india's hospitals are reporting dangerously low oxygen levels and no empty beds as coronavirus cases reach record highs. there have been over 2,200 deaths in the past 2a hours. the prime minister narendra modi says the government is trying to source additional supplies of oxygen. there are fears oxygen supplies have run out on an indonesian submarine that went missing off the coast of bali on wednesday. search teams from a number of countries are trying to find the vessel, which has a crew of 50 three on board. the former chief advisor to borisjohnson has accused the uk prime minister of falling below the standards of competence and integrity the country deserves. dominic cummings claimed mrjohnson tried to stop an inquiry into leaks in case it implicated a friend of his fiancee, carrie symonds. downing street denies the allegations.

most of us are in for some glorious spring weather this weekend, so you may be thinking of venturing into the great outdoors for a hike or a ramble. if so, you're being asked to stick to proper footpaths. adam mcclean reports from scafell pike. this path is one of hundreds across the lake district's fells — routes that help hikers reach the top of england's highest peaks. we're always repairing the paths in the lake district, and we're repairing them for the benefit of the environment, so we're stopping the damage to the vegetation and the wildlife here. and that's our main reason for the work that we're doing, to protect this wonderful scenery and this precious environment. this route�*s one of the most popular in the lake district, from those completing the three peaks challenge to others ticking off the highest point in england. i think the pandemic has made people realise how important the countryside is for their physical and mental well—being and health. and so, they're getting out into the countryside more and that's absolutely great to see but, of course, it has an impact, and what's happening is this mountain

is literally being worn away. repairing the paths needs heavy machinery — a challenge for the team fixing the fells. we realised that access for the machine to do the machine restoration was particularly tricky — well, impossible, to be honest — and that the only way to get the machine up would be to take it apart, bring it here, fly it up to the summit and then rebuild it! the work will repair five damaged sections of the path, with equipment flown up by helicopter. 0k, terry, ready to lift. and lifting, terry. after a short four—minute flight, the equipment arrives here, on the path to the top of scafell pike. piece by piece, there'll soon be a digger near the top of england's highest mountain. certainly, around these hills,

wind can play a big part of the aviation turbulence. wind shear, downdrafts — we've always got to be aware of that. this helicopter is capable of lifting 1.2 tonnes, and that's right at its maximum weight. so if we have any outside influences which may affect our performance, we've got to be extremely careful. and certainly, when we're working around people on the ground as well. and for those enjoying a walk, the hard work didn't go unnoticed. i think the work to maintain and restore the paths is fantastic because it makes it much more accessible for people to get up the mountain. and it's a beautiful view. you can see the isle of man, you can see all around. i've made good use of the panoramic feature on my phone's camera. itjust makes you feel so much safer going up this sort - of path, especially compared to, like, the other other-

—— other mountainl paths i've been on, is you feel looked after. high visitor numbers and extreme weather are eroding england's highest mountains. it's hoped this work will help preserve them for the future. adam mcclean, bbc news. it is coming up to 25 minutes to two. now on bbc news, click. this week — they are biased comp they discriminate. they are racist. they are the algorithms. welcome to click! we're going to start with a quiz this week!

