Many white-collar workers in Bend have made the transition in the last month from a bustling office atmosphere to the chaos and comfort of working from home.

A recent presentation from members of Bend Young Professionals offers some tips and tricks for maintaining productivity and a healthy attitude during the coronavirus crisis.

Fromcommon-sense tips like blocking out time to turn off your phone, to how to transform turn a task list into a morning ritual, three local work-from-home veterans shared their recommendations for getting the most out of your day in the middle of a pandemic and recession.

While home environments vary, the primary challenge of working from home remains the same: How to sustain a productive and focused workday, without the motivation and accountability of actually going to work?

Dave Salcicciolithe chief development officer at Coachwell, a leadership and business coaching organizationsuggested developing new systematic rituals to help you maintain spiritual, physical and emotional energy in the midst of a scary and unpredictable time.

All of the rhythms that we had are all up in the air, he said. They helped us function at our peak, they helped us do really well in our work. We have to establish a new lifestyle to survive and thrive in this moment.

Establish a structure: get up, get dressed, put on your shoes and walk out the door if it helps you to get into the right frame of mind, he said.

Find a physical space in the house as your home office, he suggested.

Communicate this space to your spouse and your family so you are not bombarded with distractions and interruptions, Winn said.

[Your workspace] is not being on the couch with your sweatpants on, Winn said. That only works for a certain amount of time. When you enter that space your are in work mode, when you leave you are in family [or relaxation] mode.

How do you set boundaries when youre in absolute triage mode, when youre working really long days, working more, not less, said Gabriel Davis, the second panelist. Davis has worked remotely for three years in Bend as a growth strategist for a California-based marketing company.

When work is always there, always associated with our place at home, come up with some kind of activity that says Im shutting down for the day, letting the day go,'" he said. "I dont have to open the laptop at 7pm; I can start it tomorrow morning and the world isnt going to end.

At the close of the workday, write down what needs to get done the following day.

Davis added that a quick and easy hack for day planning is starting a shared Google calendar that is open to everyone in the office so co-workers know the best times of the day to check in, and can see your workflow. Davis said hes very transparent about his time and adds time slots for homeschooling his children.

He explained that it is common for people when they first begin working from home to feel nervous that their boss cant see them working, when in fact they are actually more productive.

People are probably very thankful [for their jobs] and want to hang on to that and may be overproducing, Davis said.

Even if you are working unconventional hours, it shows your bosses you are putting the time in, he said.

Document your progress each day to get an objective perspective of what you did, he said.

Working from home tends to be more about results, Davis said. Bosses will be looking for productivity. Treat the work you do as a business, even if you only serve one, the person you are working for.

Winn said that because you are working from home, it's important to clear away distractions.

Get notifications off your phone, he said. Turn it off if you can when you are in the middle of a task.

Davis advised that during this socially isolated time, to try to stay connected with like-minded people across different industries. It helps maintain a sense of camaraderie while also offering fresh perspectives on how to adapt your business during the pandemic.

Join networking groups talk through ideas engage it takes the burden off your own shoulders, Davis said. As we come out of this lock down, the people that leaned in on community, these are going to be the biggest leaders and winners coming out of this.

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Master Working From Home, From Three Bendites Who Do It All the Time - The Source Weekly

Floyd Mayweather Jr. is beginning a post-fight career in coaching and it's a move inspired by the recent death of his famous uncle.

Mayweather Jr. began boxing at four years old, following in the footsteps of the renowned Mayweather family as his father Floyd Sr., and uncles Jeff and Roger all competed professionally in the 1980s.

Taught by his father and Roger, Mayweather Jr. was crowned a champion in five weight divisions, finished his own fighting career with a flawless record of 50 wins unbeaten, and beat a who's who of impeccable opponents such as Manny Pacquiao, Saul "Canelo" Alvarez, Oscar de la Hoya, Jose Luis Castillo, and Genaro Hernandez, amongst others.

Though Mayweather Jr. retired in 2017 after besting Conor McGregor in a lucrative bout with the UFC striker, the American teased comebacks and even fought a bizarre exhibition in Japan on December 31, 2018.

But any signs of another pro fight may finally be over, as Mayweather Jr. appears inspired by lessons imparted from Roger, who died last month after years of deteriorating health.

In a video message posted on Instagram, Mayweather Jr. said this, combined with the ongoing coronavirus crisis, makes him want to pass on lessons he has learned onto the new generation and he has already been seen on video offering boxing instruction to his 20-year-old son Koraun and his 14-year-old nephew, below, who, he said, has no experience.

Mayweather Jr. said: "This is my first day working with my 14-year-old old nephew, and my second time doing mitt work.

"As many of you know, I've had incredible trainers which included my dad and uncle. Due to the recent passing of my Uncle Roger, I've felt inspired to help those around me the same way they have been there for me throughout my boxing career.

"In a time where we must distance ourselves from others, it has allowed me to reflect on how I want to make a difference in people lives and help them achieve their goals."

Mayweather then said he wants to help younger people achieve their goals in life, pushing them "to the best of their abilities."

He added: "I am new to helping people train as I've always been on the other side of the mitts. A fighter could be impressive at mitt work but it doesn't make him a great fighter. A trainer could be impressive on the mitts but it doesn't make him a great trainer.

"It has become a goal of mine to help others reach the best versions of themselves and walk with it in confidence. I want to leave an impression on those around me and allow them to see their potential.

"This quarantine period has allowed me to see the importance of unity and helping others grow. I want to do my part on this Earth and allow people to see the potential in themselves so that they can share it with the world.

