watching the context on bbc _ watching the context on bbc news. - _ watching the context on bbc news. asl _ watching the i context on bbc news. as we _ watching the - context on bbc news. as we near the us presidential election, there are concerns ai technology is being used to make the voting process confusing. that's coming up on al decoded. but before that ai decoded. but before that it's time for sport let's get a full round—up. a very busy night of european football. the one that probably catches the eye isjose mourinho's fenerbace against his old club manchester united. the latest score there is 1—0. christian eriksen with the opening goal of the game. united are under pressure afterfailing to wind either of their games in the europa league so far this season. about half an hour gone in the first half. you can keep up to date

with that and all thursday's games on the bbc sport website and app. england will look for quick wickets against pakistan when play resumes in the third and final test in rawalpindi on friday. 13 wickets fell on the first day with the home side's spinners doing all the damage as they bowled england out for 267. sajid khan's six wickets was the stand out bowling. it could have been a lot worse without a half century from opener ben duckett and 89 from jamie smith that included 6 sixes in a century partnership with gus atkinson. pakistan closed on 73—3 so that england score doesn't seem so bad. i think it was a decent score especially from the position we were in. anytime you're first you'll need the best conditions... it'll be tough would be looking to take as

many wickets as possible. that first innings is crucial. and it was spin that did the damage for india on day one of the second test against new zealand in pune. ravi ashwin took the first three, then washington sundar took over. devon conway top scored and rachin ravindra also made a half century but when he went it sparked a collapse. the tourists lost seven wickets for 62, all to sundar, and were bowled out for 259. india will resume batting on 16 for one, 215 runs behind. alex de minaur is into the quarter—finals of the vienna open after his second—round opponent, flavio cobolli, retired with a right shoulder injury in the second set. de minaur will meet czech playerjakub mensik next. and grigor dimotrov was surprisingly beaten by another czech player tomas machac. it's a significant blow to the bulgarian�*s hopes of qualifying for the end of year atp finals. dimitorv won the first set on a tie break but then lost the next two. machac will next face

the seventh seeded jack draper in the quarter—finals. draper beat luciano darderi in straight sets. he raced past italian world no 42 luciano darderi 7—5 6—1 in 76 minutes. draper is trying to wind a second atp title this year. world number 9 daria kasatkina is through the quarter—finals at the pan pacific open in tokyo. she won the ningbo open last weekend and came from a set down to overcome the usa's mccartney kessler in three sets. she will now face sofia kenin in the quarterfinals. katie boulter is also through, the british number one beat the japanese lucky loser kyoka okamura in straight sets. she will now face canadian bianca andreescu in her first quarter—final on tour since eastbourne in june. and olympic champion zheng qinwen will face the usa's leylah fernandez

in the last eight. the top seeded chinese player beat local favourite moyuka uchijima 7—5, 6—0. fernando alonso has pulled out of his media commitments on the eve of friday practice for the mexico city grand prix after feeling unwell. are he met his fans on arrival in the country's capital, you are a you but later his aston martin team said he wouldn't attend the circuit on thursday in order to recover enough are to take part in second practice on friday. he's set to become the first driver to take part a in 400 f1 race weekends. and that's all the sport for now. you are watching the context. it's time for a weekly segment ai decoded. welcome to ai decoded, that time of the week

when we look in depth at some of the most eye catching stories in the world of artificial intellligence. .. with the us presidential election less than two weeks away there are concerns ai technology is being used to make the voting process confusing. white house national security adviserjake sullivan speaking at an event on al earlier, said the us was making progress in identifying foreign interference in its elections but in his words, there's a long way to go to get where we need to be. so while false information aimed at disrupting elections is nothing new, increasingly advanced a. i. tools could make it easier to deceive voters with video and audio that looks and sounds plausible. which brings us to this story from nbc. a new public service campaign featuring hollywood stars has been created to alert americans on how not to be duped by ai generated deepfakes in the run up to election day. we'll be speaking to miles taylor, one of the organisers of the campaign. meanwhile the futurism website says the pentagon is planning to use deepfake technology to their own advantage by creating ai personas

to infiltrate online chat forums in order to gather information. this comes despite the us government's persistent warnings that deepfakes and other ai generated content will deepen the misinformation crisis and lead to a muddier information ecosystem for everyone. which brings us to this article warning a donald trump wind in november could potentially help unleash dangerous artificial intellligence. wired magazine says the former president's opposition to what he called woke safety standards for al would likely mean the dismantling of regulations. and one of those very worried over the implications of all this is microsoft's bill gates who ranks the threat posed by artificial intelligence alongside nuclear war and bio—terrorism. the multi billionaire says he finds ai both wondrous and a little bit scary, and that it's very possible malign actors will use ai in ways that could prove dangerous for humanity.

