Its horrible: Lawyer Jen Robinson on the toughest part of working for Assange – Sydney Morning Herald

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Wrapped in a camel coat against the autumn chill, a small, determined figure walks across a concrete plaza and disappears through a set of imposing glass security doors. Its a bright September day in The Hague, and Australian human rights lawyer Jennifer Robinson has come to the seat of government in the Netherlands to deliver a complaint to the International Criminal Courts (ICC) Office of the Prosecutor.

The complaint refers to the killing of Al Jazeera journalist Shireen Abu Akleh, who was shot in the head on May 11 while covering an Israeli raid in Jenin on the West Bank. Its alleged she was killed by a bullet fired by an Israeli sniper, and Robinsons filing is part of a bigger case in which it is argued that Israeli security forces have systematically targeted Palestinian journalists in violation of international humanitarian law.

Outside the court, Nasser Abu Bakr, president of the Palestinian Journalists Syndicate, tells me about Robinsons advocacy. When we talked about bringing these cases to the ICC, some people said, This is bullshit; it is a dream for you, he says. Today this dream is a fact because of the great support Jen gave us. In four months, she knew every single bone of our case. The dead journalists brother Anton stands beside Abu Bakr, his face a mask of deep sadness: This is what Jennifer is doing giving my family hope, he tells me.

The day prior to her appearance at the ICC, 41-year-old Robinson had been in Geneva to address the UN Human Rights Council on the arbitrary detention of journalists in Hong Kong. Two days later, she was back there to address the UNs working group on enforced and involuntary disappearances on behalf of Noel Zihabamwe, an Australian citizen from Rwanda whose two brothers disappeared after being abducted by Rwandan police in 2019.

If life was giving you a hard time, youd want Jennifer Robinson on your side. She held actor Amber Heards hand outside court during Johnny Depps 2020 libel case against Britains The Sun newspaper, and sat beside Heard in a black cab as a crowd pressed at the windows, screaming abuse. Heard has called Robinson the smartest person in the room and the most treasured asset in my life.

Robinson with Anton Abu Akleh, brother of Al Jazeera journalist Shireen Abu Akleh, and colleague Tatyana Eatwell at the ICC.Credit:Courtesy of Jennifer Robinson

Robinson has been Julian Assanges go-to legal adviser and constant support since 2010, when he released 250,000 secret US diplomatic cables, causing a global furore. These days, the WikiLeaks founder remains in a high-security jail, awaiting the outcome of a final appeal against a US extradition request to face espionage charges.

She has represented exiled West Papuan leader Benny Wenda for 20 years, standing by his side at podiums around the world advocating for his homelands independence from Indonesia. And when British Asian off-spin bowler Azeem Rafiq found himself overwhelmed by the struggle to prove claims of racism against his former team, Yorkshire County Cricket Club, he called Robinson.

Rafiq eventually got a six-figure payout from the club, which was followed with a 25 million (about $44 million) pledge from the England and Wales Cricket Board to tackle racism throughout the game. After five minutes on the phone with Jen, I knew I would be able to sleep that night, he says. Her humanity and grace is something I will treasure all my life.

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This, then, is the country girl who grew up in Berry on the NSW South Coast, went to the local public school, Bomaderry High, and admits to experiencing imposter syndrome in the early years of her career. Much like the protagonist in Suzie Millers play Prima Facie still wowing audiences in digital screenings of live performances around the UK Robinsons family had little money and no connections to law, and she worked three jobs to get through her undergraduate law degree at Australian National University (ANU).

Imposter syndrome is not just in your head, its real, she tells me. Its about gender and class, and there are real, structural reasons why people from backgrounds like mine feel out of place.

Were talking over green tea at the ancient Randolph Hotel in the English university city of Oxford. Robinson came here on a Rhodes Scholarship in 2006. Shed warned ahead of our meeting, youll spot my surf hair and indeed, her usually smooth blonde bob is having an unruly moment. Dressed in jeans and sneakers, shes come here from The Wave, an artificial surf pool near Bristol. Id been longing to try it, she says, her beaming face free of make-up.

The eldest of six children two of whom her father had with his second wife her mother was a teacher, and Jennifer could read and write before she went to school. I got my commitment to education from Mum and a commitment to excellence from my dad, she says. His motto is, You can always do better.

Terry Robinson had followed his father, legendary horse trainer Kevin, into racing. When we still had trotters, hed pick me up from school in the horse truck on a Friday and wed drive up to Sydneys Harold Park. Id strap the horse for him, watch him race, then wed go back. Hed have three hours sleep before riding beach trackwork. He still does it at 67.

She pulls out her phone to show me a photo of horses galloping on Seven Mile Beach, near Berry, in the glow of sunrise. Its my favourite sound in the world: the rhythm of horses hooves on the sand and the surf in the background.

Robinson with father Terry in 2011. I got my commitment to education from my mother, and a commitment to excellence from my dad. Credit:Adam Wright

We walk across the road to the Oxford college where, as a Rhodes scholar, Robinson took civil law and a masters in international public law. Balliol is one of the dreamiest of dreaming spires and an elite one, in the upper reaches of the academic tables. Its also known as progressive and lefty, she says, though Boris Johnson was here, so probably not a good example.

In Australia, people always ask where you went to school here they ask, Oxford or Cambridge? And then, Which college? When I say Balliol, theyre thinking, Ooh, interesting, an Australian. Theyre confused and trying to place you.

Then he said, In the 1970s we let women in, so look around you, fellows, you could be sitting next to your future wife. I thought, What are we, marriage fodder?

We enter the lofty dining hall, where oil portraits of robed men are interspersed with group photographs of female alumnae. Those are new, says Robinson of the photos. When I was here there were none, only old white men. At my coming-up dinner in 2006, the vice-regent talked about all the famous Balliol men: Nobel Prize winners, prime ministers, the crme de la crme. Then he said, In the 1970s we let women in, so look around you, fellows, you could be sitting next to your future wife. I thought, What are we, marriage fodder?

Even so, she loved her time here. It was so beautiful and such a massive privilege, she says. We had world leaders passing through, a concentration of intellect. And I had a wonderful group of friends, the brightest kids from around the world.

She admits later that she suffered depression during her studies and took a term off to go home. It was partly the pressure. I didnt know how I would live up to being a scholar. Before I came, someone wise told me, Oxford will be the best and the worst time of your life. I didnt understand that until I got here.

Robinson welcoming West Papuan freedom fighter Benny Wenda, wife Maria and their first child to London in 2003. Credit:Courtesy of Jennifer Robinson

By the time Robinson was at Oxford, Benny Wenda, his wife Maria and their first child were safely in the UK, thanks in large part to the Balliol scholar, who would successfully deal with their asylum requests and citizenship applications. Robinson had met Wenda in Indonesia in 2002 as part of her ANU studies. He was in jail after being arrested for leading an independence rally and shed come across him shackled in a courtroom.

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She takes me to meet the Wendas at their home in Oxford, where shes seen their six children grow up. Theres laughter as they recall the day in 2007 when Maria asked Robinson if she could look after the children because she had to go out. Benny had been unable to help hed had surgery on his leg, broken in a bombing raid by Indonesia when he was young, and couldnt walk.

Maria had never asked me to help before, says Robinson. It turned out she was in labour and I didnt even know she was pregnant! Robinson moved in for a term: I would take the kids to school, then get on my bike and go to classes, then come back for tea and bath time. Maria nods: She just came in like an angel!