"I am new at training and so far I've been working with people with no boxing experience, therefore we are growing together. But I promise you, I will be one of the best trainers in the world."

Watch Mayweather's latest video below:

A post shared by Floyd Mayweather (@floydmayweather)Apr 9, 2020 at 4:13pm PDT

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Floyd Mayweather Jr. is beginning a post-fight career in coaching - Insider - INSIDER

Life On The Run Has Slowed For WVUs Track Program

Like other student-athletes at West Virginia University, most of the members of the Mountaineer womens track & field program scattered to their respective hometowns once the NCAA shutdown all athletic events this spring to try to slow the spread of the COVID-19 virus.

WVUs 33-member track roster features student-athletes from eight different states and five different countries, including Australia, Kenya, Jamaica and Canada. Some stayed in Morgantown, either because they were from the University City or their hometowns were virus hotspots, but most are now spread out to various locales.

WVU head coach Sean Cleary check in with his student-athletes regularly, no matter where they are.

I would say the communication between our coaching staff and our student-athletes has been good, he said. I know me personally, Im usually doing something daily, and at the very most I dont go more than two days without chatting, at least a few words.

Were communicating using WhatsApp, Instant Messenger and things like that. They have subgroups going to help keep themselves motivated, and by that, I mean mainly in school.

I think everyones big concern is how theyll adjust to online classes. Some are made for it, and for others, they think its terrible. But the fact that the whole world is doing it makes it a little easier.

Two of those Cleary has been staying in contact with are Candace Jones-Archer and Olivia Hill, who are the 2020 squads lone seniors. The NCAA has ruled that spring-sport student-athletes can get an additional year of eligibility since they missed most of the 2020 season. Thus WVUs two senior runners will have an option to return next year.

We dont have many seniors, noted Cleary, who has serves as not only West Virginias track & field coach but also its cross-country coach. Candace got married last summer. She was one of our very best milers and had an incredible breakthrough last cross-country season (finishing a team-best seventh at the Big 12 championships). She had planned on leaving town (after graduation) to run with a professional group, but she has decided to stay and will use her last year of eligibility.

Olivia, who is from Teays Valley (Christian High School, which is in Scott Depot, W.Va.), is undecided, added Cleary, who is a 1992 WVU graduate himself. She is premed, and she has numerous options in front of her, including possibly coming back. She has some thinking to do in terms of if she wants defer med school. Id say the odds of Olivia coming back are 50-50.

A native of Ontario, Canada, Cleary arrived at WVU in 1991 as a runner for the Mountaineer track and cross-country teams, and he has remained at West Virginia ever since. He served as an assistant coach from 1993-2006 and then took over as the head coach in 2007.

This is a spring unlike any other for he and everyone else. Normally hed be working hands on with his athletes on a daily basis, but now hes had to back off in many areas.

The type of thing that isnt allowed is, for instance, we cant take a local pole vaulter out to a track and work with them. We cant do that, Cleary explained of the regulations during this social distancing time. What we can do is send them a program in terms of running and strength and conditioning. The department has done a wonderful job in the last week or two in terms of putting together care packages for the kids. For our distance runners, we send them iron supplements and things like that. Weve also sent them stretching bands and some other things.

In regards to just getting out to run, its tricky, added WVUs coach. Most tracks are closed, but you can still run on the roads and places like that. We probably have 10 runners who are still in Morgantown, but we dont want to tell them to get together and go run. To me it would irresponsible to do that. What theyve done is run in singles or in pairs. Theyve been pretty responsible about that.

Im able to facilitate training for five days a week, and Ive told them these are the five days and this is what you should be doing. Im assuming they are following through with that. Those girls want to run. My job for 90 percent of the team isnt to push them but actually to hold them back when theyre doing too much. I think theyre in a good spot, all things considered.

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Life On The Run Has Slowed For WVUs Track Program - Blue Gold News

These are strange and unusual times as we wait out a deadly virus and shelter at home. With offices shuttered, we take work video calls from our dining room tables. With schools closed, students do yoga assigned via email by their physical education teacher. With restaurant dining rooms off-limits, we are eating anniversary meals of frozen pizza and cinnamon rolls.

We're also doing at-home projects, making artwork on driveways, watching Netflix and trying new recipes. And we're taking our dogs on walks. Lots and lots of walks.

Here are stories about how we are living through these times.

An unusual field trip

During these unusual times, Jennifer Meyers found an offbeat place to take her kids for fun and learning: A cemetery.

Usually, the Bartonville family is busy with school and sports. But stuck at home lately, mom took the kids Sebastien, 13, plus 10-year-old triplets Autumn, Savannah and Noah on a field trip to St. Marys Cemetery in West Peoria, the resting place of some of their ancestors.

But family heritage wasnt their only lesson of the day. They took in some history and civics while seeking military markers. They got plenty of exercise while hiking through the graveyard. Along the way, they employed their math skills in trying to figure out the oldest tombstone. For three hours, they enjoyed a one-of-a-kind educational experience.

This was a way to learn without sitting behind a computer, Jennifer Meyers says.

Phil Luciano

At-home recruiting trail

Bradley University soccer coach Jim DeRose has always been a bundle of energy. And while spring is the off-season for college soccer, his springs are hectic. He spends them training his players, coaching a short spring exhibition season, hitting the recruiting trail, running a high school boys club soccer program and administering the Peoria city amateur team.

Most of those things arent happening this spring. DeRose still is maintaining relationships with recruits and monitoring his players progress by social messaging and phone. But mainly, the coach is getting routine jobs done around his Peoria home that he usually doesnt have time to tackle.

