Category Archives: Deep Mind
Meet the Living Robot; PigeonBot With Real Feathers; DeepMind Introduces AlphaFold; PyTorch 1.4 Released – Synced
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Meet Xenobot, an Eerie New Kind of Programmable OrganismResearchers hope the living robots, made up of masses of cells working in coordination, can help unlock the mysteries of cellular communication.(WIRED)
PigeonBot Uses Real Feathers to Explore How Birds FlyIn a paper published in Science Robotics, researchers at Stanford University have presented some new work on understanding exactly how birds maintain control by morphing the shape of their wings. (Stanford University) / (IEEE SPECTRUM)
AlphaFold: Using AI for Scientific DiscoveryAlphaFold described in peer-reviewed papers now published in Nature and PROTEINS is the culmination of several years of work, and builds on decades of prior research using large genomic datasets to predict protein structure. (DeepMind)
PyTorch 1.4 Released, Domain Libraries UpdatedThe 1.4 release of PyTorch adds new capabilities, including the ability to do fine grain build level customization for PyTorch Mobile, and new experimental features including support for model parallel training and Java language bindings.(PyTorch)
ImagineNet: Restyling Apps Using Neural Style Transfer Researchers propose a neural solution by adding a new loss term to the original formulation, which minimizes the squared error in the uncentered cross-covariance of features from different levels in a CNN between the style and output images.(Stanford University)
Using Neural Networks to Solve Advanced Mathematics EquationsFacebook AI has built the first AI system that can solve advanced mathematics equations using symbolic reasoning. Researchers leverage proven techniques in neural machine translation (NMT), training models to essentially translate problems into solutions.(Facebook AI)
Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples Researchers present a preliminary design for an interactive visualization tool, CEB, to reveal biases in a commonly used AI method, Neural Networks (NN). CEB combines counterfactual examples and abstraction of an NN decision process to empower non-experts to detect bias.(Drexel University & IT University of Copenhagen)
AWS Introduces Open Source AutoML Toolkit AutoGluonAutoGluon is designed to be an easy-to-use and easy-to-extend AutoML toolkit, suitable for both machine learning beginners and experts. It enables prototyping deep learning models with a few lines; automatic hyperparameter tuning, model selection and data processing; and automatic utilization of SOTA deep learning models.(Synced)
EmotionCues: AI Knows Whether Students Are Paying AttentionA research team from the Hong Kong University of Science and Technology and Harbin Engineering University has adopted facial recognition technology to analyze students emotions in the classroom through a visual analytics system called EmotionCues.(Synced)
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The human brain often recalls past memories (seemingly) unprompted. As we go throughout our day, we have spontaneous flashes of memory from our lives. While this spontaneous conjuration of memories has long been of interest to neuroscientists, AI research company DeepMind recently published a paper detailing how an AI of theirs replicated this strange pattern of recall.
The conjuration of memories in the brain, neural replay, is tightly linked with the hippocampus. The hippocampus is a seahorse-shaped formation in the brain that belongs to the limbic system, and it is associated with the formation of new memories, as well as the emotions that memories spark. Current theories on the role of the hippocampi (there is one in each hemisphere of the brain), state that different regions of the hippocampus are responsible for the handling of different types of memories. For instance, spatial memory is believed to be handled in the rear region of the hippocampus.
As reported by Jesus Rodriguez on Medium, Dr. John OKeefe is responsible for many contributions to our understanding of the hippocampus, including the hippocampal place cells. The place cells in the hippocampus are triggered by stimuli in a specific environment. As an example, experiments on rats showed that specific neurons would fire when the rats ran through certain portions of a track. Researchers continued to monitor the rats even when they were resting, and they found that the same patterns of neurons denoting a portion of the maze would fire, although they fired at an accelerated speed. The rats seemed to be replaying the memories of the maze in their minds.
In humans, recalling memories is an important part of the learning process, but when trying to enable AI to learn, it is difficult to recreate the phenomenon.
The DeepMind team set about trying to recreate the phenomenon of recall using reinforcement learning. Reinforcement learning algorithms work by getting feedback from their interactions with the environment around them, getting rewarded whenever they take actions that bring them closer to the desired goal. In this context, the reinforcement learning agent records events and then plays them back at later times, with the system being reinforced to improve how efficiently it ends up recalling past experiences.