Robinson continues to work pro bono for Wenda and his push for West Papuas right to self-determination, a 60-year struggle. People say to me, Why do you go on? she says. It will never happen. But there is a concentrated, international legal effort. It is expensive, so we have to fundraise. She gave a TEDxSydney address, Courage is Contagious, in 2013. That produced a lot of support.

While still at Balliol, Robinson was approached by another Australian lawyer whod been a Rhodes scholar, Geoffrey Robertson, to help him with research. It included travelling the world interviewing survivors of the 1988 prisons massacre in Iran thousands of political prisoners were thought to have been summarily executed and advising Mauritius on media law reform.

I spent [so much] time at Oxford doing pro bono work on human rights cases and working for Geoff that my academic supervisor said I should just crack on with being a lawyer because I was clearly more interested in case work than academic research, she says. They continued working together after she joined London solicitors Finers Stephens Innocent, including collaborating on a case against the Catholic Church over child sex abuse.

In 2010, when a major WikiLeaks exposure of Americas military secrets emerged, the pair agreed that their fellow Australian Julian Assange might soon need their help. They were right, though not for the reasons they expected. In September that year, after Assange was accused of sexually assaulting two women in Sweden (which he denied), he contacted Robertsons Doughty Street Chambers. Two months later, WikiLeaks released the first batch of 250,000 classified US diplomatic cables, leading to global headlines.

Robinson with Julian Assange, centre, after he was granted bail in 2010, and Geoffrey Robertson, second from right.Credit:Getty Images

From then on, Robinson would be in constant touch with Assange, during his stay in rural East Anglia on bail and in 2012, when he claimed asylum at Londons Ecuadorian embassy to avoid the threat of extradition to Sweden. When asked by journalists how her feminist principles sat with defending a client accused of rape, she always gave the same answer: Everyone deserves a defence.

Robertsons Doughty Street colleague Helena Kennedy interviewed Assange with Robinson while he was on bail. Assange is a very difficult man, she tells me, and there eventually came a period when people in his inner team were peeling away from him. He had a serious falling-out with Mark Stephens, the senior lawyer with whom Robinson was working. At that moment, she could have easily decided that her future lay with being nice to her superior and casting Assange adrift, but she didnt do that. She behaved in an honourable way and also this is one of her many skills managed to keep her friendship with Mark.

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Assange remained in the embassy for seven years, convinced that the Swedish case was a pretext for his eventual extradition to the US. He received regular visits from Robinson and a stream of high-profile supporters, including Lady Gaga. In May 2017, Swedens director of public prosecutions dropped the assault case, but a year later Assange was arrested inside Ecuadors embassy on a charge of breaching bail. He was convicted and sent to Belmarsh Prison. His initial sentence was for 50 weeks, but he has been imprisoned there for three-and-a-half years while extradition proceedings continue. His fellow inmates include serial rapists and murderers.

Outside the monstrous grey walls of Belmarsh, trees are hung with tattered yellow ribbons bearing the message Free Julian Assange. I wait in the Belmarsh visitors centre for Robinson, who is inside meeting Assange. She emerges, a slight figure in a red dress among a bunch of dark-suited lawyers who, like her, have been visiting clients. We queue up, she explains. I walk past the legal meeting rooms containing people convicted of heinous crimes, and then there is Julian, winner of the Sydney Peace Prize and a Walkley award for outstanding journalism, with his copies of The Economist and the London Review of Books.

On each visit she takes him a KitKat, a tangerine and a coffee, and reports on progress and setbacks. He told me he hadnt seen his family for six months, she says. [Assange, now 51, has two sons with his wife, Stella Moris.] Then, when they came, he wasnt allowed to touch his children. Theyre stealing his life. He has a terrible depressive illness how could you not?

Robinson, pictured with Assange in 2011, calls the Australian governments lack of action to free him a shame on our country.Credit:Getty Images

Does she get upset when she cant bring him any comfort? Its horrible. We are both Australians I feel awful telling him about bushwalks and going to the beach, things he really misses but still wants to hear about. Its heartbreaking. Could his own country protect him? Absolutely Australia could be negotiating with the US about this. [Prime Minister] Anthony Albanese made positive statements in opposition saying it was time for it to end so we hope there will be a change now. It requires political action from the Australian government. Its a shame on our country.

She calls a taxi. Waiting for its arrival, we sit on a bench in the warm London sunshine, and chat. It seems shes spent more time in Australia of late, I say, in part thanks to the pandemic.

Its horrible. We are both Australians I feel awful telling him about bushwalks and going to the beach, things he really misses but still wants to hear about. Its heartbreaking.

She nods. Ive loved being at home! Julians case came so early in my career and was so compelling and so unjust it kept me here in England that and the work that spun from it. But now, in this remotely connected world, Ive done court hearings from Smiths Beach [in Western Australias Margaret River]. That was not a possibility pre-COVID and now it is entirely possible to split my time between the UK and Australia. I can work on cases of international significance and still spend time with my family.

With some trepidation, because shes always refused to talk about her personal life, I remark that everyone seems to know she spent months of lockdown in WA. I had the privilege of spending time in WA, she says evenly, living at Smiths Beach during lockdown, and travelled around in a 60s caravan spending time in Esperance and Exmouth and Denmark, staying in caravan parks and surfing. It was such freedom, like reconnecting with childhood holidays.

I let that one go through to the keeper, then later she emails me to confirm: I dont speak about my private life.

Robinson with Keina Yoshida, her co-author on the book How Many More Women?. Credit: Kate Peters

Robinson turned 40 during the pandemic; lockdown gave her time to write a book, How Many More Women?, which is out next week. Over a year, she and her co-author, fellow human rights lawyer and former Doughty Street Chambers colleague Keina Yoshida, listened via Zoom to stories from survivors of sexual assault, the journalists who wrote about them and feminist activists around the world. They heard story after shocking story about how defamation and privacy law is wielded by rich and powerful men to silence women who speak out and about how those women, even when their claims are vindicated, are further abused by vicious online trolling.

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Robinson says the idea for the book had been brewing for some time. Id observed defamation cases being filed, she says, watched the backlash to #MeToo youd be amazed how much goes on that never breaks the surface, that is resolved confidentially and never makes it to court. The result is often that the women are prevented from ever telling their story.

I could defend these cases to the end of my career, but the needle on the dial wouldnt shift. We need a bigger conversation and telling a story is an entry to empathy: those women we talked to had such resilience, I wish we could get them all in a room together.

Johnny Depp lost his 2020 defamation case against The Sun because the judge believed his ex-wifes account of the abuse she suffered at his hands. That didnt stop Depps supporters attacking Heard and the lawyer standing beside her. I had never faced anything like it before, writes Robinson, the trolling was relentless. Everything from my ethics and professionalism to my appearance and my personal relationship history was attacked. Trolls vowed to ruin me and make sure I never worked again because[We] had proven Depp was a wife-beater. (In a separate trial in the US this year, a jury found that Heard had defamed Depp in describing herself as a victim of domestic abuse in an 2018 opinion essay for The Washington Post.)

With Amber Heard in 2020. Both women faced relentless trolling during the defamation case filed by Johnny Depp.Credit:Getty Images

When it came to writing the book, Robinson was in WA, Yoshida in Madrid, ready to go: We did most of the interviews together, Yoshida tells me, and we talked almost every day. I would often be walking in the Retiro, Jen would be on the beach and I could hear the sea breaking in the background as we discussed the stories.

