all know that if the computer says no because you can't take the right boxes you're not going to get all that credit card is that what you're talking about that ability to be able to absolutely yes a lot of machines there is a very very much so and in fact there is an enormous push now amongst the academic community as well as the very big commercial players to try and have some kind of accountability an audit trail of reasoning built into the way these out these machines are operating this is absolutely necessary because we have an algorithm that drives a car or makes a decision about whether you get a credit card or

a mortgage and you are denied or something goes wrong you need to know on human understandable terms what that reason was big problem is if you force the computer to only use a trial of reasoning which is understandable to us as human beings guess what the performance of that algorithm drops and often drops below human performance so if you want to algorithms that do better or as good. as human beings we need to have algorithms which are not understandable necessarily by us is that why so many people are worried about what they might term as biased algorithms the way that an algorithm is used by people what is in proceeds by a person might be biased what is it put it by another person might be biased and i'm trying to come to something that is completely neutral i mean that's very difficult to do isn't it i very much agree and i think much of the bias goes back

to the data which the algorithms often the fed if we take a there are often classic examples if we take say the word scientist then we go on to the web and we search for pictures of scientists because of cultural bias depending on what culture we come from most the pictures we see will of be of date white men. the world is much bigger than white men and and yet an algorithm doesn't necessarily have the sensitivity to understand that it is looking at a very biased collection of pictures and so will believe what it's given it doesn't have a kind of high ethical. process and so that bias will tend to be amplified in the results it performs so when you next asked the small town of them find me a picture of a scientist it would tend to favor in favor of the statistics of the data it's been given if those statistics are skewed or biased it will amplify that and

we are going to have work very hard in order to produce unbiased algorithms which take these sensitivities into account fortunately many of the big commercial players and a lot of academic work is going into trying to do this so teams that places like facebook and google and so on are working very hard to try and remove these sort of almost cultural biases from the data the algorithms are using in the first place so how does all that feed into the criticism that some people have of the way out. over them is an artificial intelligence all use for example within the police the police allocate certain resources to certain areas because they're told by computer that area is deprived there is more likelihood of crime taking place there the criticism is that that reinforces stereotypes in that area. absolutely in principle of course what people are trying to do is is use very effectively a finite resource if we turned that problem slightly differently and said we had a finite amount of money and were allowed to spend it on buildings to make those

buildings safe for earthquakes we'd preferentially spend that money in regions where statistically earthquakes are much more likely to occur and i don't think anybody would question that that line of reasoning because the moment you start bringing that to putting more police in areas with higher crime rates you're actually beginning to have a much more complex story and that's because it involves human beings where algorithms are good at is dealing with data but that data doesn't have. an emotional context behind it or sensitivities and the depths of understanding that we need to go to in order to algorithms to understand the emotional impact and the social impact on us as human beings it's just not there yet so i think i am machine learning is extraordinarily good at analyzing data working out very smart things about how you would best use

a finite budget in some our own work with for example try to work out how given a certain budget you can have maximum impact to prevent malaria again these kind of more health implications i don't think anybody would be too concerned but the moment you begin to start looking at things to do with policing and crime and so on there is always a danger what you again doing is potentially reinforcing social stereotypes the problem isn't necessarily with the machine learning it's been done rather clumsily and perhaps the worst you can say is naively the problems are actually deeper and they are societal problems which i'm afraid even i can't solve them. on an individual basis of course many people concerned about privacy and the security of their data always getting the balance rice all mass do you think people are quite right to be worried about bruce in security there is a lot of data out there in every interaction that we have in

a world where there are now more internet connected things than the arse human beings most of the time we leave a trail of data throughout every day of our lives and that data can and often is collected often it will help prevent us against identity theft and fraud but sometimes it can be used against us either maliciously or surreptitiously for marketing or advertising purposes and so on. i think in some areas we need to have ideas a little bit like opt in or informed consent so in many countries there will be organ donation so upon your death you give your organs to medical science to try and help others to live i think there are many causes for our data medical data to be under a similar kind of framework so we can offer the data we generate as human beings with all the the bumps and bashes and illnesses that we have possibly even sections

of our genetic code to try and help medical science to improve intervention tests and medicines which are of global benefits to all. is ten years since the beginning of the first rule smartphone when we think of these the the i phone when steve jobs went on stage in two thousand and seven introduced the world to the i phone of course the smart phone i don't think any of us would want to be without but it is a device that collects data most of the big international players are actually extremely good when it. the use of that data and they but they will use it for their own commercial gains between advertising recommending friends that we might get in touch with or some or some such a game we wouldn't think of that is terribly impactful but we lose track i work in this field and i i couldn't tell you where who and how my data is being

used and that's that's slightly frightening i've got to say the words to of course fake news to what extent is algorithms machine learning how are they involved in the spread of fake news which is you know come to dominate the headlines over the past couple of years and there's been a lot of people saying well this is all down to computers managing to spread the misinformation who is to blame for that. ultimately people want to spread fake news for particular purposes whether that's too for advertising or whether it's to try and destabilize nations. is almost the same computers than i are used in two areas the first area comes back to the question you posed earlier which is if we only had a certain amount of money to spend where and how would we spend it most wisely to