Im painting my bathroom, getting projects done outside on my lawn and spending some good family time, he said. Im not a sit-around guy although Ive gotten in some reading and podcasts on professional development. But Im mostly an outdoors warrior. Ive got pulled pork on the smoker going right now for dinner tonight.

Dave Reynolds

Studying the good book

The Rev. Marvin Hightower has been taking advantage of the earthly standstill to focus on a more heavenly pursuit. Like many aspects of the Bible, it's one that has current relevance.

Hightower, who is president of the Peoria chapter of the NAACP, is studying Biblical passages regarding eschatology. That part of theology focuses on death, judgment and the final destiny of the soul.

"A study on the end times," as Hightower put it.

The senior pastor of Liberty Church of Peoria also has been reading "Chronicle of the Seventh Son: Black Panther Mark Clark." It's about the Peoria native who with fellow activist Fred Hampton was killed in 1969 during a predawn raid by Chicago police.

Besides the reading and studying, Hightower has been on numerous conference calls and responding to emails. He and his wife also have been extra careful in other pursuits, because their daughter has respiratory issues coronavirus might affect.

"It definitely has slowed me down," Hightower stated.

Nick Vlahos

Teaching the guitar, online

Lucas Myers has been playing the guitar for nearly half his life.

With time on his hands because of the state's stay-at-home order, the Washington Community High School junior wants to teach guitar lessons.

Online, of course.

"I'm bored sometimes, but I know how to play the guitar, so why not teach people how to play it?" Myers said. "It won't be easy teaching guitar online, but I'll figure it out."

Myers, 17, has been playing the guitar as a hobby for 7 years and he's taught three people how to play it. He's in the intermediate guitar class at Washington.

If you're a health care worker or senior citizen, Myers won't charge for his online lessons. He can be reached at 840-5407.

Steve Stein

Life outside a picture window

My picture window approximates the dimensions of a large flat-screen television. For the past three weeks it has offered a view of a constantly interesting parade of pedestrians bipeds and canines that has provided a welcome distraction from the work-at-home social distancing blues.

Strangers and neighbors Who are these people? Where have they come from? stealing moments from their own cabin-fevered days for a government-approved stroll in nice weather. Couples pushing strollers. Elderly folks leaning into canes. Joggers. Little kids on little bikes. Big kids on big bikes. Three at a time. Mostly alone.

And this, no lie:

A young woman with a Great Dane big as a whitetail yearling on a leash and a little dog poking its head out of her backpack, walking down my street like it was as normal as a day without COVID-19.

Scott Hilyard

A social-distancing workout

Marti Teubel teaches group fitness classes an activity that's difficult to carry out amid social-distancing protocols and with area gyms closed.

Fortunately, streaming video isn't just for business meetings.

Like a number of other instructors at Five Points Washington, Teubel has seized the opportunity to stream her classes.

"As an instructor, it's my life but I'm realizing my members needed it as well," she said, both for the workout and for continuing the sense of community and fellowship the workouts offer.

The camaraderie has already encouraged one person who usually does more work on the elliptical machines to reach out and show interest in future group fitness classes, she says.

Chris Kaergard

Pride in service

Ethan Barlow's ceremony for taking the oath to join the Illinois Air National Guard was clearly different. First off, it was on the Peoria riverfront, so the noise from construction crews on the Murray Baker Bridge reverberated through the air.

Then, there was the coronavirus causing his family and friends and even the recruiter to stand at least six feet away.

The Dunlap High School senior said he wanted to join the Guard because it's in his family's tradition and because his father, Chance Barlow, was retiring after some 30 years in the 182nd Airlift Wing. Barlow will head to basic training later this year and, at some point in the future, join the Peoria-based unit as a loadmaster on a C-130.

As his father beamed with pride, Ethan Barlow raised his right hand and took the oath. When it was over, there wasn't a handshake, or a high five. The two just nodded. Chance Barlow gave his son a hug.

Chance Barlow, a longtime member of the Peoria Fire Department as well as of the Guard, said he was proud of his son's choice, noting that choosing the Guard over active duty showed his son wanted to not only serve in time of war but also in times of peace. His son agreed.

Andy Kravetz

'Life has slowed down a bit'

Shari Mahnesmith, manager at Arby's in Galesburgfor the past 11 years, has seen some positives from dealing with the COVID-19pandemic.

"The part of it that's kind of good is life has slowed down a little bit, " she said. "We're not running somewhere every night. It's kind of a bad way it happened."

Business-wise, things could be worse.

"Our drive-thru business has picked up even though we've closed the dining room," she said. "We're not hit as bad as some restaurants."

With about 30 employees, adjustments have been made to Arby's work schedule.

"Everybody has lost hours," Mahnesmith said. "If you were working four days, it's gone to three. We're keeping everybody working anyways."

And despite stay-at-home guidelines, Mahnesmith tries to help other local businesses.

"I used to eat out all the time. I don't like to cook," she said. "We just order it to go. We try to go where we usually go just to help those people."

Mike Trueblood

Real estate work continues

Tom Knapp, designated managing broker for RE/MAX Preferred Properties in Galesburg,says the real estate business continues despite restrictions caused by stay-at-home orders.

"Real estate is considered an essential business," said the real estate agentof 30 years. "We're still working, but we try to limit some of our work to home."

Office work is still required, Knapp says, but special precautions are taken.

"Every day we disinfect the office with Lysol," he said. "We spray down the door handles and hard surfaces and things like computer keypads."

House showings are also continuing, but with new procedures in place.

"We ask the buyer and seller to tell us if they've been abroad, or have a fever or cold or flu-like symptoms," Knapp said. "We limit showings to four people maximum."