One of the most egregious of them concerns a young Japanese journalist, Shiori Ito. In 2015, Ito met up with Noriyuki Yamaguchi, a well-connected media boss in Tokyo, to discuss a job opportunity. Five days later, she walked into a police station to allege shed been raped in a hotel room by Yamaguchi while she was unconscious. She was eventually told there was not enough evidence for a prosecution.

In 2017, she went public, calling on police to reopen the investigation and bringing attention to the ways in which Japans criminal justice system was failing. A public backlash followed, during which Ito was accused of political motivation (Yamaguchi was close friends with the then prime minister, Shinzo Abe). At the same time, Yamaguchi filed a defamation claim against her. Ito countersued, arguing it was defamatory for him to allege she was making up the accusation. She produced CCTV footage that showed him carrying her, evidently unconscious, into the hotel.

Japanese journalist Shiori Ito went public when the investigation into her sexual assault was dropped.Credit:Getty Images

In 2019, Ito won damages in her civil suit, with the court dismissing his 130 million (about $1.4 million) claim against her. The court found she had been forced to have sex without contraception, while in a state of unconsciousness and severe inebriation. The countrys supreme court dismissed Yamaguchis appeal and awarded Ito 3.3 million (about $35,000) in damages, and partially recognised defamation by Ito, awarding Yamaguchi 550,000 (about $6000).

The trolling Ito receives is so bad that she has a team of checkers to go through her social media for her. She has successfully sued critics and tweeters for libel, and is campaigning to make the internet a safer space and to reform Japans sexual offences laws.

This sounds exhausting, I say to Robinson. She nods. But its important to grapple with these issues. There are women organising, campaigning, litigating and fighting back. We want their stories to inspire more women to see they arent alone, that they have options and that legal change is possible.

Robinson and her grandmother joined Australias March4Justice in early 2021, where Cracknell grumbled, I cant believe Im still protesting about this shit.

The book was in part inspired by her maternal grandmother, Philipa Cracknell, now 85, who ran womens refuges in Sydney in the 1980s. I remember the rule, says Robinson. Never, never answer the front door. That was because violent men would be trying to find the women and children. Weve been talking recently and Ive learnt so much about her own experience of abuse before she left my grandfather, and how that motivated her to help women, how she trained police in responding to domestic violence. I said, At what point in my legal career did you not think to tell me? She said, You didnt ask.

Robinson and her grandmother joined Australias March4Justice in early 2021, where Cracknell grumbled, I cant believe Im still protesting about this shit.

We took my little sister Matilda with us, recalls Robinson. Shes 13, and I remember the look on her face when women were asked to put up their hands if they were a survivor. My grandmother put up her hand, but so did most of the women there. It was as if Matilda clocked it just there, a dawning realisation. It was a powerful moment.

Robinson with sister Matilda and gran Philipa Cracknell, a survivor of abuse, at 2021s March4Justice.Credit:Courtesy of Jennifer Robinson

The journey from the badlands of Belmarsh takes an age but finally the cab pulls up outside the tall Georgian faade of 54 Doughty Street, the chambers founded in 1990 for the protection of civil liberties, and Robinsons workplace since she qualified for the English bar in 2016. Doughty Street lawyers are the rock stars of the human rights scene and, in retrospect, it was inevitable that Robinson would join them. But before she did, along with her great friend Amal Clooney, she made what seemed to some a sideways, if not backwards, move.

Celebrating Assanges 40th birthday in 2011, she got talking to a man who turned out to be a philanthropist with deep pockets. He said, There should be more lawyers like you in the world, and I said, Let me tell you why there arent. And I went on a rant about uni debt, educational privilege, access to networks and mentors. At the end he said, I need a global legal champion and I think youre going to be it. Come and see me next week.

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Goodness, I say, its a fairy story. It is, she replies, though lots of people said it was bonkers to step off the path I was on. But I was leaving my law firm anyway, and I thought, Why be one human rights lawyer when I could create opportunities for many?

In 2011, Robinson became director of legal advocacy at the Bertha Foundation, a South Africa-based social justice organisation founded by the philanthropist shed met that night, Tony Tabatznik. We supported the case against stop-and-frisk litigation [in New York] and that racist law was overturned, Robinson tells me. We funded litigation against the CIAs drone strikes in Pakistan. I made decisions about where to put money, which cases and campaigns to support.

By the time she began her pupillage at Doughty Street, says her mentor Helena Kennedy, Robinson was thoroughly versed in international human rights. Jen is a very clever, capable lawyer and enormously hardworking. She is also very bonny and that can mean having to work even harder to persuade people youre a serious person, that you can be both smart and gorgeous. Sometimes she knew men would be assessing her on her looks rather than her acumen.

Theres often a leeriness about women pushing to do the demanding cases, but no one thinks anything of men being ambitious.

Shes ambitious, and thats another thing: theres often a leeriness about women pushing to do the demanding cases, but no one thinks anything of men being ambitious.

Robinson is on the board of the Grata Fund in Australia, a not-for-profit doing similar work to the Bertha Foundation. Its founding director, Isabelle Reinecke, says, We needed an A-team of heavy-hitters and, with herinternational profile, Jen was an obvious choice.

The two met at Bambini Trust restaurant, a haunt for Sydney lawyers. She ordered champagne and said, Now tell me everything.She got it right away and said, Im in 100 per cent. She comes to board meetings after shes been for a surf and is the least puffed-up person in the room.

Robinson seems to be getting her feet into the sand in Australia pretty thoroughly. She does not practise as a barrister in Australia but takes on international cases through her London chambers: I am committing part of my practice to climate change issues and part to First Nations justice.

On behalf of Vanuatu, she is referring developed countries to the International Court of Justice on the basis that theyre not committing enough to the reduction of global warming. It raises fundamental existential questions, she says. These island countries have contributed so little to climate change and suffer so much.

Shes also working on the case of David Dungay, the 26-year-old Dunghutti man who died in custody in Sydneys Long Bay jail in 2015 after being held down by guards. He is Australias George Floyd. Ive taken a UN Human Rights Committee case on behalf of his mother Leetona Dungay against Australia for failure to prosecute prison officers responsible for Aboriginal deaths in custody.

I am committing part of my practice to climate change issues and part to First Nations justice, says Robinson, who is working on several cases closer to home.Credit:John Davis

She would like to know more about First Nations history. For example, my dads horse farm is known as Mount Coolangatta, she says. That mountain [across the road] was the centre of our lives, you could always see it from wherever you were, but I didnt know it was actually called Cullunghutti and is a sacred place. There was a building near my school which Id driven past a thousand times but had no idea what it was. I now know that it was a residential home where children of the Stolen Generations were brought. Why were we not taught these things? Why was this not part of the conversation?

Shes working on re-educating herself, sitting down with land council leaders, and last year teamed up with RebLaw, a group of young lawyers working on First Nations advocacy around the Uluru Statement from the Heart.

Three years ago, Professional Footballers Australia asked Robinson to assist in preparing a claim against FIFA for equal prize money for women players in the world cup. The difference between the mens and womens teams is astronomical, explains CEO Kathryn Gill, yet the Matildas are one of the biggest sporting teams in Australia. We approached Jen because nothing is too challenging for her she is relentless and gets a lot of pleasure in tackling injustices. Robinson says she hopes the case goes ahead: Inequality in prize money is unacceptable and violates FIFAs human rights obligations under its own constitution.