we're going to play a game of guess the famous face! using possibly the frankest faces that you have seen in awhile. they are pretty aren't they, lara? —— freaky test. they are pretty aren't they, lara? -- freaky test.- they are pretty aren't they, lara? -- freaky test. the end result is _ lara? -- freaky test. the end result is not _ lara? -- freaky test. the end result is not quite _ lara? -- freaky test. the end result is not quite as - lara? -- freaky test. the end result is not quite as scary . lara? -- freaky test. the end result is not quite as scary as| result is not quite as scary as the result of making them. {lila the result of making them. ok, so this may _ the result of making them. ok, so this may very _ the result of making them. ok, so this may very well— the result of making them. ok, so this may very well be something that you cannot see but here we go!— but here we go! 0k. who is this? and _ but here we go! 0k. who is this? and who _ but here we go! 0k. who is this? and who is _ but here we go! 0k. who is this? and who is this? - but here we go! 0k. who is this? and who is this? and| but here we go! 0k. who is - this? and who is this? and who is this? ~ , ., is this? well, while you ponder. _ is this? well, while you ponder, let _ is this? well, while you ponder, let me - is this? well, while you ponder, let me tell - is this? well, while you ponder, let me tell you| is this? well, while you - ponder, let me tell you that this is what happens when you ask an ai this is what happens when you ask an alto this is what happens when you ask an al to generate fake faces based on other faces. ask an al to generate fake faces based on otherfaces. are you ready? here come the answers. the first one is a blend of lara and me. so answers. the first one is a blend of lara and me. i blend of lara and me. so odd! i think it is _ blend of lara and me. so odd! i think it is more _ blend of lara and me. so odd! i think it is more you _ blend of lara and me. so odd! i think it is more you than - blend of lara and me. so odd! i think it is more you than me, . think it is more you than me, to be honest. i think it is more you than me, to be honest.— to be honest. i think it is more due _ to be honest. i think it is more due than _ to be honest. i think it is

more due than me! - to be honest. i think it is. more due than me! really? to be honest. i think it is- more due than me! really? i don't know _ more due than me! really? i don't know. the _ more due than me! really? i don't know. the next - more due than me! really? i don't know. the next one - more due than me! really? i don't know. the next one is. don't know. the next one is chris fox and omar mehtab. and this is oz and kitty.— this is oz and kitty. goodness, i think this is oz and kitty. goodness, i think the _ this is oz and kitty. goodness, i think the really _ this is oz and kitty. goodness, i think the really odd _ this is oz and kitty. goodness, i think the really odd bit - this is oz and kitty. goodness, i think the really odd bit is - i think the really odd bit is seeing the progression from one person into another. this seeing the progression from one person into another.— person into another. this is a really weird, _ person into another. this is a really weird, fun _ person into another. this is a really weird, fun thing - person into another. this is a really weird, fun thing that i really weird, fun thing that has come out of the really serious issue that we are going to be talking about for the rest of the programme, and that is the fact that computers have got much, much better at recognising faces but not all faces. especially faces that are not white. 2020 highlighted many inequalities in how we treat each other as humans. inequalities in who can afford to shelter from the virus and who had no choice but to physically go to work. and inequalities in how we are treated by the authorities. the killing of george floyd, the protests that followed and this week's conviction of direction

open have reminded us all that racism still exists in our societies and we all need work together to truly root it out —— derek chauvin. and it is against this backdrop that we are going to be looking at biases in technology. an industry that has often been criticised for coding our prejudices into its products. and systems recognising faces definitely fall into this category. these technologies are now being used in many applications and it's well documented that they don't work as well for everyone. so i spoke to somebody who fell foul of one of these system is a couple of years ago when she was trying to get a new passport. was trying to get a new passport-— was trying to get a new passport. was trying to get a new ”assort. ., .,, , passport. kat was using the home office's _ passport. kat was using the home office's new - passport. kat was using the home office's new tool - passport. kat was using the home office's new tool to l passport. kat was using the - home office's new tool to check and verify the photo was up to the job. and verify the photo was up to thejob. i and verify the photo was up to the 'ob. ., . ., the job. i noticed that multiple _ the job. i noticed that multiple attempts - the job. i noticed that multiple attempts i i the job. i noticed that i multiple attempts i have the job. i noticed that - multiple attempts i have tried and it could not recognise my features on my face and the first thing was my eyes, it was

looking for my mouth and it could not see the outlines of my mouth so i pulled back my hair to see if i could assist the camera to take the photograph of me properly. cat, who works as a technologist, you that what she experienced was an ongoing issue and tweeted about it. inaudible showed the problem there, detecting that with especially ethnic minorities. two years on, ethnic minorities still face similar issues. the home office says in the vast majority of cases where a photo does not pass the automated check, customers can override the outcome and submit the photo is part of their application. how does this make you feel, as