After a promising start to the year which included good weather, a strong inventory of houses and low interest rates, Knapp hopes for better days ahead. "Everybody is trying to cope and do the best they can."

Mike Trueblood

Missing baseball, restaurants

Dick Lindstrom, owner of Lindstrom's TV and Appliance in Galesburg, notices the absence of the little things that make life more enjoyable.

"You don't know how much you appreciate things until you don't have them," Lindstrom said. "Like a haircut or going to Landmark and getting a sandwich. Hopefully, it will return.

"I miss baseball," said the lifelong Cubs fan. "It's just a strange time. If sports would return, that would go a long way to bring normalcy back."

To help fill the void, Lindstrom has relied on watching Cubs highlights from years past.

"I got to watch Ryne Sandburg's two-homer game against the Cardinals (1984) and their run up to the World Series, which is something I've never been able to do," he said.

Lindstrom's business of sales and service remains open under the state's stay-at-home guidelines, but with changes.

"Obviously we don't have the business we did in a typical month," he said. "Our front door (on Main Street) is locked, but a sign directs you to the Seminary Street door. We control what's happening.

"If your refrigerator or washer is not working right, that's pretty important."

Mike Trueblood

Can't get granite for grave stones

Sharon Ponder, co-owner of Lacky & Sons Monuments in Galesburgwith her husband Harv, says the pandemic has affected her business at a crucial time of year.

"This is our prime time our Christmas just before Memorial Day," she said.

"We have a lot of granite in warehouses in Georgia that we really need before Memorial Day, and we can't get it."

According to Ponder, Lacky normally receives shipments every week, but hasn't received one in three weeksduetowarehouses closed to the pandemic.

"We've got a lot of work to do to keep our employees working this month," she added.

Lacky's office is closed, but customers can call for appointments. "We do prefer them to wear a mask," she said.

Ponder is sensitive to other businesses in similar situations.

"We live in Knoxville and used to eat out, and we've been to Big Katz and Alfano's for carryout, we've been to 156 East (in Galesburg) three times in the last week," she said. "We try to help keep small businesses going.

"I'm getting very tired of my cooking."

Mike Trueblood

Walmart anniversary dinner

Steve Brubaker, a lobbyist for the Illinois Harness Horsemens Association and the Illinois Small Loan Association, is still working albeit by phone even though the General Assembly hasnt been in town for weeks.

"Its a lot harder now," Brubaker said. "Legislators are very busy in their districts dealing with COVID-19 and not as accessible as they might be in Springfield."

Negotiations on policies and potential bills are still going on even if no one knows when the General Assembly will reconvene.

The coronavirus outbreak did force a change in plans for the 38th wedding anniversary he and his wife observed.

"We usually go out to dinner," Brubaker said. "This time we had a frozen pizza from Walmart. We had a muffin from Sams Club as our celebratory cake. That was perfectly fine."

Doug Finke

'Everything is so upside down'

Daksh Desai sits alone in his two-bedroom apartment on the University of Illinois Springfield campus, over 8,000 miles away from his home in India. His roommate bagged his belongings and left weeks ago.

Desai wishes he could be doing what he normally does in April capturing moments of UIS baseball with his camera. Instead, he is playing a baseball video game.

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How has the coronavirus changed our lives? - Pekin Daily Times

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Inspiration- Rewire your brain and reach your dreams with ease, on autopilot! - BlogTalkRadio

People working in the healthcare sector take extra precautions to avoid mistakes and medication errors that can put the lives of patients at risk. Yet, despite this, 2% of patients face preventable medical-related incidents that could be life-threatening. Inadequate systems, tools, processes or working conditions are some of the reasons contributing to these medical mistakes.

In a bid to solve this problem, Google collaborated with UCSFs Bakar Computational Health Sciences Institute to publish Predicting Inpatient Medication Orders in Electronic Health Record Data in Clinical Pharmacology and Therapeutics. The published paper discusses how machine learning (ML) can be used to anticipate standard prescribing patterns by doctors as per the availability of electronic health records.

Google used clinical data of de-identified patients, which included vital signs, laboratory results, past medications, procedures, diagnoses, and more. Googles new model was designed to anticipate a physicians prescription decisions three-quarters of the time, after evaluating the patients current state and medical history.

To train the model, Google chose a dataset containing approximately three million medication orders from more than 1,00,000 hospitals. The company acquired the retrospective electronic health data through de-identification, by choosing random dates and removing all the identifying checkpoints of the record as per the HIPPA rules and guidelines. The company did not gather any identifying information such as names, addresses, contact details, record numbers, names of physicians, free-text notes, images, etc.

The research by the tech giant was done using the open-sourced Fast Healthcare Interoperability Resources (FHIR) format that the company claims was previously applied to improve healthcare data and make it more useful for machine learning. Google did not restrict the dataset to a particular disease, which made the ML activity more demanding. It also allowed the model to identify a wider variety of medical conditions.

Also Read Best Habits For Budding Machine Learning Researchers

Google approached two different ML models the long short-term recurrent neural network, and the regularized time-bucketed logistic model, which are often used in clinical research. Both models were put into comparison against a simple baseline, which was ranked as the most commonly ordered medication based on a patients hospital service, along with time spent since the admission in the hospital. The models ranked a list of 990 possible medications every time a medication was entered in the retrospective data. The team further assessed if the models assigned high probabilities to the medication that were provided by the doctors for each case.

Googles best performing model was the LSTM model, which is capable of handling sequential data, including text and language. The model has been designed to choose the recent events in data and their order, which makes it an excellent option to deal with this problem. Almost 93% of the top-10 list included at least one medication that a clinician would prescribe to a patient within the next day.