DeepMind added the replaying of experiences to a reinforcement learning algorithm using a replay buffer that would playback memories/recorded experiences to the system at specific times. Some versions of the system had the experiences played back in random orders while other models had pre-selected playback orders. While the researchers experimented with the order of playback for the reinforcement agents, they also experimented with different methods of replaying the experiences themselves.
There are two primary methods that are used to provide reinforcement algorithms with recalled experiences. These methods are the imagination replay method and the movie replay method. The DeepMind paper uses an analogy to describe both of the strategies:
Suppose you come home and, to your surprise and dismay, discover water pooling on your beautiful wooden floors. Stepping into the dining room, you find a broken vase. Then you hear a whimper, and you glance out the patio door to see your dog looking very guilty.
As reported by Rodriguez, the imagination replay method doesnt record the events in the order that they were experienced. Rather, a probable cause between the events is inferred. The events are inferred based on the agents understanding of the world. Meanwhile, the movie replay method stores memories in the order in which the events occurred, and replays the sequence of stimuli spilled water, broken vase, dog. The chronological ordering of events is preserved.
Research from the field of neuroscience implies that the movie replay method is integral to the creation of associations between concepts and the connection of neurons between events. Yet the imagination replay method could help the agent create new sequences when it reasons by analogy. For instance, the agent could reason that if a barrel is to oil as a vase is to water, a barrel could be spilled by a factory robot instead of a dog. Indeed, when DeepMind probed further into the possibilities of the imagination replay method, they found that their learning agent was able to create impressive, innovative sequences by taking previous experiences into account.
Most of the current progress being made in the area of reinforcement learning memory is being made with the movie strategy, although researchers have recently begun to make progress with the imagination strategy. Research into both methods of AI memory can not only enable better performance from reinforcement learning agents, but they can also help us gain new insight into how the human mind might function.
The algorithm was developed by experts of the company DeepMind, solve two complex tasks in the field of biology. The network will investigate the processes of protein folding and the operation of the human brain.
Scientists believe that some of the program, based on machine learning can, like the human brain, to work on the reward system. Usually it is based on the production of dopamine. Experiments on mice showed that the probable scheme of award to build certain neurons.
The neural network also needs to predict a proteins fold. The work is to understand the structures of the compounds with amino acid composition. The problem is particularly acute in medicine and biology so as to identify all configurations of the protein, scientists will need at least 13.8 billion years.
Demis of Hassabis created DeepMind to develop algorithms to beat people in chess. Now the company has delivered more challenging goal is the use of artificial intelligence to solve difficult problems with science.
Natasha Kumar is a general assignment reporter at the Times Hub. She has covered sports, entertainment and many other beats in her journalism career, and has lived in Manhattan for more than 8 years. She studies in University of Calcutta. Natasha has appeared periodically on national television shows and has been published in (among others) Hindustan Times, Times of India
Go here to see the original:
The DeepMind algorithm to solve two complex problems of biology - The Times Hub
To commemorate the Rev. Martin Luther King Jr., Nicholas Thomas and more than 100 other volunteers will board up vacant houses, install school safety signs and make other improvements to a Detroit neighborhood.
Their mission is to celebrate Kings legacy by being good neighbors and helping lift up a primarily black school in one of the poorer areas of the city.
As Thomas fans out across the neighborhood with hammer and nails, Kings legacy of peace and racial and social justice will be foremost in his mind. But at the same time, hes struggling to come to grips with the deep racial divisions roiling the nation under President Donald Trump.
Dr. King wanted unity. We have Trump separating immigrants ... the wall, said the 19-year-old Thomas who is black.
As the nation marks the holiday honoring King, the mood surrounding it is overshadowed by deteriorating race relations in an election season that has seen one candidate of color after another quit the 2020 presidential race.
Two black candidates U.S. Sens. Kamala Harris and Cory Booker and the lone candidate of Hispanic ancestry, former Housing Secretary Julian Castro, have dropped out of the Democratic race for the White House.
That scares me a lot, said Deja Hood, 21, of Chicago, a senior at Eastern Michigan University. Who is going to really back our voicing? You cant understand a minority if youve never been in a minority situation. Even though you can advocate for us all day, you could never understand the issues we go through on a daily basis.