somebody with dark skin, where you possibly don't feel like this has been designed properly to cater for you?— to cater for you? there is a broad spectrum _ to cater for you? there is a broad spectrum of- to cater for you? there is a broad spectrum of people i to cater for you? there is a i broad spectrum of people with various skin tones, raising from very light individuals who are from an ethnic minority background to people who are very, very dark skinned and that needs to be taken into consideration. it is actually about individuals or software companies that are putting out software that is not ready for the mass market.— software that is not ready for the mass market. that was cat hallam. facial— the mass market. that was cat hallam. facial recognition - the mass market. that was cat hallam. facial recognition is i hallam. facial recognition is powered by artificial intelligence, a technology possibly the technology which is going to impact our lives more than any other in the next decade. it will underpin the self—driving cars that need to recognise pedestrians, the decisions that officers will take when policing our streets, the selection ofjobs that we may or may not be offered and,

very probably, the next vaccine we develop too. 50 very probably, the next vaccine we develop too.— we develop too. so it needs to net we develop too. so it needs to get things _ we develop too. so it needs to get things right. _ we develop too. so it needs to get things right, right? - we develop too. so it needs to get things right, right? well. get things right, right? well craig langham from the bbc radio programme people fixing the world has been looking at how we can create facial recognition systems that work for everyone. recognition systems that work for everyone-— for everyone. facial recognition - for everyone. facial recognition is - for everyone. facial| recognition is slowly for everyone. facial- recognition is slowly seeping into everything we do. while it can be a convenient way of interacting, for some it has been creating problems. you know those passport gates at the airport? for me, they often don't work as well as they should. sometimes it can take me several goes before i finally get through but sometimes they don't even seem to work at all. it's a little bit annoying and i'm never quite sure what is going on or why. but at least the issue at

the gates is not affecting my livelihood. i travelled to meet this man who wanted to remain anonymous. he lost his job overnight. anonymous. he lost his 'ob overnightd anonymous. he lost his 'ob overniaht. , ., . , overnight. uber introduces fake -- face recognition _ overnight. uber introduces fake -- face recognition technology. j —— face recognition technology. they sent messages out, saying that occasionally we are going to ask you to take a salty of yourself and they compare against the picture you have got on their so it is quite dark and i had a hat on and i tooka dark and i had a hat on and i took a selvey and i got an e—mail about i! took a selvey and i got an e—mail about 11 o'clock saying your account has been deactivated due to not being recognised —— selfie. we have chosen to end a partnership with you. i hope you understand this. i was shocked at the time, i did not know what to do. .. time, i did not know what to do, ,,., ., , , . time, i did not know what to do. , . , ., , do. sahir has since sent dozens of messages — do. sahir has since sent dozens of messages to _ do. sahir has since sent dozens of messages to uber _ do. sahir has since sent dozens of messages to uber eats - do. sahir has since sent dozens of messages to uber eats and l do. sahir has since sent dozens. of messages to uber eats and he has a similar generic response each time. he asked for his case to be reviewed and whether it would be acceptable if the mcdonald's manager where he usually picks his deliveries up from could vouch for him. he got nowhere. all of these messages were sent by you? yes.

and that is the first _ messages were sent by you? jazz and that is the first message got back? and that is the first message not back? , , got back? yes, but as you can see it as generic message. . got back? yes, but as you can i see it as generic message. uber sa s it see it as generic message. uber says it believes _ see it as generic message. uber says it believes the _ see it as generic message. uber says it believes the picture - says it believes the picture provided to its system: an assertion sahir denies. when he pleaded with the company to be able to appeal the decision, and asked for a review of his file, bubertold him and asked for a review of his file, buber told him the decision was final. five months on, sahir is still waiting for a response to his letter that he sent to holland where the appeals team is based on since we have taken on his case, they have agreed to share the image captured on the night of the incident. 22 captured on the night of the incident. ., ., , , incident. 22 of our members have been — incident. 22 of our members have been dismissed - incident. 22 of our members have been dismissed by - incident. 22 of our members| have been dismissed by uber eats for substitution, and that is through our facial recognition software system, but its use and of those, 12 were be ame and four were from