with me now is our regular ai commentator and presenter stephanie hare and joining us once again is susie alegre, author of the book human rights, robot wrongs and joining us down the line from california we have miles taylor, ai commentator and former us government official in both the bush and trump administrations. stephanie, so we are less than fortnight away from the us presidential election and polls are telling us the race is way too close to call, how does this provide you opportunities for the mis use of artificial intelligence? areal are a ijust come back from a weekend _ are a ijust come back from a weekend in _ are a ijust come back from a weekend in chicago - are a ijust come back from a weekend in chicago and - are a ijust come back from a weekend in chicago and i - weekend in chicago and i definitely felt it was very tight in the midwest of the united states. what i was picking up when i was there it was this discussion about misinformation and disinformation and people not knowing where to go to get

trustworthy facts. this brings us into this whole question of if people are going to use artificial intelligence to deceive, what are we creating in terms of a trustworthy electoral process? it’s in terms of a trustworthy electoral process?- in terms of a trustworthy electoral process? it's a big question — electoral process? it's a big question indeed. _ electoral process? it's a big question indeed. let's - electoral process? it's a big question indeed. let's look| electoral process? it's a big l question indeed. let's look at the public service campaign warning americans not to be duped ahead of election day. i'm definitely real. a fake message saying voting has been extended... gr

a fake message saying voting has been extended. . .- has been extended... or you need new — has been extended... or you need new documentation - has been extended... or you i need new documentation these are all scams — need new documentation these are all scams designed - need new documentation these are all scams designed to - need new documentation these are all scams designed to trick| are all scams designed to trick you into not voting. d0 are all scams designed to trick you into not voting.— you into not voting. do not fall for it. _ you into not voting. do not fall for it. if— you into not voting. do not fall for it. if something - you into not voting. do not. fall for it. if something seems off, it probably _ fall for it. if something seems off, it probably is. _ fall for it. if something seems off, it probably is. voting - fall for it. if something seems off, it probably is. voting is i off, it probably is. voting is our off, it probably is. voting is your right- _ off, it probably is. voting is your right. don't _ off, it probably is. voting is your right. don't let - off, it probably is. voting is your right. don't let anyone take it from you. so sorry, i'm not even american.

so sorry, i'm not even american-— so sorry, i'm not even american. let's now speak to miles taylor, who's an author, commentator on al and former us government official in the bush and trump administrations.. miles was one of the organisers of the public service campaign we just watched, let's now bring him in. is this to get people to not take everything at face value? yes this is for what people saw with_ yes this is for what people saw with spam _ yes this is for what people saw with spam in the 1990s. when we first got— with spam in the 1990s. when we first got an — with spam in the 1990s. when we first got an e—mail account we trusted — first got an e—mail account we trusted everything that came in because — trusted everything that came in because no one you didn't trust had your— because no one you didn't trust had your e—mail address, until they— had your e—mail address, until they did — had your e—mail address, until they did. and then you got that message — they did. and then you got that message from the drudgery and prints _ message from the drudgery and prints trying to scam you and people — prints trying to scam you and people started to get duped, and fulled. this is the next generation of spam and its highly—