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Hardworking, loyal, relentless: the epithets turn up again and again, but the same people assure me Robinson knows how to party and has a wide circle of friends. She is great fun, says Helena Kennedy, and interested in all the arts. We went to the Venice Biennale together its all part of who she is.

Robinson used to play touch footy with a mens team but says its too difficult to fit in group sports with all her travelling. Now I bushwalk and do yoga and surf whenever I can. Keina Yoshida recalls Robinson taking a party of friends to Montpellier in southern France to watch the Matildas beat Brazil in the 2019 World Cup: She bought us all team T-shirts.

Robinson once told an interviewer that she keeps only champagne in her fridge. As for those beautifully cut dresses shes wearing in multiple press photographs? Theyre sourced for her by a stylist. I hate shopping, she says. Id rather be out with my friends.

Ive been in touch with her on and off for weeks for this story. Shes always on message, always replies promptly, but is like the Scarlet Pimpernel Im never quite sure where shell be. One minute at the cinema with her friend Jemima Khan for a private screening of Khans new film, the next on a plane to Geneva for another filing at the UN. By the time you read this, shell be in Australia. I cant help but wonder how frequent international travel fits with her climate concerns. I do try to limit it but there are bigger structural problems than my flights, she says crisply.

Theres steel beneath that bonny exterior.

To read more from Good Weekend magazine, visit our page at The Sydney Morning Herald, The Age and Brisbane Times.

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Its horrible: Lawyer Jen Robinson on the toughest part of working for Assange - Sydney Morning Herald

Machine learning operations offer agility, spur innovation – MIT Technology Review

The main function of MLOps is to automate the more repeatable steps in the ML workflows of data scientists and ML engineers, from model development and training to model deployment and operation (model serving). Automating these steps creates agility for businesses and better experiences for users and end customers, increasing the speed, power, and reliability of ML. These automated processes can also mitigate risk and free developers from rote tasks, allowing them to spend more time on innovation. This all contributes to the bottom line: a 2021 global study by McKinsey found that companies that successfully scale AI can add as much as 20 percent to their earnings before interest and taxes (EBIT).

Its not uncommon for companies with sophisticated ML capabilities to incubate different ML tools in individual pockets of the business, says Vincent David, senior director for machine learning at Capital One. But often you start seeing parallelsML systems doing similar things, but with a slightly different twist. The companies that are figuring out how to make the most of their investments in ML are unifying and supercharging their best ML capabilities to create standardized, foundational tools and platforms that everyone can use and ultimately create differentiated value in the market.

In practice, MLOps requires close collaboration between data scientists, ML engineers, and site reliability engineers (SREs) to ensure consistent reproducibility, monitoring, and maintenance of ML models. Over the last several years, Capital One has developed MLOps best practices that apply across industries: balancing user needs, adopting a common, cloud-based technology stack and foundational platforms, leveraging open-source tools, and ensuring the right level of accessibility and governance for both data and models.

ML applications generally have two main types of userstechnical experts (data scientists and ML engineers) and nontechnical experts (business analysts)and its important to strike a balance between their different needs. Technical experts often prefer complete freedom to use all tools available to build models for their intended use cases. Nontechnical experts, on the other hand, need user-friendly tools that enable them to access the data they need to create value in their own workflows.

To build consistent processes and workflows while satisfying both groups, David recommends meeting with the application design team and subject matter experts across a breadth of use cases. We look at specific cases to understand the issues, so users get what they need to benefit their work, specifically, but also the company generally, he says. The key is figuring out how to create the right capabilities while balancing the various stakeholder and business needs within the enterprise.

Collaboration among development teamscritical for successful MLOpscan be difficult and time-consuming if these teams are not using the same technology stack. A unified tech stack allows developers to standardize, reusing components, features, and tools across models like Lego bricks. That makes it easier to combine related capabilities so developers dont waste time switching from one model or system to another, says David.

A cloud-native stackbuilt to take advantage of the cloud model of distributed computingallows developers to self-service infrastructure on demand, continually leveraging new capabilities and introducing new services. Capital Ones decision to go all-in on the public cloud has had a notable impact on developer efficiency and speed. Code releases to production now happen much more rapidly, and ML platforms and models are reusable across the broader enterprise.

Open-source ML tools (code and programs freely available for anyone to use and adapt) are core ingredients in creating a strong cloud foundation and unified tech stack. Using existing open-source tools means the business does not need to devote precious technical resources to reinventing the wheel, quickening the pace at which teams can build and deploy models.

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Machine learning operations offer agility, spur innovation - MIT Technology Review

Its Not Just About Accuracy – Five More things to Consider for a Machine Learning Model – AZoM

You might think that a machine learning (ML) specialist company likeIntellegens is always pursuing the perfect model - one that takes a new set of system inputs and predicts their outputs correctly every time. But, despite the importance of model accuracy, it is possible to focus on it too much in real-world R&D.

A near-perfect model typically considered a model that predicts outputs reliably to within 5% - could mean thatmachine learning (ML)has found a set of robust relationships not previously observed by cutting through multi-dimensional complexity.

Image Credit: Intellegens Limited

However, this can also mean that experiments were poorly designed or trivial, and the ML is simply confirming the obvious. Such perfection is, in any case, mathematically unachievable in many complex systems with inherent uncertainties.

In the real world of R&D, a typical use case might be designing a set of experiments to find more effective formulations, chemicals, or materials. Here, visualizing the range of possibilities is beyond the capacity of the human brain and even relatively sophisticated Design of Experiments methods still result in large, expensive and time-consuming experimental programs. Users dont want perfection they just want ML to shift the odds in their favor, with predictions that outperform the logic currently driving their work.

Pursuing the ideal model may also waste time that is better spent elsewhere. It may also lead to users inadvertently narrowing down their search space in ways that exclude more innovative solutions.

Instead of asking how accurate a model is, the right question may focus on the models usefulness. Below are Intellegens top five examples of questions that might help a user to shape their model:

1. Can we get to an answer in fewer experiments?

Does the ML that is being used have the ability to understand what missing data could best improve its accuracy? This information can then be deployed to decide what experiment to perform next, resulting in a significantly reduced time-to-market. In some cases, theAlchemitesoftware from Intellegens has reduced experimental workloads by 80%+. More commonly, reductions of 50% are reported.

2. How do we generate new ideas for formulations that achieve our goals?

New concepts with a chance of success can result from a moderately-accurate model. And R&D teams are given a big helping hand if the model comes with a robust estimate of its uncertainty, pointing them towards those most likely to succeed. If the ML can move the dial so that one in three candidate formulations succeed when the previous metric was one in five, this could make a big difference.

3. Can we remove costly or environmentally harmful ingredients?

Questions like this typically derive from consumer, regulatory, or market pressure and require a fast response. ML can screen potential solutions, and an indication of probable success can be given by quantifying the uncertainty of the predictions.

4. Where should we focus which inputs are the most significant?

The absolute accuracy of predictions may be less important than whether useful relationships are identified, for example, between structure, processing variables, and properties. Often, the latter is the most vital piece of information that users need. A series ofanalytical toolsthat enable users to explore the sensitivity of outputs to particular inputs are provided by Alchemite.

5. Can we make better use of the expertise weve already developed?

Insight developed at great expense in R&D projects is often not be re-used. A valuable starting point for future projects can be provided by the ability to capture this insight in anML model.