a brazilian — portuguese heritage. a brazilian - portuguese heritage-— a brazilian - portuguese heritaue. . , . ., heritage. have since spoken to another union who have said their members too have faced issues. 50 their members too have faced issues. , , , issues. so basically, they launched _ issues. so basically, they launched a _ issues. so basically, they launched a new _ issues. so basically, they launched a new system i issues. so basically, they| launched a new system to issues. so basically, they - launched a new system to check the real—time id, they have blocked my account on the time, it's very bad for me because i have no other social —— source of income. in have no other social -- source of income-— have no other social -- source of income. in a statement uber said: these stories show us just how crucial it is to get this right. these systems have got to be robust and they've got to work for everyone. a key part of the problem is often within the data sets these algorithms

have been trained on. they are often built from images straight from the web, images of celebrities in the media. or pictures on social networks. even our predicting a wedding dress will show the western christian wedding is the prediction for that because it's very heavily influenced by western media so as a result of that of course western media, it is not very diverse and it does not feature people of colour in a lot of tv shows. 50 colour in a lot of tv shows. so now some _ colour in a lot of tv shows. so now some companies are hoping that al itself could just be the answer to solving the problem. using the might of something called generative adversarial network or gans, for short. to see how it worked, we have embarked on an experiment. these are the faces of the click team which we have fed directly into an off the she” fed directly into an off the shelf gans software from nvidia. on the right is the image of the person the

software already knows and we are on the left. the algorithm starts by comparing the facial features it knows to the new image that it is looking at. after hundreds of iterations, it is able to work out what makes that face look the way it does. at least mathematically, anyway. once we have this digital replica, we can start playing around with different features. we can fiddle with age, ethnicity and mixed faces, but most importantly, create people that don't exist at all. this technology is used to create a large databases of fake faces, which is then used to train facial recognition systems. but creating faces in this way is not enough. if you want to create something that works and treats everyone more equally, the real images you feed into the gans have to be representative of life. after all, we would not be looking straight into a camera in the real world. this is a photo

shoot by generated photos in the us, a company specialising in creating gan images. it's been several months taking pictures of thousands of people. these models were specifically chosen for their diversity, but they are also being captured doing all sorts of things. so do you think that gans can totally eliminate bias in an area like facial recognition? totally is probably a very strong term but i think they can mitigate significantly, yes, i think so. i would say that if you do collect more real data, if you are able to

do that then you should do that. , . , ., that. this technique should optimise — that. this technique should optimise how _ that. this technique should optimise how these - that. this technique should l optimise how these systems work, but what is more important are the people behind the code. the reality is that technology always reflects the biases that exist in society. and just changing facial recognition tool to make them more accurate is not going to change that. how did it feel backin change that. how did it feel back in october when you realise that you would lose your livelihood? it realise that you would lose your livelihood?— your livelihood? it was horrible. _ your livelihood? it was horrible. i— your livelihood? it was horrible. i can't - your livelihood? it was horrible. i can't even l your livelihood? it was - horrible. i can't even explain it, sleepless nights, things were going on in my head. what am i going to do now, how am i going to survive? stories like sahir's show _ going to survive? stories like sahir's show us _ going to survive? stories like sahir's show usjust - going to survive? stories like sahir's show usjust how- sahir's show us just how important it is to have people on the other side that you can easily talk to and reason with. ethical debates around how these technologies are used and deployed need to continue, and life impacting decisions shouldn't be left to machines alone. hello and welcome to the week in tech. it was the week

apple unveiled all new ipads and a spectrum of imacs, both built around its newest mi chip. air tag item trackers also launched at the spring—loaded event. and also came confirmation of a ios update that requires users to opt into ad tracking. with the default set to off this could hit revenue for companies like facebook. instagram announced it would let users filter out abusive messages. the move follows footballers speaking out about experiencing harassment through direct messages. a total of 2019 model s crashed and killed two people with police believing there was nobody in the driver seat. anyone musk tweeted that data recovered so far showed the car's autopilot driver assistance system was not enabled. nasserflew assistance system was not enabled. nasser flew a assistance system was not