The model rightly forecasted the medications prescribed by a doctor as one of the top-10 most likely medications, which calculated to an accuracy amount of 55%. 75% of the ordered medication were ranked in top-25, whereas false-negative cases, where a doctors medication did not make it into the top-25 results, found itself to be in the same 42% of the time as ranked by the model.

These models are trained to mimic a physicians behavior as it appears in historical data, and did not learn the optimal prescribing pattern. Due to this, the models do not understand how the medications might work, or if they have any side effects or not. As per Google, the learning sequence will take time to show normal behavior in a bid to spot abnormal and potentially dangerous orders. In the next phase, the company will examine the models under different circumstances to understand which medication error can cause harm to patients.

Also Read 10 leading Analytics Accelerators/Incubators in India

The result of this work by Google is a small step towards testing the hypothesis that machine learning can be applied to build different systems which can prevent mistakes on the part of doctors and clinicians to keep patients safe. Google is looking forward to collaborating with doctors, pharmacists, clinicians and patients to continue the research for a better result.

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How Machine Learning Is Being Used To Eradicate Medication Errors - Analytics India Magazine

Despite the huge contributions of deep learning to the field of artificial intelligence, theres something very wrong with it: It requires huge amounts of data. This is one thing that boththe pioneersandcritics of deep learningagree on. In fact, deep learning didnt emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data.

Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers.

In hiskeynote speech at the AAAI conference, computer scientist Yann LeCun discussed the limits of current deep learning techniques and presented the blueprint for self-supervised learning, his roadmap to solve deep learnings data problem. LeCun is one of thegodfathers of deep learningand the inventor ofconvolutional neural networks (CNN), one of the key elements that have spurred a revolution in artificial intelligence in the past decade.

Self-supervised learning is one of several plans to create data-efficient artificial intelligence systems. At this point, its really hard to predict which technique will succeed in creating the next AI revolution (or if well end up adopting a totally different strategy). But heres what we know about LeCuns masterplan.

First, LeCun clarified that what is often referred to as the limitations of deep learning is, in fact, a limit ofsupervised learning. Supervised learning is the category of machine learning algorithms that require annotated training data. For instance, if you want to create an image classification model, you must train it on a vast number of images that have been labeled with their proper class.

[Deep learning] is not supervised learning. Its not justneural networks. Its basically the idea of building a system by assembling parameterized modules into a computation graph, LeCun said in his AAAI speech. You dont directly program the system. You define the architecture and you adjust those parameters. There can be billions.

Deep learning can be applied to different learning paradigms, LeCun added, including supervised learning,reinforcement learning, as well as unsupervised or self-supervised learning.

But the confusion surrounding deep learning and supervised learning is not without reason. For the moment, the majority of deep learning algorithms that have found their way into practical applications are based on supervised learning models, which says a lot aboutthe current shortcomings of AI systems. Image classifiers, facial recognition systems, speech recognition systems, and many of the other AI applications we use every day have been trained on millions of labeled examples.

Reinforcement learning and unsupervised learning, the other categories of learning algorithms, have so far found very limited applications.

Supervised deep learning has given us plenty of very useful applications, especially in fields such ascomputer visionand some areas of natural language processing. Deep learning is playing an increasingly important role in sensitive applications, such as cancer detection. It is also proving to be extremely useful in areas where the scale of the problem is beyond being addressed with human efforts, such aswith some caveatsreviewing the huge amount of content being posted on social media every day.

If you take deep learning from Facebook, Instagram, YouTube, etc., those companies crumble, LeCun says. They are completely built around it.

But as mentioned, supervised learning is only applicable where theres enough quality data and the data can capture the entirety of possible scenarios. As soon as trained deep learning models face novel examples that differ from their training examples, they start to behave in unpredictable ways. In some cases,showing an object from a slightly different anglemight be enough to confound a neural network into mistaking it with something else.

ImageNet vs reality: In ImageNet (left column) objects are neatly positioned, in ideal background and lighting conditions. In the real world, things are messier (source: objectnet.dev)

Deep reinforcement learning has shownremarkable results in games and simulation. In the past few years, reinforcement learning has conquered many games that were previously thought to off-limits for artificial intelligence. AI programs have already decimated human world champions atStarCraft 2, Dota, and the ancient Chinese board game Go.

But the way these AI programs learn to solve problems is drastically different from that of humans. Basically, a reinforcement learning agent starts with a blank slate and is only provided with a basic set of actions it can perform in its environment. The AI is then left on its own to learn through trial-and-error how to generate the most rewards (e.g., win more games).

This model works when the problem space is simple and you have enough compute power to run as many trial-and-error sessions as possible. In most cases, reinforcement learning agents take an insane amount of sessions to master games. The huge costs have limited reinforcement learning research to research labsowned or funded by wealthy tech companies.

Reinforcement learning agents must be trained on hundreds of years worth of session to master games, much more than humans can play in a lifetime (source: Yann LeCun).

Reinforcement learning systems are very bad attransfer learning. A bot that plays StarCraft 2 at grandmaster level needs to be trained from scratch if it wants to play Warcraft 3. In fact, even small changes to the StarCraft game environment can immensely degrade the performance of the AI. In contrast, humans are very good at extracting abstract concepts from one game and transferring it to another game.

Reinforcement learning really shows its limits when it wants to learn to solve real-world problems that cant be simulated accurately. What if you want to train a car to drive itself? And its very hard to simulate this accurately, LeCun said, adding that if we wanted to do it in real life, we would have to destroy many cars. And unlike simulated environments, real life doesnt allow you to run experiments in fast forward, and parallel experiments, when possible, would result in even greater costs.