generation of spam and its highly sophisticated and we're going — highly sophisticated and we're going to — highly sophisticated and we're going to see deep fakes in a lot of— going to see deep fakes in a lot of parts of our lives. on the — lot of parts of our lives. on the tip _ lot of parts of our lives. on the tip of— lot of parts of our lives. on the tip of the iceberg is in our— the tip of the iceberg is in our elections. we wanted to start— our elections. we wanted to start getting the public eye clematis to the fact that things— clematis to the fact that things which might seem innocuous about when, where and how to _ innocuous about when, where and how to vote — innocuous about when, where and how to vote potentially could be deep _ how to vote potentially could be deep fake tricks trying to jeep — be deep fake tricks trying to jeep them. this is something that— jeep them. this is something that law— jeep them. this is something that law enforcement is tracked concerned about in the us. they are worried _ concerned about in the us. they are worried that people are trying — are worried that people are trying to— are worried that people are trying to suppress the vote to change — trying to suppress the vote to change the outcome. the goal here _ change the outcome. the goal here is— change the outcome. the goal here is to _ change the outcome. the goal here is to try to get folks to try to — here is to try to get folks to try to show some critical thinking _ try to show some critical thinking and check their sources. thinking and check their sources— thinking and check their sources. ., ., ., , , thinking and check their sources. ., ., ., , sources. the spam analogy is a aood sources. the spam analogy is a good one- _ sources. the spam analogy is a good one. how _ sources. the spam analogy is a good one. how bigger- sources. the spam analogy is a good one. how bigger threat i sources. the spam analogy is a good one. how bigger threat is| good one. how bigger threat is this to democracy. especially where the polls are really

tight with small voters could really make a difference and make the poll go one way or another?— make the poll go one way or another? ~ ., ., ., , another? we saw parliamentary rotarians around _ another? we saw parliamentary rotarians around the _ another? we saw parliamentary rotarians around the world - rotarians around the world concerned _ rotarians around the world concerned about _ rotarians around the world concerned about how - rotarians around the world i concerned about how artificial intelligence _ concerned about how artificial intelligence could _ concerned about how artificial intelligence could influence . intelligence could influence elections _ intelligence could influence elections. it— intelligence could influence elections. it is— intelligence could influence elections. it is very - intelligence could influence elections. it is very often i elections. it is very often about— elections. it is very often about voter— elections. it is very often about voter suppression, elections. it is very often - about voter suppression, it's not about— about voter suppression, it's not about changing - about voter suppression, it's not about changing ideas - about voter suppression, it's not about changing ideas it i about voter suppression, it'sl not about changing ideas it is about— not about changing ideas it is about stopping _ not about changing ideas it is about stopping you _ not about changing ideas it is about stopping you from - not about changing ideas it is i about stopping you from getting off the _ about stopping you from getting off the couch _ about stopping you from getting off the couch and _ about stopping you from getting off the couch and going - about stopping you from getting off the couch and going to - off the couch and going to vote _ off the couch and going to vote it's _ off the couch and going to vote. it's something - off the couch and going to vote. it's something thatl vote. it's something that requires _ vote. it's something that requires tough _ vote. it's something that i requires tough enforcement vote. it's something that - requires tough enforcement of electoral— requires tough enforcement of electoral law— requires tough enforcement of electoral law to— requires tough enforcement of electoral law to enforce - requires tough enforcement of electoral law to enforce these | electoral law to enforce these issues — electoral law to enforce these issues al _ electoral law to enforce these issues. aland _ electoral law to enforce these issues. aland deep _ electoral law to enforce these issues. aland deep fakes- electoral law to enforce these issues. aland deep fakes are | issues. aland deep fakes are out there _ issues. aland deep fakes are out there and _ issues. aland deep fakes are out there and what _ issues. aland deep fakes are out there and what we - issues. aland deep fakes are out there and what we need i issues. aland deep fakes are | out there and what we need is to make — out there and what we need is to make sure _ out there and what we need is to make sure the _ out there and what we need is to make sure the people - to make sure the people involved _ to make sure the people involved in— to make sure the people involved in elections - to make sure the people involved in elections and to make sure the people - involved in elections and bad actors — involved in elections and bad actors have _ involved in elections and bad actors have consequences. involved in elections and bad i actors have consequences when they stretch _ actors have consequences when they stretch the _ actors have consequences when they stretch the law. _ actors have consequences when they stretch the law. this - they stretch the law. this brin . s they stretch the law. this brings us _ they stretch the law. this brings us to _ they stretch the law. this brings us to the - they stretch the law. this brings us to the point - they stretch the law- brings us to the point about accountability. susie is