Booker, Harris and Castro struggled with raising money and with polling. Asian American entrepreneur Andrew Yang, Hawaii Rep. Tulsi Gabbard, a Samoan American, and black former Massachusetts Gov. Deval Patrick remain in the race but are not considered top contenders for the Democratic nomination.
The front-runners in the field are all white men and women.
Its disappointing, but really not surprising. You look at it and think, damn, now what? said Xavier Cheatum, 22, an African American senior at Eastern Michigan who along with Hood is participating in King events on the schools Ypsilanti campus, west of Detroit.
People have the right to be and should be concerned about the state of race relations and the way people of color, in particular, are being treated, said Jill Savitt, president of the National Center for Civil and Human Rights in Atlanta.
What were seeing right now, its very public and people are showing their hatred openly, but it doesnt mean it wasnt there, Savitt said. There is a coming realization in our country. We have to come to a reckoning about our past and the truth about our history from slavery to the lynching era to Jim Crow. Only with real honesty about our situation can we come to some reconciliation and move on to fulfill Kings hope and dream of a real, peaceful multicultural democracy.
It doesnt help when elected leaders dont or are slow to stand against hate and intolerance, she added.
Trump referred last year to a predominantly African American congressional district that includes Baltimore as a disgusting, rat and rodent infested mess. During a 2018 immigration conversation in the Oval Office, he disparaged Haiti and some African countries with coarse language.
And following a 2017 clash between white nationalist demonstrators and counterprotesters in Charlottesville, Virginia, Trump said there were very fine people on both sides and that there was blame on both sides. One anti-racism activist was killed.
In 2018, there were more than 7,000 single-bias incidents reported by law enforcement, according to FBI hate crime statistics. More than 53% of the offenders were white, while 24% were black. Nearly 60% of the incidents involved race, ethnicity and ancestry.
Racism has long been a way for people to maintain their power, Savitt said. Manipulating peoples fears and anxieties is the way you do that. The Trump administration has certainly fanned the flames.
Trump is trying to court black voters, knowing that he isnt likely to win them over en masse but could chip into Democratic advantages if he wins more black support in critical swing states. His campaign has stepped up outreach efforts, including to African Americans and Latinos, marking a departure from 2016 when Trumps volunteer National Diversity Coalition struggled to make an impact.
The campaign already has spent more than $1 million on black outreach, including radio, print and online advertising in dozens of markets since the coalitions launch, the campaign has said.
Only 6% of African American voters went for Trump in the 2016 election, according to a Pew Research Center analysis. Trumps message to black voters in that campaign was: What have you got to lose? Supporters now say they have a record to point to, including the low black unemployment rate and investments in historically black colleges and universities.
A Washington Post-Ipsos poll of African Americans in early January found that 90% disapprove of Trumps job performance and 83% say Trump is racist.
Laying it all in Trumps lap is unfair, said Carol Swain, an advisory board member to the national Black Voices for Trump.
With Trump, he has pushed the American nationalist identity that I think tamps down the kind of conflicts we would have, said Swain, who is black and has taught political science at Vanderbilt and Princeton universities. He has pushed patriotism over race and that benefits our country.
Faith Morris, chief marketing and external affairs officer for the National Civil Rights Museum in Memphis, Tennessee, doesnt see it that way.
Its definitely a white America. A black America. A Hispanic America, Morris said. And theres a very broken line that connects the different Americas. In 2020, we still feel the oppressive issues that Dr. King fought against. He focused on the same things were focusing on now.
Jacob Sklarsky recently read a book about King and the civil rights movement to students in his second-grade Chicago Public Schools class.
To look at the faces of young black kids who are sometimes hearing about this history for the first time, they are distressed by it, said Sklarsky, who is white and a member of KAM Isaiah Israel, a Jewish congregation in Chicago.
They were very relieved at the end because, in a way, it was all worth it, Sklarsky said. It gives us some hope, but its also very sad that were not anywhere near what King dreamed of.
Continue reading here:
U.S. marks King holiday amid fears of deep racial divisions - The Westerly Sun
Nick Cave (Photo by Fred Duval/Getty Images)
The growing weight of evidence of its effectiveness in tackling all manner of mental and physical maladies make it an attractive prospect for all of the people whose internal monologue isnt as measured or as supportive as theyd like it to be.
While business leaders have praised the way meditation helps them to deal with extreme levels of stress, the more artistic types have long waxed lyrical about it as a pathway to creativity.