enabled. nasserflew a mini helicopter on mars in the first powered controlled flight of its kind. the autonomous drone hovered for nearly a0 seconds. and how do you follow a helicopter on mars? with a dna robot shaped like an aeroplane. where previously it took days to design these tiny devices, new software has helped scientists develop minuscule structures complete with rotors and hinges injust a minute. looks like that tech is taking off. and i, a woman, face my face... earlier in the programme we saw how algorithmic bias is our impacting people's lives in a very real way. joy is an ai researcher at mit and she spent the last four years trying to raise awareness of the social implications and harms of ai. as i make whimsical symptoms to paint walls with our smiles or

project inspirations on faces, but at times i am invisible. in 2016joy founded the 2016 joy founded the algorithmicjustice leg which algorithmic justice leg which was algorithmicjustice leg which was inspired by her own experiences of facial recognition and she is now the star of brand—new netflix documentary called coated bias. it is notjust face classification, it is any data centric technology. —— coded bias. centric technology. -- coded bias. ., , bias. one of the first questions _ bias. one of the first questions we - bias. one of the first questions we should | bias. one of the first i questions we should be bias. one of the first - questions we should be asking is is _ questions we should be asking is is the — is is the technology necessary in the first place, are there alternatives? and after we ask that, _ alternatives? and after we ask that, if— alternatives? and after we ask that, if the benefits outweigh the harms, we also need to do something that i consider algorithmic hygiene, and with algorithmic hygiene, and with algorithmic hygiene, and with algorithmic hygiene, you are actually— algorithmic hygiene, you are actually checking, who do the systems — actually checking, who do the systems work for and who doesn't _ systems work for and who doesn't it work for? there is actually— doesn't it work for? there is actually continuous oversight for how—

actually continuous oversight for how they are used because you would _ you would not just floss once or take a _ you would not just floss once or take a shower once and think you are — or take a shower once and think you are ok— or take a shower once and think you are ok when these symptoms are in_ you are ok when these symptoms are in the — you are ok when these symptoms are in the real world, we have two _ are in the real world, we have two see — are in the real world, we have two see how they are actually being — two see how they are actually being used. so two see how they are actually being used-— two see how they are actually being used. so what about the solution we — being used. so what about the solution we talked _ being used. so what about the solution we talked about - solution we talked about earlier, making up for the lack of ethnically diverse training data by generating fake faces of ai data by generating fake faces of al to learn from?— data by generating fake faces of al to learn from? when we are thinking _ of al to learn from? when we are thinking about _ of al to learn from? when we are thinking about how- of al to learn from? when we i are thinking about how systems are thinking about how systems are being trained, and how systems are being evaluated, do the evaluation methods actually reflect the real world conditions? because if they don't, we can give ourselves a false sense of progress, which in the research i have done has been very characteristic of the field, we take a limited data set, we make major claims about that limited data set, that often does not reflect real—world conditions. [30 often does not reflect real-world conditions. do you know whether _ real-world conditions. do you know whether there - real-world conditions. do you know whether there are, - real-world conditions. do you know whether there are, or i real-world conditions. do you i know whether there are, or used to be any genuine technical reasons why cameras couldn't

pick up the details in dark skin as well as they could in light skin?— skin as well as they could in liuht skin? . .,, ., ., light skin? cameras are often looked at _ light skin? cameras are often looked at as _ light skin? cameras are often looked at as being _ light skin? cameras are often looked at as being objective, | looked at as being objective, but default settings generally reflect the priorities of the people who are creating a specific technology. if we look in the analogue space, we saw that with kodak for example, they actually changed the chemical composition of their film when chocolate companies and furniture companies complained that you can't see the difference between my dark chocolate and my milk chocolate, or the fine grain in the mahogany. and so where this is fascinating to me is, what happens in the analogue, right, is then replicated in the digital space. that is not to say we don't have differences in skin reflect deafness and so forth, it is to say we can choose to develop systems that account for those differences, or not. —— differences. —— skin