LeCun breaks down the challenges of deep learning into three areas.

First, we need to develop AI systems that learn with fewer samples or fewer trials. My suggestion is to use unsupervised learning, or I prefer to call it self-supervised learning because the algorithms we use are really akin to supervised learning, which is basically learning to fill in the blanks, LeCun says. Basically, its the idea of learning to represent the world before learning a task. This is what babies and animals do. We run about the world, we learn how it works before we learn any task. Once we have good representations of the world, learning a task requires few trials and few samples.

Babies develop concepts of gravity, dimensions, and object persistence in the first few months after their birth. While theres debate on how much of these capabilities are hardwired into the brain and how much of it is learned, what is for sure is that we develop many of our abilities simply by observing the world around us.

The second challenge is creating deep learning systems that can reason. Current deep learning systems are notoriously bad at reasoning and abstraction, which is why they need huge amounts of data to learn simple tasks.

The question is, how do we go beyond feed-forward computation and system 1? How do we make reasoning compatible with gradient-based learning? How do we make reasoning differentiable? Thats the bottom line, LeCun said.

System 1 is the kind of learning tasks that dont require active thinking, such as navigating a known area or making small calculations. System 2 is the more active kind of thinking, which requires reasoning.Symbolic artificial intelligence, the classic approach to AI, has proven to be much better at reasoning and abstraction.

But LeCun doesnt suggest returning to symbolic AI or tohybrid artificial intelligence systems, as other scientists have suggested. His vision for the future of AI is much more in line with that of Yoshua Bengio, another deep learning pioneer, who introduced the concept ofsystem 2 deep learningat NeurIPS 2019 and further discussed it at AAAI 2020. LeCun, however, did admit that nobody has a completely good answer to which approach will enable deep learning systems to reason.

The third challenge is to create deep learning systems that can lean and plan complex action sequences, and decompose tasks into subtasks. Deep learning systems are good at providing end-to-end solutions to problems but very bad at breaking them down into specific interpretable and modifiable steps. There have been advances in creatinglearning-based AI systems that can decompose images, speech, and text. Capsule networks, invented by Geoffry Hinton, address some of these challenges.

But learning to reason about complex tasks is beyond todays AI. We have no idea how to do this, LeCun admits.

The idea behind self-supervised learning is to develop a deep learning system that can learn to fill in the blanks.

You show a system a piece of input, a text, a video, even an image, you suppress a piece of it, mask it, and you train a neural net or your favorite class or model to predict the piece thats missing. It could be the future of a video or the words missing in a text, LeCun says.

The closest we have to self-supervised learning systems are Transformers, an architecture that has proven very successful innatural language processing. Transformers dont require labeled data. They are trained on large corpora of unstructured text such as Wikipedia articles. And theyve proven to be much better than their predecessors at generating text, engaging in conversation, and answering questions. (But they are stillvery far from really understanding human language.)

Transformers have become very popular and are the underlying technology for nearly all state-of-the-art language models, including Googles BERT, Facebooks RoBERTa,OpenAIs GPT2, and GooglesMeena chatbot.

More recently, AI researchers have proven thattransformers can perform integration and solve differential equations, problems that require symbol manipulation. This might be a hint that the evolution of transformers might enable neural networks to move beyond pattern recognition and statistical approximation tasks.

So far, transformers have proven their worth in dealing with discreet data such as words and mathematical symbols. Its easy to train a system like this because there is some uncertainty about which word could be missing but we can represent this uncertainty with a giant vector of probabilities over the entire dictionary, and so its not a problem, LeCun says.

But the success of Transformers has not transferred to the domain of visual data. It turns out to be much more difficult to represent uncertainty and prediction in images and video than it is in text because its not discrete. We can produce distributions over all the words in the dictionary. We dont know how to represent distributions over all possible video frames, LeCun says.

For each video segment, there are countless possible futures. This makes it very hard for an AI system to predict a single outcome, say the next few frames in a video. The neural network ends up calculating the average of possible outcomes, which results in blurry output.

This is the main technical problem we have to solve if we want to apply self-supervised learning to a wide variety of modalities like video, LeCun says.

LeCuns favored method to approach supervised learning is what he calls latent variable energy-based models. The key idea is to introduce a latent variable Z which computes the compatibility between a variable X (the current frame in a video) and a prediction Y (the future of the video) and selects the outcome with the best compatibility score. In his speech, LeCun further elaborates on energy-based models and other approaches to self-supervised learning.

Energy-based models use a latent variable Z to compute the compatibility between a variable X and a prediction Y and select the outcome with the best compatibility score (image credit: Yann LeCun).

I think self-supervised learning is the future. This is whats going to allow to our AI systems, deep learning system to go to the next level, perhaps learn enough background knowledge about the world by observation, so that some sort of common sense may emerge, LeCun said in his speech at the AAAI Conference.

One of the key benefits of self-supervised learning is the immense gain in the amount of information outputted by the AI. In reinforcement learning, training the AI system is performed at scalar level; the model receives a single numerical value as reward or punishment for its actions. In supervised learning, the AI system predicts a category or a numerical value for each input.

In self-supervised learning, the output improves to a whole image or set of images. Its a lot more information. To learn the same amount of knowledge about the world, you will require fewer samples, LeCun says.

We must still figure out how the uncertainty problem works, but when the solution emerges, we will have unlocked a key component of the future of AI.

If artificial intelligence is a cake, self-supervised learning is the bulk of the cake, LeCun says. The next revolution in AI will not be supervised, nor purely reinforced.