absolutely spot—on that the last two elections that we've seenin last two elections that we've seen in the united states were using technology to change how people might vote and there was a big debate about whether that was a factor or not. now, we're seeing that america is so polarised and people are entrenched and tribalist and it's going to come down to the swing states. the real trick is suppressing the vote and making it so that people don't sign up on time, don't know where to 90, on time, don't know where to go, so the question that i would have four miles is are we going to see any action in the united states where that people who are interfering are criminalised?— who are interfering are criminalised? it's a great question- _ criminalised? it's a great question. there - criminalised? it's a great question. there were - criminalised? it's a great question. there were a l criminalised? it's a great - question. there were a number of bills— question. there were a number of bills introduced to deter bad — of bills introduced to deter bad actors and i don't think anyone _ bad actors and i don't think anyone will be surprise to hear me say— anyone will be surprise to hear me say that they did not pass in the — me say that they did not pass in the congress. i was meeting today— in the congress. i was meeting today with fbi officials and

they— today with fbi officials and they will say that existing laws _ they will say that existing laws on _ they will say that existing laws on the books allow them to id laws on the books allow them to go prosecute voter suppression because _ go prosecute voter suppression because that is illegal, but there — because that is illegal, but there is— because that is illegal, but there is an additional complication with deep fakes. those — complication with deep fakes. those law authorities need to be able — those law authorities need to be able to detect that something is fake in the first place — something is fake in the first place and a lot of these agencies and local officials do not have _ agencies and local officials do not have deep fake detection tools — not have deep fake detection tools so _ not have deep fake detection tools. so if there are phone calls — tools. so if there are phone calls to _ tools. so if there are phone calls to voters telling that a polling _ calls to voters telling that a polling location has closed or changed, there aren't systems in place — changed, there aren't systems in place in _ changed, there aren't systems in place in real—time to detect that— in place in real—time to detect that those might be deep fake phone — that those might be deep fake phone calls and fraudulent actors _ phone calls and fraudulent actors. that's not gonna be sorted _ actors. that's not gonna be sorted in _ actors. that's not gonna be sorted in time for this election. certainly, if something goes wrong the cycle folks _ something goes wrong the cycle folks are — something goes wrong the cycle folks are going to wake up and say that— folks are going to wake up and say that we need those real—time detection tools to protect— real—time detection tools to protect our networks our democracy. protect our networks our democracy-— protect our networks our democracy. protect our networks our democra . ,, , ., democracy. this brings us onto the next story _ democracy. this brings us onto the next story that _ democracy. this brings us onto the next story that the - the next story that the pentagon is going to use

generative ai pentagon is going to use generative al to create fake ai people, i suppose it online people, i suppose it online people can go into chat forums to get information, even as the government warns that the ai can be used by nefarious actors. , , , . , actors. yes it is effectively what is good _ actors. yes it is effectively what is good for _ actors. yes it is effectively what is good for the - actors. yes it is effectively what is good for the goose actors. yes it is effectively i what is good for the goose is good — what is good for the goose is good for— what is good for the goose is good for the _ what is good for the goose is good for the gander- what is good for the goose is good for the gander so - what is good for the goose is good for the gander so if- good for the gander so if peopte _ good for the gander so if peopte are _ good for the gander so if people are going - good for the gander so if people are going to - good for the gander so if people are going to be i good for the gander so if- people are going to be using them — people are going to be using them for— people are going to be using them for the _ people are going to be using them for the various - people are going to be using them for the various but - people are going to be using them for the various but —— i them for the various but —— nefarious— them for the various but —— nefarious purposes - them for the various but —— nefarious purposes then . them for the various but ——| nefarious purposes then the government— nefarious purposes then the government may— nefarious purposes then the government may to. - nefarious purposes then the government may to. i- nefarious purposes then the government may to. i wonder if --eole government may to. i wonder if people are _ government may to. i wonder if people are going _ government may to. i wonder if people are going to _ government may to. i wonder if people are going to question i people are going to question whether we use social media at all. if you know you're in a