reflectiveness. do you think| these technology companies reflectiveness. do you think- these technology companies are serious about combating racial bias, or are theyjust paying lip service? bias, or are they 'ust paying up serviceah bias, or are they 'ust paying up sewiceah lip service? they are saying they want — lip service? they are saying they want to _ lip service? they are saying they want to have _ lip service? they are saying they want to have equality l lip service? they are saying l they want to have equality in al, they can be ai, they can be well—intentioned but we have to look at the impacts of the products they are creating, and so when we see researchers were being dismissed for pointing out problems when we see problems being minimised, then we can see that we can'tjust listen to what companies are saying, we have to watch what they are doing.— they are doing. that was 'oy buolamwini. i they are doing. that was 'oy buolamwini. and i they are doing. that was 'oy buolamwini. and let's i they are doing. that was joy buolamwini. and let's talk l buolamwini. and let's talk about those companies now, from about those companies now, from a different perspective. as the pandemic eases, there will inevitably be more people looking forjobs. over the last few years, more and more recruitment companies have been using ai recruitment companies have been using al to match positions with people. and it may be that cvs, references, past salaries and academic grades are not the

best way tojudge and academic grades are not the best way to judge suitability. now some companies have been using other tools to try and uncover hidden talent. 22—year—old jess, who left school after her gcses, feels that the skills that she has are being missed injob interviews. as there is too much focus on qualifications rather than ability. i much focus on qualifications rather than ability.— rather than ability. i have a history in — rather than ability. i have a history in hospitality, i rather than ability. i have a | history in hospitality, which has obviously been hard—hit with covid, and so i spent a good few months searching and applying for hundreds ofjobs, with no success. 50 applying for hundreds of 'obs, with no success.i applying for hundreds of 'obs, with no success. so she 'oined a scheme i with no success. so she 'oined a scheme that i with no success. so she 'oined a scheme that is i with no success. so she 'oined a scheme that is part i with no success. so she 'oined a scheme that is part ofi with no success. so she joined a scheme that is part of the i a scheme that is part of the recruitment process —— as part of the recruitment process uses ai of the recruitment process uses al to game if i someone's personality and behaviour traits to match them to a job that would really suit them. the last question i remember vividly we had to try and crack the code, so there would be a

certain amount of numbers in the middle of a wheel and he had to try and click it when it hovered over the number, and it got faster and faster each time, it was an impossible thing. i actually found out in the end that theyjust thing. i actually found out in the end that they just wanted to test how long you trying for, so that was interesting. the arctic shores platform looks at thousands of data points. it's a! comparing results to that of a larger dataset. aiming to push users in the direction of a job they will do well. while hopefully overcoming any potential bias or stilted job interviews. so or stilted 'ob interviews. so there is or stilted job interviews. ’sr there is abstract thinking and detailed thinking, and i scored all the way to abstract which is apparently rare, so. ifeel like abstract thinking in itself is really never looked at as a strength, or it is hard to sell it in an interview. cognitive diversity is something that businesses are starting to consider a bit more, so i think it is great to have this game that kinda picks up have this game that kinda picks up on that, even if you are not

aware of it in yourself, but it can be picked up on. it's a really great feeling to be working with other people that have more traditional qualifications, and went through more traditional means of interviewing. i through more traditional means of interviewing.— of interviewing. i also tried the process _ of interviewing. i also tried the process myself- of interviewing. i also tried the process myself to i of interviewing. i also tried the process myself to see. of interviewing. i also tried i the process myself to see how it feels. so sky rise corporation is advertising their office spaces to potential companies that are visiting the building. you must inflate a5 balloons for their event taking place later today. i reckon i can get three. bono... they are not the same size. as well as testing your logic, it is a game of risk, showing how diverse you are to it. something there is not a right or wrong answer to, it just shows that you may be more suited to some roles than to others. myjob doesn't actually depend on this, does it? neuroscientists know what can be gleaned from these tasks. the process was not pressure