I had attempted meditation in the past, in the less than stressful environs of Brighton Buddhist Centre, but enjoyable and pleasant as it was, I was unable to immediately tame my monkey mind, and blundered on with the chaotic maelstrom of life.
But then in June last year I was lucky enough to see Nick Cave perform a memorable show at the Brighton Dome, in which the engaging singer-songwriter espoused the benefits of Transcendental Meditation (TM), and cited it as a contributory factor in his continued recovery following the sudden loss of his 15-year-old son Arthur.
Cave has always been a hugely charismatic figure, but his openness and generosity of spirit in the face of such a harrowing experience, was remarkable, and he provided an incredibly positive and persuasive advert for TM.
Inspired by the words and positive actions of the former Godfather of Goth, I got in touch with the Brighton and Hove branch of Transcendental Meditation.
The worldwide organisation, developed by Maharishi Mahesh Yogi, and famously popularised in the UK by The Beatles, has taught its techniques to more than ten million people of all ages, cultures and religions, who learn to meditate in the hope of attaining inner peace and wellness.
Those techniques are simplicity itself, based on the silent recital of a mantra, whilst seated, for 20 minutes, twice a day.
Film director David Lynch, a passionate advocate of TM, has not missed a single twice-daily meditation in more than 45 years, and has said it has given him effortless access to unlimited reserves of energy, creativity and happiness deep within.
For Sussex residents the pursuit of that happiness begins in the suitably serene surroundings where Mark Heath teaches the principles of Transcendental Meditation in the cosiest and most welcoming of venues at the Brighton and Hove TM Centre on the northern outskirts of Brighton.
An enjoyable one-to-one session included a simple ceremony where I was given my very own personal mantra and immediately encouraged to embark upon the aforementioned twice-daily meditations.
It was easy as it sounds. And within days, something resembling a meditative state, was, at least intermittently, accessible.
The following week I attended small group sessions with other people who had also just begun their Transcendental Meditation journey, where we discussed our early experiences and Mark, with great enthusiasm and patience, answered our questions.
He also explained the theoretical principles, which he said would subconsciously stop the brain questioning the process and help TM to do its thing.
The mantra is key, obviously, and TM accepts that you wont immediately be able to banish all other thoughts, but in the moments between the mantra and random thought, will, hopefully, be the moments when you transcend thought.
There were also plenty of great group meditations (which are available on an open-ended basis after the course concluded) and Mark proved to be the perfect guide, full of warmth and the second great advert for TM Ive encountered.
A month on from completing the course, and looking forward to more group meditations, Im largely sticking to the twice-daily programme (sometimes life gets in the way) and noticing some benefits and hopeful for more in the future.
Im miles away from the beatific glow which seems to come across Marks face the moment he begins to meditate, and I havent quite reached the joyful depths that David Lynch luxuriates in on a twice-daily basis, but Im happy in the spiritual beginners pool and poised to go deeper...
To find out more about TM vist: [uk.tm.org/brighton|uk.tm.org/brighton]
AI can fight climate change but there’s a catch: Optimization doesn’t automatically equal emissions reduction – ZDNet
The good news for planet earth is that artificial intelligence has some brilliant tools that may help slow or reverse global warming. The bad news is that not much will happen unless AI somehow finds the right goals, what's known as the "objective function."
A workshop on AI in climate change in mid-December gathered hundreds of scholars in Vancouver during the NeurIPS AI conference, including some of the Illuminati of machine learning. The event was sponsored by Google's DeepMind, Microsoft, andElementAI, the AI software and services firm co-founded by Yoshua Bengio, a star in the field of deep learning. Organizers were fromClimate Change AI, a group of volunteer researchers from institutions around the world.
The participants discussed numerous ways to implement neural networks for climate science, including real-time weather predictions, making buildings more energy-efficient, and designing better materials for solar panels.
Here's the catch: All of the projects specify some task to be optimized that is not directly tied to reducing greenhouse gas emissions. But reduction of green house gas emissions is the stated goal of all global warming mitigation, and without it, it's not clear meaningful change can happen.
A report last year by the Intergovernmental Panel on Climate Change of the United Nations started that "without increased and urgent mitigation ambition in the coming years, leading to a sharp decline in greenhouse gas emissions by 2030, global warming will surpass 1.5C in the following decades, leading to irreversible loss of the most fragile ecosystems, and crisis after crisis for the most vulnerable people and societies."