get biggest effect we can use algorithms to say if i'm going to spread fake news what outfits be they media or social media or wherever should i put that fake news so that i believe it will spread like wildfire and we can use algorithms to tell us how we're going to do that you can imagine i could create fake news but if i if i put it up even publicly in the wrong place hardly anybody of figures to see it it's a bit like a contagious disease it needs a critical mass of interaction and we can use algorithms i say we not me but people can use algorithms to seed fake news in the right places at the right time for maximum effect the second thing is the fact that algorithms are used to spread information around the world and they do that without correcting it they don't know the difference that precedence between true news and fake news they simply propagated on and that means that if if somebody wants to spread fake news part of

the question they ask themselves is going to be where can i explore it the loopholes in the existing algorithms are the social media companies then. doing enough to combat that because they've had they've come under a lot of criticism i see they've got people working on that rather rapidly right now they certainly do the very big social media companies have actually very large teams the teams of people and they are teams of small town themselves well people are working very hard to create algorithms that try and weed. out what is fake from what isn't and it's a bit like. fraud prevention. i've been traveling recently and i know it's annoying i want to pay for something with a credit card and it was denied because an algorithm thought what's this person doing in paris they're supposed to be in the u.k. in oxford or london or whatever it's annoying at the time but that actually is kind of to benefit me similar kinds of algorithms are in deep being used but the point

i'm trying to make is even if we get those algorithms to work most of the time there will always be cases where they charge totally genuine news which is shocking outrageous maybe rather different from the normal news they often will judge that to be fake as well so we call have a filter that accurately works one hundred percent of the time but we can do something which will stop the escalation or at least stop the acceleration of fake news but part of the education process that i know to social media companies are doing is to educate us and uses one thing i've not mentioned is that almost all of the algorithms that are out there are what we would term human in the loop they are ways of distilling the the enormous ocean of data in the world to make it focused for what me as an individual might be interested in and they

will filter that that data. they're they work in a wonderful way because i haven't got the time to look at this ocean of data and i like to have it distilled to something bite sized but i as a human being need to be educated that puts me at risk of those algorithms me believing the kind of stories that they might choose when you look ahead to say the next twenty years how do you see the future of machine learning of artificial intelligence what do you may stick sighted about the areas that i work in are twofold i work in artificial intelligence machine learning in commerce and industry so if we got a big practical problems the other half that i work on are what we think of as pure science problems not only in the life sciences but but in the physical sciences as well and there we will use our machine learning to sift through a universe of data which is so big that decades of human scientists all their lives

they could spend looking at this data and they'd still not spot interesting things in it and yet computers can do this and help us these are areas where i augment saccade ability of doing science it spots things like a normal is it lets us detect weird and wonderful things like gravitational waves neutron stars colliding with each other it tells us about amazing incredible things in the universe around us but it also helps us understand the complexity of biology it helps us discover innovative new treatments and innovative new drugs that offer a ray of hope in the in the most awful diseases so these are the the wonderful benefits of being able to look at the oceans of data and find interesting a normal those things which could be a crack of hope in an otherwise closed door so that scientists can focus their efforts on those and work to produce things of global societal benefit. that i

think is incredible very much signed up to that. the more dystopian side i hope never comes to pass by i think the really soon as i mentioned earlier is that algorithms will increasingly have human like ability in such a wide range of areas that they will begin to replace arses human beings and displace us from the job market so this is where we're going i like to think that we are at least partly in control of it yes there will be problems yes it would be perfect and there is much legislature compliance and policy that we need to get ahead and governments and policy makers are very active in this space as all the big multinationals so i personally believe the future is utopian it's very bright this is an incredible tool that we have available and if we use it wisely it can help a lot of the secrets of the universe to enormous success and provide

wonderful advances in medical science and the way we try and tackle some of the very big global challenges what's the not to like do every year and for a time that was simpler easier that wasn't dominated by machines wasn't dominated by phones by computers personally very much so i think there was a great pleasure in getting up close with things and i think it was keats who said it was it was taking part in the flow of of reality. it is very easy if i've got a something broken at home it would be very easy for me to try and fix it by downloading the parts from the web and using a three d. printer to make it whether it's a little plastic cold or something there is a great pleasure to spend hours more filing it carefully out of wood or plastic or metal and creating it as a human being i think that that intimacy with the material universe is

a great pleasure in. it's a pleasure in small things that i personally enjoy it's not for everybody but i hope that that never goes away. thank you very much but.

documentaries that open your eyes at this time. oh is it when they're on line we were in hurricane. almost like thirty six hours these are the things that new u.k. has to address or if you join us on set. but. it. is a dialogue tweet us with hash tag a.j. stream and one of your pitches might make the next show join the global conversation at this time on al-jazeera. on the top story. he's ruled zimbabwe force.