free either, but apparently that is also part of the test. so how did ifair? that is also part of the test. so how did i fair?— so how did i fair? you were very measured in the i so how did i fair? you were | very measured in the rescue take, so you are less likely to be impulsive...— take, so you are less likely to be impulsive... oh, yeah, i am not impulsive _ be impulsive... oh, yeah, i am not impulsive at _ be impulsive... oh, yeah, i am not impulsive at all, _ be impulsive... oh, yeah, i am not impulsive at all, that i be impulsive... oh, yeah, i am not impulsive at all, that is i not impulsive at all, that is very accurate.— not impulsive at all, that is very accurate. interesting in an interview _ very accurate. interesting in an interview situation i very accurate. interesting inj an interview situation where you are having to absorb information that is being given to you while at the same time processing what is being said, how am i going to respond to it, how is it relevant to the discussion, that is a lot of information you are holding at any one time in order to be able to do that successfully. so it shows that you are in the right role... if you ever needed telling that, lara, it is confirmed here.— is confirmed here. that was re is confirmed here. that was pretty intense. _ is confirmed here. that was pretty intense, you - is confirmed here. that was pretty intense, you don't i is confirmed here. that was i pretty intense, you don't want to be interrupted by a child or a housemate. (laughs). so let's see, ou a housemate. (laughs). so let's see. you are _ a housemate. (laughs). so let's see, you are risk— a housemate. (laughs). so let's see, you are risk averse - a housemate. (laughs). so let's see, you are risk averse and i see, you are risk averse and can hold a lot of information in your head, i have to say thatis in your head, i have to say

that is you, and come to think of it, that's me too.— of it, that's me too. what a coincidence! _ of it, that's me too. what a coincidence! we _ of it, that's me too. what a coincidence! we must i of it, that's me too. what a coincidence! we must both | of it, that's me too. what a i coincidence! we must both be on the rightjob. coincidence! we must both be on the right job-— coincidence! we must both be on the rightjob— the right 'ob. there you go, lona the right job. there you go, long may — the right job. there you go, long may it _ the right job. there you go, long may it continue. i the right job. there you go, long may it continue. part | the right job. there you go, | long may it continue. part of thejob long may it continue. part of the job now involves saying goodbye, so could you offload all about information that you have been holding in your head please? have been holding in your head lease? , ., , have been holding in your head lease? , ., i. have been holding in your head lease? , .,, i. please? yes, as ever you can find the team _ please? yes, as ever you can find the team on _ please? yes, as ever you can find the team on social i please? yes, as ever you can | find the team on social media, on youtube, instagram, facebook and twitter at bbc click. thanks for watching and we will see you soon. thanks for watching and we will see you soon-— hello. the weekend gets off to a chilly start, though not as cold as recent mornings. still a patchy rural frost around, a few mist and fog patches in eastern england and some areas of cloud in scotland and into north—east england. and the cloud thickening in shetland with a bit of light rain. but for most areas, it'll be another day of unbroken sunshine.

will feel cooler along the north sea coasts with a breeze coming in from the sea. whereas across western parts in the sunshine, every bit as warm as it's been over the past couple of days, 19, even 20 degrees celsius in a few spots. overnight and into sunday, we start to bring in more cloud from the north sea here into particularly parts of england and wales. there could be a few mist and fog patches around. just pockets of rural frost going into sunday. and then on sunday, more cloud certainly for england and wales. parts of scotland, too, breaking to allow some sunny spells. easternmost spots in england could stay rather cloudy with a chance of a light shower. an isolated heavy shower in highland scotland can't be ruled out. lots of sunshine in northern ireland. all parts a bit cooler.

welcome to bbc news. i'm mark lobel. our top stories: india's healthcare system buckles as a record surge in covid cases puts pressure on hospital beds and life—saving supplies. we have a special report from the frontline. if oxygen runs out, there is no leeway for many patients. there is no leeway. they will die. president macron says france will never yield to islamist terrorism after a man fatally stabs a woman police clerk near paris. from close ally to bitter critic: dominic cummings launches an explosive attack on borisjohnson, accusing the uk prime minister of lacking integrity.