The keynote speaker at the event, Jeff Dean of Google, put the problem bluntly. He offered a chart, based on data from the IPCC report, showing how the planet has to take steps to reduce annual carbon dioxide emissions by as much as 10% a year, amounting to hundreds of "gigatonnes" worth of reduction in CO2, whereas the world is currently increasing CO2 by a couple percent per annum. This has to happen in the next decade to avoid those irreversible effects the IPCC speaks of. "We are effectively running out of time to take action," said Dean.
Also: An AI payout? Should companies remunerate society for lost jobs?
Many of the fifty two papers accepted for the workshop are breathtaking in the ingenuity with which they apply machine learning to climate issues, but they are far from the task of actually reducing greenhouse gas emissions.
Google's Jeff Dean put up a slide of data on the needed carbon emissions reductions, based on work of the U.N.'s Intergovernmental Panel on Climate Change. "We are effectively running out of time to take action," said Dean.
For example, a paper authored by scientists at GE Global Research, the Georgia Institute of Technology, and others, and given a "best paper" recommendation, employs something called an "invertible residual network," a technique that was developed at Google's DeepMind in recent years. The I-ResNet program can ingest pictures of clouds at 1 kilometer in resolution and, going pixel by pixel, categorize what type of cloud it is -- "Altostratus," "Nimbostratus," "Deep Convection," etc. Types of clouds in the world affect climate models, so you can't actually model climate with great accuracy without knowing about which types are present and to what extent.
Graphic from the paper "Cumulo: A Dataset for Learning Cloud Classes," by Valentina Zantedeschi et. al.
Such work has the potential to improve forecasting, but on its own it obviously is far from actually proposing action that will lead to a reduction in greenhouse gases. A lot of the work has that quality: it is laying the groundwork for years of research but it's not always clear how an optimization will lead to emissions reductions.
In fact, the organizers, lead by David Rolnick, a postdoctoral research fellow at the University of Pennsylvania, published a 100-page report this past summer that is chock full of fascinating projects, such as improving energy grid forecasting, or better forecasting of road traffic, or how to design better agriculture. In every one of those cases, a single optimization may not lead to any emissions reduction. For example, improving the "shared mobility" culture, such as Uber and Lyft, by making it more efficient, can potentially lead to more miles driven overall, as Lynn Kaack, a scholar with ETH Zrich points out in a piece on machine learning in transportation. This is known as the "Jevons Paradox," which they describe as a "situation where increased efficiency nonetheless results in higher overall demand." In other words, optimizing something for the purposes of productivity or for the sake of increased profits can actually worsen the greenhouse gas situation.
ZDNet reached out to panelists and to Rolnick and the other organizers by email. Representatives for Yoshua Bengio, and Andrew Ng of LandingAI, said that they could not respond in time for this article. The other organizers did not reply to multiple emails.
However, there is interesting perspective to be gained from the panel discussion that was held that day, involving Bengio and Ng and Dean, along with Carla Gomes of Cornell University and Lester Mackey of Microsoft. One of the organizers, Priya Donti, a doctoral student at Carnegie Mellon in computer science and public policy, asked the panelists an insightful question: How can AI as a discipline incentivize work on climate change given that the focus for the discipline is often on the number of papers published versus the tons of carbon reduced?
Bengio replied, "change your objective function," which elicited a lot of laughter. "The sort of projects we're talking about in this workshop can potentially be much more impactful than one more incremental improvement in GANs, or something."
Carla Gomes, center, who runs the "Computational Sustainability" program at Cornell University, flanked by Andrew Ng of LandingAI, left, and Lester Mackey of Microsoft, right. AI, said Gomes, has been "unfortunately developed for a single objective," and for ethical AI, she suggested, "we should really develop systems that can understand the impacts across different dimensions."
It was a wry observation about the field, but the panelists acknowledged a deeper problem, that merely making good neural networks won't lead to emissions reduction on its own. Rolnick asked the panelists what should be done about AI that improves fossil fuel discovery, thereby potentially leading to a Jevon's Paradox of increased CO2. Bengio replied, again, to much laughter, "public shaming." Gomes replied that AI has "unfortunately developed for a single objective [...] we should really develop systems that can understand the impacts across different dimensions."
