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Making New Drugs With a Dose of Artificial Intelligence

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SAN FRANCISCO — You can think of it as a World Cup of biochemical research.

Every two years, hundreds of scientists enter a global competition. Tackling a biological puzzle they call “the protein folding problem,” they try to predict the three-dimensional shape of proteins in the human body. No one knows how to solve the problem. Even the winners only chip away at it. But a solution could streamline the way scientists create new medicines and fight disease.

Mohammed AlQuraishi, a biologist who has dedicated his career to this kind of research, flew in early December to Cancun, Mexico, where academics were gathering to discuss the results of the latest contest. As he checked into his hotel, a five-star resort on the Caribbean, he was consumed by melancholy.

The contest, the Critical Assessment of Structure Prediction, was not won by academics. It was won by DeepMind, the artificial intelligence lab owned by Google’s parent company.

“I was surprised and deflated,” said Dr. AlQuraishi, a researcher at Harvard Medical School. “They were way out in front of everyone else.”

DeepMind specializes in “deep learning,” a type of artificial intelligence that is rapidly changing drug discovery science. A growing number of companies are applying similar methods to other parts of the long, enormously complex process that produces new medicines. These A.I. techniques can speed up many aspects of drug discovery and, in some cases, perform tasks typically handled by scientists.

“It is not that machines are going to replace chemists,” said Derek Lowe, a longtime drug discovery researcher and the author of In the Pipeline, a widely read blog dedicated to drug discovery. “It’s that the chemists who use machines will replace those that don’t.”

After the conference in Cancun, Dr. AlQuraishi described his experience in a blog post. The melancholy he felt after losing to DeepMind gave way to what he called “a more rational assessment of the value of scientific progress.”

But he strongly criticized big pharmaceutical companies like Merck and Novartis, as well as his academic community, for not keeping pace.

“The smartest and most ambitious researchers wanting to work on protein structure will look to DeepMind for opportunities instead of Merck or Novartis,” he wrote. “This fact should send chills down the spines of pharma executives, but it won’t, because they’re clueless, rudderless, and asleep at the helm.”

The big pharma companies see the situation differently. Though Merck is not exploring protein folding because its researchers believe its potential impact would be years away, it is applying deep learning to other aspects of its drug discovery process.

“We have to connect so many other dots,” said Juan Alvarez, associate vice president of computational and structural chemistry at Merck.

In the spring of 2016, after making headlines with A.I. systems that played complex games like the ancient board game Go, DeepMind researchers were looking for new challenges. So they held a “hackathon” at company headquarters in London.

Working with two other computer scientists, the DeepMind researcher Rich Evans homed in on protein folding. They found a game that simulated this scientific task. They built a system that learned to play the game on its own, and the results were promising enough for DeepMind to greenlight a full-time research project.

The protein folding problem asks a straightforward question: Can you predict the physical structure of a protein — its shape in three dimensions?

If scientists can predict a protein’s shape, they can better determine how other molecules will “bind” to it — attach to it, physically — and that is one way drugs are developed. A drug binds to particular proteins in your body and changes their behavior.

In the latest contest, DeepMind made these predictions using “neural networks,” complex mathematical systems that can learn tasks by analyzing vast amounts of data. By analyzing thousands of proteins, a neural network can learn to predict the shape of others.

This is the same deep learning technology that recognizes faces in the photos you post to Facebook. Over the past decade, the technology has reinvented a wide range of internet services, consumer products, robotic devices and other areas of scientific research.

Many of the academics who competed used methods that were similar to what DeepMind was doing. But DeepMind won the competition by a sizable margin — it improved the prediction accuracy nearly twice as much as experts expected from the contest winner.

DeepMind’s victory showed how the future of biochemical research will increasingly be driven by machines and the people who oversee those machines.

This kind of A.I. research benefits from enormous amounts of computing power, and DeepMind can lean on the massive computer data centers that underpin Google. The lab also employs many of the world’s top A.I. researchers, who know how to get the most out of this hardware.

“It allows us to be much more creative, to try many more ideas, often in parallel,” said Demis Hassabis, the chief executive and a co-founder of DeepMind, which Google acquired for a reported $650 million in 2014.

Universities and big pharmaceutical companies are unlikely to match those resources. But thanks to cloud computing services offered by Google and other tech giants, the price of computing power continues to drop. Dr. AlQuraishi urged the life-sciences community to shift more attention toward the kind of A.I. work practiced by DeepMind.

Some researchers are already moving in that direction. Many start-ups, like Atomwise in San Francisco and Recursion in Salt Lake City, are using the same artificial intelligence techniques to accelerate other aspects of drug discovery. Recursion, for instance, uses neural networks and other methods to analyze images of cells and learn how new drugs affect these cells.

The big pharma companies are also beginning to explore these methods, sometimes in partnership with start-ups.

“Everyone is trending up in this area,” said Jeremy Jenkins, the head of data science for chemical biology and therapeutics at Novartis. “It is like turning a big ship, and I think these methods will eventually scale to the size of our entire company.”

Mr. Hassabis said DeepMind was committed to solving the protein folding problem. But many experts said that even if it was solved, more work was needed before doctors and patients benefited in any practical way.