Alchemite Analytics How Do Changes in Inputs Impacts Outputs?

Rather than focusing on ML as a magic bullet, it is essential to consider its use in informing scientific intuition and functioning alongside it.

Image Credit: Intellegens Limited

It is vital to have the right tools like uncertainty quantification and graphical analytics to interrogate and understand the results. When data is messy, as it often is in R&D, rather than investing up-front effort to clean and enrich the data, it can be valuable to be able to generate an ML model even an imperfect one quickly. By exploring this model, users can gain insight and improve their work iteratively and at a much lower cost.

The team at Intellegens values accurate models, and sometimes, they are, of course, essential. Mostly they also work in the spirit of the aphorism commonly attributed to statistician George Box:All models are wrong; some are useful.

This information has been sourced, reviewed and adapted from materials provided by Intellegens Limited.

For more information on this source, please visit Intellegens Limited.

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Its Not Just About Accuracy - Five More things to Consider for a Machine Learning Model - AZoM

Machine learning to predict the development of recurrent urinary tract infection related to single uropathogen, Escherichia coli | Scientific Reports…

A total of 963 E. coli UTI patients from NCKUH were included, 14.2% of them had E. coli RUTI. All the 137 RUTI patients included in this study had RUTI caused by E. coli, 74 patients (54%) had 2 episodes of UTI within 6months and 63 patients (46%) had 3 episodes of UTI within 12months. All these episodes of E. coli related RUTI in this study were reinfection (recurrence of UTI with the same organisms in more than 2weeks). The duration of antibiotic treatment varied from 3 to 14days, and the antibiotic regimens included empirical antibiotic therapy and definitive antibiotic therapy according to the antimicrobial susceptibility test. The patient characteristics related to UTI and RUTI caused by E. coli are shown in Table 1. The median age was 67 and 75years for patients with UTI and RUTI, respectively. Compared to the UTI group, patients with RUTI had an older age, a greater prevalence of diabetes mellitus, liver cirrhosis, indwelling Foley catheter, neurogenic bladder, more frequent hospitalization/emergency department (ED) visit/UTI within 2years and any UTI symptom, and a worse renal function (Table 1).

The bacterial characteristic factors (phylogenicity, virulence genes, and antimicrobial susceptibility) related to UTI and RUTI are shown in Tables 2 and 3, respectively. Compared to those in the UTI group, E. coli isolates derived from the RUTI group had a lower prevalence of papG II, usp, ompT, and sat genes, and a higher prevalence of antimicrobial resistance in several antibiotics (including cefazolin, cefuroxime, cefixime, and levofloxacin).

The analysis results suggested RF model was better than the LR and DT model for RUTI prediction in the clinical visit. The 32 factors considered in the models for the first stage were age, gender, comorbidities (Dis1~Dis12), UTI symptoms (UTI_symptom1~UTI_symptom8), serum creatinine, frequency of hospitalization/emergency department (ED) visit/UTI within 2years (Pre_hos_2y, Pre_UTI_ER_2y, Pre_UTI_hos_2y), urinary red blood cell/HPF (URBC_level), urinary white blood cell (WBC)/high power field (HPF) (UWBC_level), urinary bacterial count (UBact), peak blood WBC count (BloodWBC), place (outpatient or ED) of urine sample collection (Place_of_collection), and disease group (four_disease_group). These factors are labeled in Table 1.

URBC_level and UWBC_level represent the rescaled level of the URBC and UWBC with values from 0 to 4 and from 1 to 4, respectively. The values 0, 1, 2, 3, and 4 of the URBC_level and UWBC_level corresponded to the ranges 0, 1~10, 11~100, 101~1000, and greater than 1000 per HPF, respectively. Place_of_collection indicates the place of urine sample collection, including outpatient clinic and ED. A new factor called four_disease_group was defined for RUTI prediction with value 0 or 1. We set four_disease_group value to 1 when one of the following diseases with anatomical or functional defect of urinary tract is present: indwelling Foley catheter (Dis5), obstructive uropathy (Dis6), urolithiasis (Dis7), and neurogenic bladder (Dis9). We would like to confirm the relation of four_disease_group with RUTI.

Regarding the validation results of fitted models to predict the development of RUTI in the clinical visit, Table 4 shows that the mean validation accuracy of RF is 0.700 which is higher than the results of LR and DT. The mean validation sensitivity and specificity of RF are 0.626 and 0.712, respectively. The standard deviations of estimated validation accuracy, sensibility, and specificity are 0.039, 0.131, and 0.046, respectively, which support the stability of RF model prediction. Note that the RUTI rate is only 136/963=0.138 which is relatively low for the observed samples. A nave model would predict non of the patients to have RUTI with a high accuracy 827/963=0.862. However, such prediction will lead to a very poor sensitivity with value 0. The RF model avoided such serious bias and provided a balance prediction capability in both sensitivity and specificity. The key technique in the RF model training is the usage of upsampling.

Variable importance in RF is evaluated by the mean decrease of accuracy in predictions on the out of bag samples when a given variable is excluded from the model. For example, if the age is taken away, the model prediction will reduce the accuracy rate by 11.9%. Figure1 is the variable importance plot of the RF analysis and shows that age, cirrhosis (Dis4), diabetes mellitus (Dis1), and disease group (four_disease_group) are the most important factors to predict recurrence of UTI in the clinical visit. Each of the 4 factors contributed around 10% prediction accuracy in the RF model.

Variable importance plot of the first stage RF analysis in percentage of mean decrease accuracy for the factors. It shows that age, cirrhosis (Dis4), diabetes mellitus (Dis1), and disease group (four_disease_group) are the most important 4 factors to predict recurrence in the clinical visit (sample size = 963).

A DT model is able to construct the decision rules for RUTI classification and provides the order of importance of the factors at the same time. Table 4 shows that the mean validation accuracy, sensitivity, and specificity of DT model are 0.654, 0.618, and 0.660, respectively. Although the validation accuracy of the DT is less than the values of the RF model, the results of DT model has its own edge in decision rule construction.

To obtain more insight on the RUTI factors in the clinical visit, one can check on Fig.2 which is the decision rules of the DT model built from all the 963 patients. The purpose of building a DT model with all collected data is to construct the decision rules for RUTI classification. In a DT model, when the patients satisfy the node's condition, the patients will be allocated to the left path of the node, otherwise the patients will be allocated to the right path of the node. The classification accuracy of this tree is 0.88, and the sensitivity and specificity are 0.26 and 0.98, respectively. Although the sensitivity is low due to the unbalanced rates of RUTI and UTI in the DT model, there are several valuable rules for RUTI classification. The 2 green boxes and 1 red box in Fig.2 indicate the nodes of the decision rules with a accuracy rate higher than 0.85 and 0.70 for non RUTI and RUTI classification, respectively. The three decision rules are:

When the factor states of a patient are without neurogenic bladder (Dis9=0) and without hospitalized within 2years (Pre_hos_2y<1), this rule claims that the patient will have no RUTI with classification accuracy 439/(439+34)=0.92.

When the factor states of a patient are without neurogenic bladder (Dis9=0), with previous hospitalization at least one time within 2years (Pre_hos_2y>=1), with serum creatinine less than 0.93mg/dL (creatinine<0.93), without cirrhosis (Dis4=0), and previous ER for UTI less than two times within 2years (Pre_UTI_ER_2y<2), this rule claims that the patient will have no RUTI with classification accuracy 296/(296+46)=0.86.