Even that may not be enough. AI may need some external forces to direct and shape its optimizations. That may mean aligning the cost benefits of "smarter everything" -- IoT, smart cities, ride sharing, etc. -- with the goal of emissions reduction. And that may require increased regulation, if private enterprise can't commit itself in earnest.
It's easy to be both thrilled with the work on display in December and also discouraged by the lack of imminent progress in emissions reduction. However, one of the invited speakers, Felix Creutzig, who is an author of the IPCC report, had a more upbeat view of the big picture.
Creutzig was asked by an audience member if the field is just fooling itself, "wasting time digressing from the important issues that are going wrong in policy?"
"I would be not too pessimistic about it," he replied. "We have technologies that are already available" such as electric vehicles, "and there is a lot of pressure to change, so I wouldn't be too pessimistic about anything not happening."
Update: Following the publication of the article, organizer Priya Donti was in touch via email. Donti writes that "there is still much work to be done, both within and outside of machine learning" and that the kinds of work shown at the workshop need to be "applied in parallel to (or to accelerate) action of other kinds, such as policy." Donti also refers to multiple "practical challenges" for the field. "These include forging meaningful connections between ML practitioners and those from other relevant fields, unifying and standardizing data from disparate sources, integrating proposed solutions with legacy systems, and changing incentives within the ML field to encourage impactful work on climate change."
NEW YORK, NY-AKA, the world's leading hotel residence brand, wants to encourage people to create new positive habits and break old bad habits to kick-off 2020. Seventy percent of American adults have at least one unhealthy habit that can shorten their lifespan, according to the Institute for Health Metrics and Evaluation at the University of Washington.
To help residents replace negative habits with new, healthier behavior, AKA has created the program, "Own It!: Own Making a Change & Own the New Behavior," to help guide travelers and residents to a more positive and uplifting lifestyle - and commit to it long-term. After all, it's not easy to banish bad habits.
Given AKA's specialty in weekly and monthly stays, residents have the time that's needed to form a habit and replace bad behavior. An AKA Resident Services Team Member will speak with a resident any time before or during a stay to find out what bad habit they'd like to crack or what positive habit they'd like to introduce into their life, to help uplift their mind, body and soul. Based on these discussions, the AKA Resident Services Team Member will suggest a program with the right coaches, classes or activities based on the resident and the city. (AKA has properties in NYC, D.C., Philadelphia, L.A. and London).
Here are three common bad habits with suggestions for casting them off when staying at any AKA in NYC.
Spending too much time on social media is almost a given today. But hours of scrolling through Twitter, Instagram and Facebook can result in a downfall to your physical and mental health, including lack of sleep and low self-esteem.
AKA wants to help you take back your winning confidence by engaging in new interests that leave social media behind. These programs will take you away from your phone, while helping you gain a new perspective and a renewed connection with yourself.
Floral design classes at Flower School New York will keep your hands too occupied to be checking your mobile screen. Flowers have a positive impact on our emotional health. You'll feel your creativity flowing and a deeper connection to nature, something we are missing when our faces are glued to our phones' radio frequencies.
2. Want to Incorporate More Exercise into Your Routine and Lifestyle?
With the advent of streaming platforms, like Netflix, Amazon and Hulu, binge-watching has become the new normal, which, for many people, means that physical activity takes a back seat. Exercise helps control weight, combat disease and boost mood, energy and sleep. Here are four ways to bring the elixir of exercise back into your life:
Boxing-inspired, group fitness workout at RUMBLE, incorporates the best principles of boxing, strength training and metabolic conditioning to help stimulate the mind and body.
3-in-1 wellness experiences at Aqua Studio, where aqua-cycling classes provide a saltwater massage, cardio and strength training, as well as healing therapy.
Shock yourself out of laziness and into a fitness routine at Shock Therapy Fitness NYC where strength and metabolism classes will give you mood-boosting, muscle-toning and fat-burning results that will make you want to keep up the good work and feeling.
Study after study finds that sleep and health go hand in hand and failing to keep proper sleep habits in mind could be a recipe for disaster. Here are mindful programs to help put your sleep patterns back on the right track and fight insomnia:
Daily meditation classes with MNDFL at MNDFL or in your suite to embark on a Zen-filled journey that will help you enter a state of deep relaxation, reduce stress and develop mindfulness.