This story is republished fromTechTalks, the blog that explores how technology is solving problems and creating new ones. Like them onFacebookhere and follow them down here:

Published April 5, 2020 05:00 UTC

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Self-supervised learning is the future of AI - The Next Web

Based on the data thats collected, it tells me how Im moving compared to previously and how Im moving compared to my ideal movement signature, as they call it, Mr. Ross said. Sparta Science then tailors his workouts to move him closer to that ideal.

The Pittsburgh Steelers, the Detroit Lions and the Washington Redskins, among others, use the system regularly, Dr. Wagner said. Sparta Science is also used to evaluate college players in the National Football Leagues annual scouting combine.

Of course, it is inevitable that machine learnings predictive power would be applied to another lucrative end of the sports industry: betting. Sportlogiq, a Montreal-based firm, has a system that primarily relies on broadcast feeds to analyze players and teams in hockey, soccer, football and lacrosse.

Mehrsan Javan, the companys chief technology officer and one of its co-founders, said the majority of National Hockey League teams, including the last four Stanley Cup champions, used Sportlogiqs system to evaluate players.

Josh Flynn, assistant general manager for the Columbus Blue Jackets, Ohios professional hockey franchise, said the team used Sportlogiq to analyze players and strategy. We can dive levels deeper into questions we have about the game than we did before, Mr. Flynn said.

But Sportlogiq also sells analytic data to bookmakers in the United States, helping them set odds on bets, and hopes to sell information to individual bettors soon. Mr. Javan is looking to hire a vice president of betting.

They key to all of this sports-focused technology is data.

Algorithms come and go, but data is forever, Mr. Alger is fond of saying. Computer vision systems have to be told what to look for, whether it be tumors in an X-ray or bicycles on the road. In Seattle Sports Sciences case, the computers must be trained to recognize the ball in various lighting conditions as well as understand which plane of the foot is striking the ball.

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Want to Be Better at Sports? Listen to the Machines - The New York Times

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by Kristen Senz

Imagine a future in which a smart marketing machine can predict the needs and habits of individual consumers and the dynamics of competitors across industries and markets. This device would collect data to answer strategic questions, guide managerial decisions, and enable marketers to quickly test how new products or services would perform at various prices or with different characteristics.

The machine learning algorithms that might power such a device are, at least for now, incapable of producing such promising results. But what about tomorrow? According to a group of researchers, the envisioned virtual market machine could become a reality but would still require one missing ingredient: a soul.

The soul is our human intuition, scientific expertise, awareness of customer preferences, and industry knowledgeall capabilities that machines lack and intelligent marketing decisions require.

Without a soul, without human insight, the capabilities of the machine will be limited, a group of 13 marketing scholars write in their working paper,Soul and Machine (Learning), which takes a high-level view of the present and future role of machine learning tools in marketing research and practice. We propose to step back and ask how we can best integrate machine learning to solve previously untenable marketing problems facing real companies?

A product of the11th Triennial Invitational Choice Symposiumheld last year, the paper explains how machine learning leverages Big Data, giving managers new tools to help unravel complex marketing puzzles and understand consumer behavior like never before.Tomomichi Amano, assistant professor in the Marketing Unit at Harvard Business School, is one of the papers authors.

We tend to think that when we have all this rich data and this machine learning technology, that the machines are going to just come up with the best solution, says Amano. But thats not something were able to do now, and to have any hope of doing that, we need to be integrating the specialized domain knowledge that managers possess into these tools and systems.

Marketers have long envisioned the potential for technology to bring about a virtual marketan algorithm so sophisticated that multiple departments within the firm could query it for answers to questions ranging from optimal pricing to product design. What prevents this from materializing? After all, machine learning is delivering self-driving cars and beating human players onJeopardy!

The answer: context specificity, says Amano.

The factors that influence consumer behavior are so varied and complex, and the data that companies collect is so rich, just modeling how consumers search a single retail website is a monumental task. Each companys data are so firm and occasion-specific that building and scaling such models is neither feasible nor economical. Machine learning technology today excels at self-contained tasks like image recognition and content-sorting.

The kind of tasks that we want to do in marketing tend to be more challenging, because were trying to model human behavior, Amano says. So the number of things the model cannot systematically predict is much larger. In other words, theres lots of noise in human behavior.

Instead of working to create the virtual market, marketers and marketing researchers are trying to break it down into more manageable pieces. Amano approaches this from an economic perspective, using basic economic principlesassuming customers prefer lower-priced products, for exampleto build models that can begin to explain how consumers approach online search. (SeeLarge-Scale Demand Estimation with Search Data.)

Other researchers are developing machine learning tools that can leverage content from customers product reviews to identify their future needs. But here the human analysts are key players. They must review the selected content and formulate customer needs, because natural language processing technology still lacks the sophistication to infer them. Increasingly, this hybrid approach is allowing companies to replace traditional customer interviews and focus groups, according to Amano and his colleagues.

Understanding what prompts a customer to purchase a producta concept known as attributionis an area ripe for new hybrid tactics, says Amano. For example, a customer exposed to three different ads for a cell phoneon a bus, on TV, and onlinetalks to his or her friends about cell phones and then buys the phone from the ads a week later.

Regardless of how much data is collected, we dont know how much that bus ad you saw contributed to your purchase of the cell phone, Amano says. We dont know how to model that, and we dont know how to think about it, but its a really important question, because that informs whether you run another ad on the bus.

Heres where managerial insight and behavioral theory can guide firms use of data and machine learning to gain new knowledge about current and potential market segments. It might be that people on the bus use their cell phones more, Amano posits, so they just tend to buy cell phones more often.