When the factor states of a patient are without neurogenic bladder (Dis9=0), with previous hospitalization at least one time within 2years (Pre_hos_2y>=1), with serum creatinine in the range between 0.74 and 3.9mg/dL (0.74

The decision rules of the DT analysis for development of RUTI in the clinical visit. (sample size = 963). The 2 green boxes and 1 red box indicate the nodes of the decision rules with an accuracy rate higher than 0.85 and 0.70 for non RUTI and RUTI classification, respectively.

The analysis results suggested RF model was better than the LR and DT model for RUTI prediction after hospitalization. The 62 factors considered in the models for the second stage not only contain the 32 factors used in the first stage analysis, but also include phylogenicity, 16 virulence genes, 11 antimicrobial susceptibility, Bacterial_Name, UTI_pos, Hospitalday, and Place_of_collection. The genes and antimicrobial are labeled in Table 2. Bacterial_name indicates Escherichia coli with or without extended spectrum -lactamase (ESBL). UTI_pos represents the location of urinary tract infection. Hospital_day gives the length (day) of hospital stay. Place_of_collection records the place of sample collection at ER, hospital, or outpatient clinic.

Regarding the validation results of refitted models to predict the development of RUTI after hospitalization, Table 5 shows that the mean validation accuracy of RF is 0.709 which is higher than the results of LR and DT. The mean validation sensitivity and specificity of RF are 0.620 and 0.722, respectively. The standard deviations of estimated validation accuracy, sensibility, and specificity are 0.047, 0.057, and 0.058, respectively, which support the stability of RF model prediction. Note that the RUTI rate is only 112/809=0.138 which is relatively low for the observed samples. A nave model would predict non of the patients to have RUTI with a high accuracy 697/809=0.862. However, such prediction will lead to a very poor sensitivity with value 0. The RF model avoided such serious bias and provided a balance prediction capability in both sensitivity and specificity.

Variable importance plot shows that based upon the mean decrease of accuracy in predictions on the out of bag samples when a given variable is excluded from the model. For example, if the cefixime (Anti7) is taken away, the model prediction will reduce the accuracy rate by 9.14%. Figure3 is the variable importance plot of the RF analysis and shows that cefixime (Anti7), afa (Gene11), usp (Gene8), and cefazolin (Anti5) are important factors to predict recurrence after hospitalization. Each of the 4 factors contributed more than 8% prediction accuracy in the RF model.

Variable importance plot of the second stage RF analysis in percentage of mean decrease accuracy for the factors. It shows that cefixime (Anti7), afa (Gene11), usp (Gene8), and cefazolin (Anti5) are important factors to predict recurrence after hospitalization (sample size = 809).

To obtain more insight on the RUTI factors after hospitalization, one can check on Fig.4 which is the decision rules of the DT model built from all the 803 patients. The classification accuracy of this tree is 0.89, and the sensitivity and specificity are 0.27 and 0.99, respectively. Although the sensitivity is low due to the unbalanced rates of RUTI and UTI in the DT model, there are several valuable rues for RUTI classification. The 4 green boxes and 3 red boxes in Fig.4 indicate the nodes of the decision rules with an accuracy rate higher than 0.85 and 0.70 for non RUTI and RUTI classification, respectively. The 7 decision rules are:

When the factor states of a patient are bacterial phylogenetic group B2 (Gene17=3) and the age less than 76years old (Age<76), this rule claims that the patient will have no RUTI with classification accuracy 322/(322+18)=0.94.

When the factor states of a patient are bacterial phylogenetic group B2 (Gene17=3), the age over 76years old (Age (ge) 76), and serum creatinine less than 3.5mg/dL (creatinine<3.5), this rule claims that the patient will have no RUTI with classification accuracy 148/(148+21)=0.87.

When the factor states of a patient are bacterial phylogenetic group B2 (Gene17=3), the age over 76years old (Age (ge) 76), serum creatinine less than 3.5mg/dL (creatinine (ge) 3.5), and more than 19days of hospital stay (Hospital_day (ge) 19), this rule claims that the patient will have RUTI with classification accuracy 8/(3+8)=0.72.

When the factor states of a patient are non-group B2 in bacterial phylogenicity (Gene17 (ne) 3) and S or I type in levofloxacin susceptibility (Anti25=1, 2), this rule claims that the patient will have no RUTI with classification accuracy 137/(137+22)=0.86.

When the factor states of a patient are non-group B2 in bacterial phylogenicity (Gene17 (ne) 3), R type in levofloxacin susceptibility (Anti25=3), bloodWBC more than 7.8 (bloodWBC (ge) 7.8), and group A or B1 in bacterial phylogenicity (Gene17=1, 2), this rule claims that the patient will have no RUTI with classification accuracy 42/(42+5)=0.89.

When the factor states of a patient are non-group B2 in bacterial phylogenicity (Gene17 (ne) 3), R type in levofloxacin susceptibility (Anti25=3), bloodWBC more than 7.8 (bloodWBC (ge) 7.8), group D in phylogenicity (Gene17=4), and more than 57days of hospital stay (Hospital_day (ge) 57), this rule claims that the patient will have RUTI with classification accuracy 6/(6+1)=0.85.

When the factor states of a patient are non-group B2 in bacterial phylogenicity (Gene17 (ne) 3), R type in levofloxacin susceptibility (Anti25=3), bloodWBC less than 7.8 (bloodWBC<7.8), and the value of UWBC more than 10 (UWBC_level (ne) 1), this rule claims that the patient will have RUTI with classification accuracy 16/(6+16)=0.72.

The decision rules of the DT analysis for development of RUTI after hospitalization. The 4 green boxes and 3 red boxes indicate the nodes of the decision rules with an accuracy rate higher than 0.85 and 0.70 for non RUTI and RUTI classification, respectively (sample size = 809).

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Machine learning to predict the development of recurrent urinary tract infection related to single uropathogen, Escherichia coli | Scientific Reports...

The more data, the more deep learning capacity – Innovation Origins

Why we write about this topic:

AI and Deep Learning are influencing our lives in a never before seen way. Albert van Breemen helps us understand the consequences.

Kivis algorithm is plain and simple: every second Friday of the month, together with Mikrocentrum, they invite a lecturer in the field of engineering for a talk about their field of expertise, followed by an opportunity to meet other engineers. Today, the floor in the AI Innovation Center at High tech Campus Eindhoven was for Albert van Breemen, CEO and CTO of VBTI, an AI engineering company that develops Deep Learning solutions for companies in agriculture and manufacturing. Van Breemen, whose company recently won a Gerard & Anton Award, guided his audience through the hidden tracks of Artificial Intelligence and deep learning.

VBTI has successfully applied deep learning technology to agricultural robots and harvest forecasting systems. This required the development of a dedicated platform to get deep learning operational: AutoDL. The platform has automated many of the lifecycle tasks of deep learning development; with the support of VDL, VBTI is now taking the technology to a new level.

VBTI introduces robots fitted with smart camera tech to agriculture

His work is all about making automation intelligent, Van Breemen says at the start of his lecture. We want to help industries like agriculture, manufacturing, logistics, and robotics in their transformation processes, using deep learning and computer vision. But first, what is deep learning?