conversation with the robot, or a spy' conversation with the robot, or a spy, or marketing,... the whole point was that you would have real connection, i don't know what you would go to it it is just a sewer. i’m know what you would go to it it isjust a sewer.— isjust a sewer. i'm here more and more _ isjust a sewer. i'm here more and more people _ isjust a sewer. i'm here more and more people complaining| and more people complaining about the content that what they see on their platforms... i think whizzing across society a lot — i think whizzing across society a lot of— i think whizzing across society a lot of these platforms... whizzing _ a lot of these platforms... whizzing partisan one of these 'ust whizzing partisan one of these just like — whizzing partisan one of these just like it was bad for business for google in that era when — business for google in that era when we — business for google in that era when we were getting a lot of spam — when we were getting a lot of spam e—mails and that company

invested — spam e—mails and that company invested a — spam e—mails and that company invested a lot in spam detection and most gets filtered out, we likely to go into— filtered out, we likely to go into an _ filtered out, we likely to go into an era where most of the interactions online are going to be — interactions online are going to be not— interactions online are going to be not real and before tech companies are going to develop the technology to weed them out — the technology to weed them out i'm _ the technology to weed them out. i'm not privy to the plans on fake — out. i'm not privy to the plans on fake personas, but i suspect folks— on fake personas, but i suspect folks who — on fake personas, but i suspect folks who work in al on fake personas, but i suspect folks who work in ai will say that — folks who work in ai will say that it — folks who work in ai will say that it is _ folks who work in ai will say that it is a _ folks who work in ai will say that it is a double—edged problem. we don't want to see this happen but because it is, if we're — this happen but because it is, if we're going to engage our adversaries, coaches that want to do— adversaries, coaches that want to do the — adversaries, coaches that want to do the west harm, we're going — to do the west harm, we're going to _ to do the west harm, we're going to have to play that game and were — going to have to play that game and were going to have to deptov_ and were going to have to deploy bots to engage with their— deploy bots to engage with their bots. it does become a grey— their bots. it does become a grey area _ their bots. it does become a grey area-— their bots. it does become a re area. ., , ., grey area. coming up after the break. what could a second

donald trump presidential term mean for al development and regulation? we'll get a unique insight from miles taylor who previously served in his administration and should we all be worried like bill gates who's concerned artificial intelligence could get out of control? we'll discuss all that after the break.

welcome back to ai decoded, the global battle to regulate artificial intelligence has been raging ever since deep concerns were raised over the unpredicatability of this super intelligent technology. the european union were first to legislate with the european artificial intelligence act which came into force last august. however the move has been criticised by tech giant meta who have warned that the eu's approach to regulating ai is creating a risk the continent could be cut off from accessing cutting edge services. other initiatives in the us have found themselves facing headwinds from every direction, so what is the future for controlling this technology that will transform all our lives?

welcome back to our regular ai commentator and presenter stephanie hare and susie alegre, author of the book, human rights, robot wrongs and joining us down the line from california we have miles taylor, ai commentator and former us government official in both the bush and trump administrations. miles, you previously worked as a senior us government official in the trump administration.. miles, you had an insight into donald trump when you worked for him during his presidency, how do you think he will approach ai regulation? donald trump does not operate with a public policy scalp, he operates with a wrecking ball. there may need to be

adjustments to ai regulations, personally i believe that absolutely needs to be. we're eartv — absolutely needs to be. we're early in — absolutely needs to be. we're early in the age of ai and we haven't— early in the age of ai and we haven't got it right. but if you — haven't got it right. but if you take _ haven't got it right. but if you take a wrecking ball to the issue _ you take a wrecking ball to the issue that _ you take a wrecking ball to the issue that you can cause a lot more — issue that you can cause a lot more damage than good. right now on— more damage than good. right now on this question the issue of bias— now on this question the issue of bias is— now on this question the issue of bias is what it key here. all al _ of bias is what it key here. all ai systems are trained on the world _ all ai systems are trained on the world we live in. things that— the world we live in. things that we _ the world we live in. things that we say, they get reflected in these — that we say, they get reflected in these models. what a lot of companies are trying to do is take — companies are trying to do is take those dangerous or frustrating things out. discussions about self—harm, discussions about self— harm, racism, — discussions about self—harm, racism, and try to remove those biases— racism, and try to remove those biases from _ racism, and try to remove those biases from the models. doing that there are secondary consequences. when you put your finger— consequences. when you put your finger on— consequences. when you put your finger on the scale you create

hallucinations. some of these platforms trying to control for these — platforms trying to control for these problems and creating new problems. if you these problems and creating new