“This is a first step,” said David Baker, the director of the Institute for Protein Design at the University of Washington. “There are so many other steps still to go.”

As they work to better understand the proteins in the body, for instance, scientists must also create new proteins that can serve as drug candidates. Dr. Baker now believes that creating proteins is more important to drug discovery than the “folding” methods being explored, and this task, he said, is not as well suited to DeepMind-style A.I.

DeepMind researchers focus on games and contests because they can show a clear improvement in artificial intelligence. But it is not clear how that approach translates to many tasks.

“Because of the complexity of drug discovery, we need a wide variety of tools,” Dr. Alvarez said. “There is no one-size-fits-all answer.”

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Trudeau Government Should Turn to Sustainable Floor Heating In Its New Deal

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A consortium has been chosen by Public Services and Procurement Canada (PSPC) to manage the $1.1-billion overhaul of five heating and cooling plants in the National Capital Region. However, this decision has been met with a lot of disapproval by the country’s largest federal public service union.

Early June, the department announced that Innovate Energy has been awarded the 30-year contract “to design, retrofit, maintain and operate the plants,”winning the bid over a rival group that included SNC-Lavalin.

Minister of Environment, Catherine McKenna, said the federal government was “leading by example” in its bid to drastically reduce the amount of greenhouse gas emissions across the country. McKenna noted that by supporting this project, they’re utilizing heating and cooling infrastructure to promote a more environmentally friendly option.

“We’re very proud that our government is working with partners like Innovate Energy to modernize this critical infrastructure,” she said during the announcement at one of the facilities that will be upgraded, the Cliff Heating and Cooling Plant in downtown Ottawa.

The plants would be known as the district energy system and would heat 80 buildings in the area with steam. It is also expected to cool 67 of these buildings with chilled water through more than 14 kilometres of underground pipes.

Under the Energy Services Acquisition Program, PSPC will be tasked with modernizing the outdated technology in the plants to lower emissions and supportgrowth in the eco-friendly technology sector.

During the first stage of the overhaul, the system would be converted from steam to low temperature hot water and then switched from steam to electric chillers—with the estimated completion date being 2025. PSPC notes that the project will reduce current emissions by 63 per cent, the equivalent of removing 14,000 non-eco-friendly cars off the road.

Afterwards, the natural gas powering the plant will then be replaced by carbon-neutral fuel sources, which according to estimated will reduce emissions by a further 28 per cent. The renovation project is bound to save the government an estimated fee of more than $750 million in heating and cooling costs in the next 40 years.

Furthermore, the implementation of radiant floor heating in Ottawa by the federal government would be an additional step in driving its agenda for a more eco-friendly state.

According to the U.S. Department of Energy’s Energy Savers website, radiant floor heating has a lot of benefits and advantages over alternate heat systems and can cut heating costs by 25 to 50 per cent.

“It is more efficient than baseboard heating and usually more efficient than forced-air heating because no energy is lost through ducts,” the website states.

Radiant floor heating provides an equal amount of heat throughout a building, including areas that are difficult to heat, such as rooms with vaulted ceilings, garages or bathrooms. Consideringit warms people and objects directly—controlling the direct heat loss of the occupant—radiant floor heating provides comfort at lower thermostat settings.

“Radiators and other forms of ‘point’ heating circulate heat inefficiently and hence need to run for longer periods to obtain comfort levels,” reports the Residential Energy Services Network (RESNet).

Radiant heating is a clean and healthy option—a perfect choice for those with severe allergies—as it doesn’t rely on circulating air, meaning there are no potentially irritating particles blowing around the room. Additionally, it is more energy efficient, aesthetically pleasing with wall radiators or floor registers and virtually noiseless when in operation.

“They draw cold air across the floor and send warm air up to the ceiling, where it then falls, heating the room from the top down, creating drafts and circulating dust and allergens.”

It is important for the leadership in Ottawa to equally drive the adoption of radiant floor heating as doing this would lead to increased usage in residential buildings—and even government-owned buildings.

However, in October, the Public Service Alliance of Canada (PSAC), a representative body of employees of the plants,began a campaign target at the government against their decision to use a public-private partnership (P3) for the retrofitting project, citing concerns about costs and safety.

According to the union, outside employees won’t be bound to the same health and safety standards of government workers and that typically P3 projects cost a lot more than traditional public financing deals.

The union demands that the government scraps the proposed project and meet PSAC members and experts to brainstorm on a new way forward that would ensure federal employees continue to operate and maintain the plants.

However, parliamentary secretary to public services and procurement minister, Steve MacKinnon said that the union officials have consulted him but that after conducting an analysis, the P3 option was still the best for the job.

“We didn’t have (to) sacrifice on safety or health — we didn’t have to sacrifice on job security,” he said.

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Steps to becoming a Data Scientist

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Data science has become one of the most in-demand career paths in this century, according to Business Insider. With the amount of information being circulated online, it has created a huge demand for storing, interpreting and implementing big data for different purposes—hence the need for a data scientist.