Managers who implement marketing tactics and analytics that meld human capital and the machine learning toolbox stand to improve decision-making and product development. But doing so requires careful consideration of the balance between personalization and privacy. At what point do curated online product recommendations become so creepy or intrusive that they sour customers on the brand?

Amano points out that the benefits of personalized marketing are often overshadowed by the creepiness factor. There definitely are a bunch of benefits that we reap from the fact that firms and governments have access to more of our data, he says, even though some of those benefits are hard to see.

Receiving information about available products is one benefit to consumers. In the case of government, the marketing scholars who attended the Choice Symposium contend that machine learning will soon augment or replace expensive survey-based data gathering techniques to keep important indices, such as unemployment rates, up to date.

Machines can scrape at high frequency to collect publicly available information about consumers, firms, jobs, social media, etc., which can be used to generate indices in real-time, the scholars write. With careful development, these measures will be more precise and able to better predict the economic conditions of geographic areas at high granularity, from zip codes to cities, to states and nations.

But privacy concerns among consumers are real and growing, and marketing professionals and scholars are still trying to understand the implications.

Facebook and Googlethese services are free from a monetary perspective, but I think theres some recognition that we are paying some cost in using them, by giving out some of our data, and from that perspective, there is some more research we have to do on the academic front to make sure we understand how firms ought to be responding to these concerns, Amano says.

Managers, in the meantime, must rely on their own insight and experience to find the answer to that question and others. They also need to keep their expectations realistic when it comes to the capacity of machine learning tools, says Amano, and employ people who can communicate effectively about data-based approaches. Ultimately, managers who have the foresight to collaborate with data analysts to design data collection efforts and stagger promotions will be well positioned to harness the power of new machine learning tools in marketing.

You cant do something in business, and then collect the data, and then expect the machine learning methods to spit out insight for you, Amano says. Its important that throughout the process you consult and think about your goals and how what youre doing is going to influence the kind of data you can collect.

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Don't Turn Your Marketing Function Over To AI Just Yet - Forbes

As development teams race to build outAI tools, it is becoming increasingly common to train algorithms on edge devices. Federated learning, a subset of distributed machine learning, is a relatively new approach that allows companies to improve their AI tools without explicitlyaccessing raw user data.

Conceived byGoogle in 2017, federated learning is a decentralized learning model through which algorithms are trained on edge devices. In regard to Googles on-device machine learning approach, the search giant pushed their predictive text algorithm to Android devices, aggregated the data and sent a summary of the new knowledge back to a central server. To protect the integrity of the user data, this data was eitherdelivered via homomorphic encryption or differential privacy, which is the practice of adding noise to the data in order to obfuscate the results.

Generally speaking, with federated learning, the AI algorithm is trained without ever recognizing any individual users specific data; in fact, the raw data never leaves the device itself. Only aggregated model updates are sent back. These model updates are thendecrypted upon delivery to the central server. Test versions of the updated model are then sent back to select devices, and after this process is repeated thousands of times, the AI algorithm is significantly improvedall while never jeopardizing user privacy.

This technology is expected to make waves in the healthcare sector. For example, federated learning is currently being explored by medical start-up Owkin. Seeking to leverage patient data from several healthcare organizations, Owkin uses federated learning to build AI algorithms with data from various hospitals. This can have far-reaching effects, especially as its invaluable that hospitals are able to share disease progression data with each other while preserving the integrity of patient data and adhering to HIPAA regulations. By no means is healthcare the only sector employing this technology; federated learning will be increasingly used by autonomous car companies, smart cities, drones, and fintech organizations. Several other federated learning start-ups are coming to market, includingSnips,S20.ai, andXnor.ai, which was recently acquired by Apple.

Seeing as these AI algorithms are worth a great deal of money, its expected that these models will be a lucrative target for hackers. Nefarious actors will attempt to perform man-in-the-middle attacks. However, as mentioned earlier, by adding noise and aggregating data from various devices and then encrypting this aggregate data, companies can make things difficult for hackers.

Perhaps more concerning are attacks that poison the model itself. A hacker could conceivably compromise the model through his or her own device, or by taking over another users device on the network. Ironically, because federated learning aggregates the data from different devices and sends the encrypted summaries back to the central server, hackers who enter via a backdoor are given a degree of cover. Because of this, it is difficult, if not impossible, to identify where anomalies are located.

Althoughon-device machine learning effectively trains algorithms without exposing raw user data, it does require a ton of local power and memory. Companies attempt to circumvent this by only training their AI algorithms on the edge when devices are idle, charging, or connected to Wi-Fi; however, this is a perpetual challenge.

As 5G expands across the globe, edge devices will no longer be limited by bandwidth and processing speed constraints.According to a recentNokia report, 4G base stations can support 100,000 devices per square kilometer; whereas, the forthcoming 5G stations will support up to 1 million devices in the same area.Withenhanced mobile broadband and low latency, 5G will provide energy efficiency, while facilitating device-to-device communication (D2D). In fact, it is predicted that 5G will usher in a 10-100x increase in bandwidth and a 5-10x decrease in latency.

When 5G becomes more prevalent, well experience faster networks, more endpoints, and a larger attack surface, which may attract an influx of DDoS attacks. Also, 5G comes with a slicing feature, which allows slices (virtual networks) to be easily created, modified, and deleted based on the needs of users.According to aresearch manuscript on the disruptive force of 5G, it remains to be seen whether this network slicing component will allay security concerns or bring a host of new problems.

To summarize, there are new concerns from both a privacy and a security perspective; however, the fact remains: 5G is ultimately a boon for federated learning.

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How Will the Emergence of 5G Affect Federated Learning? - IoT For All