Two appointments at WTHN, New York City's favorite modern acupuncture and healing studio, where a menu of services will help relax your mind, restore balance, and enhance your sleep and overall well-being.
For more information on how to book Own It!, visit: https://www.stayaka.com/ownit.
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Companies are implementing new technologies faster than ever in the race to remain competitive, often without understanding the inherent risks.
In response to a growing need to raise awareness about the risks associated with artificial intelligence, the World Economic Forum, together with the Centre for the Fourth Industrial Revolution Network Fellows from Accenture, BBVA, IBM and Suntory Holdings, worked with more than 100 companies and technology experts over the past year to create the 'Empowering AI Toolkit'. Developed with the structure of a company board meeting in mind, the toolkit provides a framework for mapping AI policy to company objectives and priorities.
Any board director reading through WEF's Empowering AI Toolkit will find it valuable not because it delivers any silver bullets, but because it can provide much-needed context and direction to AI policy discussions - without having to hire expensive consultants.
The new framework identifies seven priorities, like brand strategy and cybersecurity, to be considered from an ethics, risk, audit and governance point of view. The toolkit was designed to mimic how board committees and organisations typically approach ethics, policy and risk.
Artificial intelligence promises to solve some of the most pressing issues faced by society, from ensuring fairer trade and reducing consumer waste, to predicting natural disasters and providing early diagnosis for cancer patients. But scandals such as big data breaches, exposed bias in computer algorithms and new solutions that threaten jobs can destroy brands and stock prices and irreparably damage public trust.
Facebooks 2018 Cambridge Analytica data crisis opened the world's eyes to the risks of trusting the private sector with detailed personal data. The fact that an otherwise unknown London analytics company had drawn data on 50 million Facebook users without their permission not only drew public backlash, it sent Facebook's market value plunging $50 billion within a week of the episode being reported.
There is some awareness that new technologies can wreak havoc if not used carefully - but there isnt enough.
In addition to Facebook's Cambridge Analytica woes, there have been a number of high-profile revelations that artificial intelligence systems used by both government and business have applied hidden bias when informing decisions that affect people's lives. These include a number of cases where algorithms used by big companies in recruitment have been biased based on the race or gender of job candidates.
There is some awareness that new technologies can wreak havoc if not used carefully - but there isnt enough. And it can challenge corporate boards to predict where a pitfall may present itself on a companys path to becoming more tech-savvy.
Despite all the warning signs, there remains an "it can't happen here" attitude. Customer experience company Genesys recently asked more than five thousand employers in six countries about their opinions about AI and found that 54 per cent were not concerned about the unethical use of AI in their companies.
Many corporations have established AI working groups, ethics boards and special committees to advise on policy, risks and strategy. A new KPMG survey found that 44 per cent of businesses surveyed claimed to have implemented an AI code of ethics and another 30 per cent said that they are working on one.Since AI is an emerging technology, new risks are emerging too. Any company could use a road map.
One of today's biggest AI risks for corporations is the use of, as WEF calls them, inscrutable black box algorithms. Simply put, most algorithms work in a manner only understood by the programmers who developed them. These algorithms are often considered to be valuable intellectual property, further reinforcing the need to keep their inner-workings a secret and thus removed from scrutiny and governance.
There are already a number of collaborations, groups and institutes that are helping to address some of these issues. The non-profit coalition Partnership on AI, founded by tech giants Amazon, DeepMind, Facebook, Google, IBM and Microsoft, was established to research best practices to ensure that AI systems serve society. Last year, Harvard Kennedy Schools Belfer Center for Science and International Affairs convened the inaugural meeting of The Council on the Responsible Use of Artificial Intelligence, bringing together stakeholders from government, business, academia and society to examine policymaking for AI usage.
However, the speed and ubiquitous nature of artificial intelligence mean that even accurately defining certain risks remains a challenge. Even the best policies must allow for change. The good news is that WEF's new AI toolkit is available free-of-charge and so could prove to be of immediate value to commercial policymakers the world over.
Updated: January 20, 2020 12:07 PM
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New AI toolkit from the World Economic Forum is promising because it's free - The National
Google’s DeepMind AI outperforms doctors in identifying breast cancer from X-ray images – Business Insider UK
Researchers from Imperial College London and Google Health this week published newresearchthat shows DeepMind's medical AI system can outperform doctors on identifying breast cancer from X-ray images,per CNBC.