Van Breemen presents a timeline showing three significant periods. Not many people are aware of it, but artificial intelligence was already mastered in the 1950s, by creating machines that could sense, reason, act, and adapt. In the 1980s, we had the second wave called Machine Learning. It was the age of the algorithms that used data to improve their performance. Neural networks were created. Only after 2005 can we speak of Deep Learning: we started training deep neural networks with big data.

Big data is crucial for this: the more data, the more deep learning options. But its not like humans are out of work because of this development, Van Breemen says. Most importantly, we need people to collect and select the data and annotate all this so the machine can actually learn from it. After these processes, the model training can start, and finally, its time for deployment.

Deep learning consumer successes can be found in autonomous driving, GO and chess achievements in GO and chess, or smart assistants like Siri or Alexa. And now, its time for the industrial domain to get into deep learning. VBTI/VDL is doing this by using AI to develop a cucumber leaf-cutting robot. Thats a really complex world because not one cucumber leaf or stem is the same, and still, the machine needs to recognize them exactly. All those variations make it difficult, the deep learning toolbox can make this process robust.

Van Breemen and his team have been working for years on the technology to support de de-leafing robot. Getting 80 percent accuracy is easy, but the last 20 percent is extremely difficult. You always wonder which and how much data should be collected and annotated, how you handle storage and versioning, and how you can tell what the quality of the data is. Van Breemen says he is happy and proud about the result, leading to an effective robot, but he also knows that this can never be the end of it. You never stop learning. You keep collecting new data more data is more deep learning capacity.

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The more data, the more deep learning capacity - Innovation Origins

Outlook on the Machine Learning in Life Sciences Global Market to 2027 – Featuring Alteryx, Anaconda, Canon Medical Systems and Imagen Technologies…

DUBLIN, Oct. 12, 2022 /PRNewswire/ --The "Global Markets for Machine Learning in the Life Sciences" report has been added to ResearchAndMarkets.com's offering.

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This report highlights the current and future market potential for machine learning in life sciences and provides a detailed analysis of the competitive environment, regulatory scenario, drivers, restraints, opportunities and trends in the market. The report also covers market projections from 2022 through 2027 and profiles key market players.

The publisher analyzes each technology in detail, determines major players and current market status, and presents forecasts of growth over the next five years. Scientific challenges and advances, including the latest trends, are highlighted. Government regulations, major collaborations, recent patents and factors affecting the industry from a global perspective are examined.

Key machine learning in life sciences technologies and products are analyzed to determine present and future market status, and growth is forecast from 2022 to 2027. An in-depth discussion of strategic alliances, industry structures, competitive dynamics, patents and market driving forces is also provided.

Artificial intelligence (AI) is a term used to identify a scientific field that covers the creation of machines (e.g., robots) as well as computer hardware and software aimed at reproducing wholly or in part the intelligent behavior of human beings. AI is considered a branch of cognitive computing, a term that refers to systems able to learn, reason and interact with humans. Cognitive computing is a combination of computer science and cognitive science.

ML algorithms are designed to perform tasks such data browsing, extracting information that is relevant to the scope of the task, discovering rules that govern the data, making decisions and predictions, and accomplishing specific instructions. As an example, ML is used in image recognition to identify the content of an image after the machine has been instructed to learn the differences among many different categories of images.

There are several types of ML algorithms, the most common of which are nearest neighbor, naive Bayes, decision trees, a priori algorithms, linear regression, case-based reasoning, hidden Markov models, support vector machines (SVMs), clustering, and artificial neural networks. Artificial neural networks (ANN) have achieved great popularity in recent years for high-level computing.

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They are modeled to act similarly to the human brain. The most basic type of ANN is the feedforward network, which is formed by an input layer, a hidden layer and an output layer, with data moving in one direction from the input layer to the output layer, while being transformed in the hidden layer.

Report Includes

32 data tables and 28 additional tables

A comprehensive overview and up-to-date analysis of the global markets for machine learning in life sciences industry

Analyses of the global market trends, with historic market revenue data for 2020 and 2021, estimates for 2022, and projections of compound annual growth rates (CAGRs) through 2027

Highlights of the current and future market potential for ML in life sciences application, and areas of focus to forecast this market into various segments and sub-segments

Estimation of the actual market size for machine learning in life sciences in USD million values, and corresponding market share analysis based on solutions offering, mode of deployment, application, and geographic region

Updated information on key market drivers and opportunities, industry shifts and regulations, and other demographic factors that will influence this market demand in the coming years (2022-2027)

Discussion of the viable technology drivers through a holistic review of various platform technologies for new and existing applications of machine learning in the life sciences areas

Identification of the major stakeholders and analysis of the competitive landscape based on recent developments and segmental revenues

Emphasis on the major growth strategies adopted by leading players of the global machine learning in life sciences market, their product launches, key acquisitions, and competitive benchmarking

Profile descriptions of the leading market players, including Alteryx Inc., Canon Medical Systems Corp., Hewlett Packard Enterprise (HPE), KNIME AG, Microsoft Corp., and Phillips Healthcare

Key Topics Covered:

Chapter 1 Introduction

Chapter 2 Summary and Highlights

Chapter 3 Market Overview 3.1 Introduction 3.1.1 Understanding Artificial Intelligence in Healthcare 3.1.2 Artificial Intelligence in Healthcare Evolution and Transition

Chapter 4 Impact of the Covid-19 Pandemic 4.1 Introduction 4.1.1 Impact of Covid-19 on the Market

Chapter 5 Market Dynamics 5.1 Market Drivers 5.1.1 Investment in Ai Health Sector 5.1.2 Rising Chronic Diseases 5.1.3 Advanced, Precise Results 5.1.4 Increasing Research and Development Budget 5.2 Market Restraints and Challenges 5.2.1 Reluctance Among Medical Practitioners to Adopt Ai-Based Technologies 5.2.2 Privacy and Security of User Data 5.2.3 Hackers and Machine Learning 5.2.4 Ambiguous Regulatory Guidelines for Medical Software 5.3 Market Opportunities 5.3.1 Untapped Potential in Emerging Markets 5.4 Value Chain Analysis

Chapter 6 Market Breakdown by Offering 6.1 Software 6.1.1 Market Size and Forecast 6.2 Services 6.2.1 Market Size and Forecast

Chapter 7 Market Breakdown by Deployment Mode 7.1 Cloud 7.1.1 Market Size and Forecast 7.2 On-Premises 7.2.1 Market Size and Forecast

Chapter 8 Market Breakdown by Application 8.1 Diagnosis 8.1.1 Market Size and Forecast 8.2 Therapy 8.2.1 Market Size and Forecast 8.3 Healthcare Management 8.3.1 Market Size and Forecast

Chapter 9 Market Breakdown by Region 9.1 Global Market 9.2 North America 9.2.1 U.S. 9.2.1 Canada 9.3 Europe 9.3.1 Germany 9.3.2 U.K. 9.3.3 France 9.3.4 Italy 9.3.5 Spain 9.3.6 Rest of Europe 9.4 Asia-Pacific 9.4.1 China 9.4.2 Japan 9.4.3 India 9.4.4 Rest of Asia-Pacific 9.5 Rest of the World

Chapter 10 Regulations and Finance 10.1 Regulatory Framework 10.1.1 American Diabetes Association's Standards of Medical Care in Diabetes 10.1.2 Ata Guidelines for Artificial Intelligence 10.1.3 Indian Ai Guidelines, Strategy, and Standards

Chapter 11 Competitive Landscape 11.1 Overview 11.1.1 Development 11.1.2 Cloud 11.1.3 Users 11.1.4 Parent Market: Global Artificial Intelligence Market

Chapter 12 Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/qc8qjo

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Outlook on the Machine Learning in Life Sciences Global Market to 2027 - Featuring Alteryx, Anaconda, Canon Medical Systems and Imagen Technologies...