Today, there too much information flying around for regular people to process efficiently and use. Therefore, it has become the responsibility of data scientists to collect, organize and analyze this data. Doing this helps various people, organizations, enterprise businesses and governments to manage, store and interpret this data for different purposes.

Though data scientists come from different educational backgrounds, a majority of them need to have a technical educational background. To pursue a career in data science, computer-related majors, graduations and post graduations in maths and statistics are quite useful.

Therefore, the steps to becoming a data scientist are quite straightforward.  After obtaining a bachelor’s degree in an IT related field—such as computer science, maths or physics—you can also further your education by obtaining a master’s degree in a data science or any other related field of study. With the necessary educational background, you can now search for a job and obtain the required experience in whichever filed you choose to invest your acquired skills.

Here are the necessary steps to be taken to become a data scientist.

Step 1: Obtain the necessary educational requirements

As earlier noted, different educational paths can still lead to a career in data science. However, it is impossible to begin a career in data science without obtaining a collegiate degree—as a four-year bachelor’s degree is really important. However, according to a report by Business Insider, over 73% of data scientist in existence today have a graduate degree and about 38% of them hold a Ph.D. Therefore, to rise above the crowd and get a high-end position in the field of data science, it is important to have a Master’s degree or a Ph.D.—and with various online data science masters program, obtaining one is quite easy.

Some institutions provide data science programs with courses that will equip students to analyze complex sets of data. These courses also involve a host of technical information about computers, statistics, data analysis techniques and many more. Completing these programs equips you with the necessary skills to function adequately as a data scientist.

Additionally, there are some technical—and computer-based degrees—that can aid you begin a career in data science. Some of them include studies in, Computer Science, Statistics, Social Science, Physics, Economics, Mathematics and Applied Math. These degrees will imbibe some important skills related to data science in you—namely, coding, experimenting, managing large amounts of data, solving quantitative problems and many others.

Step 2: Choose an area of specialization

There rarely exists an organization, agency or business today that doesn’t require the expertise of a data scientist. Hence, it is important that after acquiring the necessary education to start a career as a data scientist, you need to choose an area of specialization in the field you wish to work in.

Some of the specializations that exist in data science today include automotive, marketing, business, defence, sales, negotiation, insurance and many others.

Step 3: Kick start your career as a data scientist

After acquiring the necessary skills to become a data scientist, it is important to get a job in the filed and company of your choice where you can acquire some experience.

Many organizations offer valuable training to their data scientists and these pieces of training are typically centred around the specific internal systems and programs of an organization. Partaking in this training allows you learn some high-level analytical skills that were not taught during your various school programs—especially since data science is a constantly evolving field.

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Artificial intelligence pioneers win tech’s ‘Nobel Prize’

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Computers have become so smart during the past 20 years that people don’t think twice about chatting with digital assistants like Alexa and Siri or seeing their friends automatically tagged in Facebook pictures.

But making those quantum leaps from science fiction to reality required hard work from computer scientists like Yoshua Bengio, Geoffrey Hinton and Yann LeCun. The trio tapped into their own brainpower to make it possible for machines to learn like humans, a breakthrough now commonly known as “artificial intelligence,” or AI.

Their insights and persistence were rewarded Wednesday with the Turing Award, an honor that has become known as technology industry’s version of the Nobel Prize. It comes with a $1 million prize funded by Google, a company where AI has become part of its DNA.

The award marks the latest recognition of the instrumental role that artificial intelligence will likely play in redefining the relationship between humanity and technology in the decades ahead.

Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society,” said Cherri Pancake, president of the Association for Computing Machinery, the group behind the Turing Award.

Although they have known each other for than 30 years, Bengio, Hinton and LeCun have mostly worked separately on technology known as neural networks. These are the electronic engines that power tasks such as facial and speech recognition, areas where computers have made enormous strides over the past decade. Such neural networks also are a critical component of robotic systems that are automating a wide range of other human activity, including driving.

Their belief in the power of neural networks was once mocked by their peers, Hinton said. No more. He now works at Google as a vice president and senior fellow while LeCun is chief AI scientist at Facebook. Bengio remains immersed in academia as a University of Montreal professor in addition to serving as scientific director at the Artificial Intelligence Institute in Quebec.

“For a long time, people thought what the three of us were doing was nonsense,” Hinton said in an interview with The Associated Press. “They thought we were very misguided and what we were doing was a very surprising thing for apparently intelligent people to waste their time on. My message to young researchers is, don’t be put off if everyone tells you what are doing is silly.” Now, some people are worried that the results of the researchers’ efforts might spiral out of control.

While the AI revolution is raising hopes that computers will make most people’s lives more convenient and enjoyable, it’s also stoking fears that humanity eventually will be living at the mercy of machines.

Bengio, Hinton and LeCun share some of those concerns especially the doomsday scenarios that envision AI technology developed into weapons systems that wipe out humanity.

But they are far more optimistic about the other prospects of AI empowering computers to deliver more accurate warnings about floods and earthquakes, for instance, or detecting health risks, such as cancer and heart attacks, far earlier than human doctors.

“One thing is very clear, the techniques that we developed can be used for an enormous amount of good affecting hundreds of millions of people,” Hinton said.

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