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The study involved training the AI algorithm on 29,000 anonymized images from breast cancer screenings of women from the UK and the US, and the results were compared against the performance of radiologists from both countries. When compared with radiologists from the US, DeepMind's AI system reduced false positive rates (when an image is falsely identified as abnormal) by nearly 6% and false negative rates (when a cancer is missed) by over 9% meaning there were more than 2,600 cases where a radiologist did not flag breast cancer, but an AI might have.
DeepMind's study joins a growing body of research that demonstrates AI's potential to transform radiology and improve patient outcomes.The FDAapprovedmore AI algorithms for radiology between 2017 and 2018 than for other use applications,perthe Regulatory Affairs Professionals Society: For example,GEearned approval for its AI-powered portable X-ray capable of slashing time to diagnosis for a collapsed lung from nine hours using traditional methods to a mere 15 minutes.
And AI has also proven to be on par or better than medical professionals in terms of accuracy: A 2018studyfrom Stanford tested an AI algorithm for radiology and found that the system was capable of performing as well or better than a team of three radiologists on 11 of 14 tested pathologies, accurately diagnosing 420 X-rays in 90 seconds while the team of live doctors took several hours to do the same.
However, research has shown that AI performs best when it complements traditional, human intelligence rather than supplants it entirely.While some, like investment mogulVinod Khosla, believe radiologists are an endangered species in healthcare, a follow-upstudyfrom many of the same researchers involved in the 2018 Stanford study suggests that "human-in-the-loop" workflows that utilize AI as a time-saving triage tool perform better than either AI or human doctors on their own.
As DeepMind works to secure widespread adoption of its AI in healthcare, it'll likely want to do more to address patient privacy concerns surrounding its parent company, Google.Google has come under fire in the last year for its use of patient health data, most notably around its secretive "Project Nightingale" tie-up with major US health system Ascension: It surfaced in November of 2019 that Google was conducting research using personally identifiable patient information unbeknownst to Ascension doctors or patients.
Given that only20%of US adults say they'd trust AI-generated health advice and that there's a growingdistrustof big tech among US consumers, DeepMind will likely need to provide more assurances to hospitals and patients regarding data sharing practices involving sensitive patient health info if it wants to attract wary provider partners.
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An instant 2nd opinion: Google’s DeepMind AI bests doctors at breast cancer screening – FierceBiotech
Googles DeepMind team showed that it can outperform trained radiologists in spotting cases of breast cancer, and that its artificial intelligence is capable of providing an independent, automated and immediate second opinion.
By using programs trained on 2D and 3D mammography images from nearly 30,000 women in the U.S. and the U.K., DeepMinds system was able to identify those that had their cancer confirmed within the following year via a tissue biopsy or subsequent X-rays.
Among patients from the U.S., the AI cut the number of people incorrectly referred for further screening with a false-positive result by 5.7%while also detecting 9.4% of potentially missed breast cancer cases. Published in Nature, the study said it surpassed the work of six independent physicians.
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Additionally, Googles researchers simulated its performance in automating the double-reading screening process employed in U.K. hospitals. By only bringing in a second human when the AI and the first clinician disagreed, they found the program was able to reduce the workload of the backup reader by 88%, while still maintaining the standard of care and providing on-the-spot feedback.
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The studys authorssome of whom had been recently reappropriated into Googles wider healthcare efforts over the past yearsaid that the work of interpreting mammograms can still be challenging, with differences in patients breast density as well as variabilities among experts. The paper was the result of a collaboration with Cancer Research UK, Northwestern University and the NHS Royal Surrey County Hospital.
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Clinical trials will still be needed to judge its performance in a real-world setting, as the system did not include all the different mammography technologies in use today, and most images were obtained from a single manufacturers systemaccording to an accompanying editorial in Nature by Etta Pisano, chief research officer of the American College of Radiology and a professor at Beth Israel Deaconess Medical Center at Harvard Medical School.
In addition, it would be essential to develop a mechanism for monitoring the performance of the AI system as it learns from cases it encounters, as occurs in machine-learning algorithms, Pisano wrote. Such performance metrics would need to be available to those using these tools, in case performance deteriorates over time.