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Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning...

Forensic Discovery Taps Reveal-Brainspace to Bolster its Analytics, AI and Machine Learning Capabilities – Business Wire

DENVER & CHICAGO--(BUSINESS WIRE)--Forensic Discovery, a leader in digital forensic and eDiscovery services for the legal industry and corporations, announced that it is expanding its technology offering with Reveal, the global provider of the leading AI-powered eDiscovery and investigations platform. Reveal uses adaptive AI, behavioural analysis, and pre-trained AI model libraries to help uncover connections and patterns buried in large volumes of unstructured data.

Forensic Discovery is excited to offer next generation Artificial Intelligence to its hosted review and data analytics services through use of Reveal, said Trent Walton, founder of Forensic Discovery. Our clients, which range from the Am Law 100 to the Fortune 500, will greatly benefit from having the power to investigate, review and produce their data in new ways, thereby reducing litigation costs.

Forensic Discovery will leverage the platform globally to unlock intelligence that will help clients mitigate risks across a range of areas including litigation, investigations, compliance, ethics, fraud, human resources, privacy and security.

As we continue to expand the depth and breadth of Reveals marketplace offerings, we are excited to partner with Forensic Discovery, a demonstrated leader in digital forensics and eDiscovery, said Wendell Jisa, Reveals CEO. By taking full advantage of Reveals powerful platform, Forensic Discovery now has access to the industrys leading SaaS-based, AI-powered technology stack, helping them and their clients solve their most complex problems with greater intelligence.

For more information about Reveal-Brainspace and its AI platform for legal, enterprise and government organizations, visit http://www.revealdata.com.

About Forensic Discovery

Forensic Discovery is a litigation case management firm with expertise in Digital Forensics, eDiscovery, and Expert Testimony. The company has developed a proprietary workflow that allows its clients to forensically collect, filter, review, and produce electronic evidence using a hosted review platform. With offices in Colorado, California and Texas, Forensic Discovery is a leader in digital forensic and eDiscovery services for the legal industry and corporations. Learn more about the companys offerings by visiting http://www.forensicdiscovery.expert.

About Reveal

Reveal-Brainspace is a global provider of the leading AI-powered eDiscovery platform. Fuelled by powerful AI technology and backed by the most experienced team of data scientists in the industry, Reveals cloud-based software offers a full suite of eDiscovery solutions all on one seamless platform. Users of Reveal include law firms, Fortune 500 corporations, legal service providers, government agencies and financial institutions in more than 40 countries across five continents. Featuring deployment options in the cloud or on-premises, an intuitive user design and multilingual user interfaces, Reveal is modernizing the practice of law, saving users time and money and offering them a competitive advantage. For more information, visit http://www.revealdata.com.

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Forensic Discovery Taps Reveal-Brainspace to Bolster its Analytics, AI and Machine Learning Capabilities - Business Wire

Hackers have stolen record $3 billion in cryptocurrency this year – CBS News

Hackers have stolen more than $3 billion in cryptocurrency so far this year, shattering the previous record of $2.1 billion set in 2021, according to blockchain analytics firm Chainalysis.

A big chunk of that $3 billion, around $718 million, was taken this month in 11 different hacks, Chainalysis said in a series of tweets posted Wednesday.

"October is now the biggest month in the biggest year ever for hacking activity, with more than half the month still to go," the company tweeted.

In past years, hackers focused their efforts on attacking crypto exchanges, but those companies have since strengthened their security, Chainalysis said. These days, cybercriminals are targeting "cross-chain bridges," which allow investors to transfer digital assets and data among different blockchains.

The bridges hold a lot of cryptocurrencies, providing a larger and more complex arena for hackers to infiltrate, according to cybersecurity experts.

"Cross-chain bridges remain a major target for hackers, with three bridges breached this month and nearly $600 million stolen, accounting for 82% of losses this month and 64% of losses all year," Chainalysis said.

Hackers initially made of with$570 million in cryptocurrency from Binance, but company officials have minimized the losses to under $100 million, its CEO said last week. Hackers also struck Nomad in August, reportedly taking nearly $200 million. Both the Binance and Nomad attackswere instances of hackers exploiting security flaws within the cross-chain bridge transaction protocols.

Crypto.com, known for its recent $700 million deal torename the former Staples Centerin Los Angeles, said in January that hackers managed to bypass its two-factor authentication system and withdraw funds from 483 customer accounts. Harmony lost about $100 million in ahack in June.Crypto platforms WormholeandRoninNetwork were also targets of hackers this year.

All told, Chainalysis said there have been 125 hacks so far this year.

Binance CEO Changpeng Zhaosaidin an interview with CNBC last week that the crypto industry is vulnerable to hackers whenever customers move assets from one blockchain to another, but the goal is to learn from what caused the hack and develop extra safeguards in the future.

Cryptocurrency is not federally regulated or FDIC insured like a bank account, which means if an account gets hacked, the government will not work to restore a customer's funds.

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Khristopher J. Brooks is a reporter for CBS MoneyWatch covering business, consumer and financial stories that range from economic inequality and housing issues to bankruptcies and the business of sports.

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Hackers have stolen record $3 billion in cryptocurrency this year - CBS News

Cryptocurrency firm advised by Philip Hammond withdraws UK application – The Guardian

A cryptocurrency firm that employs the former chancellor Philip Hammond as an adviser has withdrawn its application to operate in the UK, after struggling to win approval from the financial regulator.

The Guardian revealed earlier this year that Copper Technologies, in which Hammond holds a 0.5% stake, was considering seeking registration in Switzerland rather than the UK.

The company had been given temporary registration by the Financial Conduct Authority (FCA), pending approval of the controls it had put in place to prevent money laundering and terrorist financing.

Fintech company Revolut, which had also been placed on the FCAs temporary list, was awarded full registration for its UK crypto business last month.

But Copper Technologies has revealed, in accounts filed at Companies House, that it had withdrawn its application and moved UK customers to Switzerland, after winning approval there.

Hammond, who was chancellor between July 2016 and July 2019, has been critical of the UK for failing to set up a comprehensive regulatory framework governing cryptocurrencies.

Earlier this year he said it was frankly quite shocking that Britain was lagging behind other countries.

The FCAs regime for digital assets currently covers money laundering and terrorist financing but not specific aspects of cryptocurrency trading and investing.

Hammond, recruited by Copper Technologies as a senior adviser in 2021, has growth shares that were thought to be worth up to $15m (13m), based on reports by Bloomberg that the company was seeking a valuation of $3bn in a fundraising exercise.

The accounts show that Copper Technologies has raised $196m so far but the ultimate success of the fundraising and thus the valuation could be affected by a broad global sell-off of digital assets over the past year.

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In the meantime, losses at Copper, which provides digital currency infrastructure to other businesses, have increased from 3.6m to 14.3m, accounts show.

A spokesperson for the company said: Copper maintains open and active dialogue with regulators across the jurisdictions where we are operating, including of course with the FCA. Since gaining our membership to [Swiss body] VQF in May, we are pleased to be able to offer clients services from Switzerland.

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Cryptocurrency firm advised by Philip Hammond withdraws UK application - The Guardian