Building A.I. That Can Build A.I.

SAN FRANCISCO — They are a dream of researchers but perhaps a nightmare for
highly skilled computer programmers: artificially intelligent machines that can build other artificially intelligent machines. With recent speeches in both Silicon Valley and China, Jeff Dean, one of Google’s leading engineers, spotlighted a Google project called AutoML. ML is short for machine learning, referring to computer algorithms that can learn to perform particular tasks on their own by analyzing data. AutoML, in turn, is a machine-learning algorithm that learns to build other machine-learning algorithms.
With it, Google may soon find a way to create A.I. technology that can partly take the humans out of building the A.I. systems that many believe are the future of the technology industry. The project is part of a much larger effort to bring the latest and greatest A.I. techniques to a wider collection of companies and software developers.
The tech industry is promising everything from smartphone apps that can recognize
faces to cars that can drive on their own. But by some estimates, only 10,000 people worldwide have the education, experience and talent needed to build the complex
and sometimes mysterious mathematical algorithms that will drive this new breed of artificial intelligence. The world’s largest tech businesses, including Google, Facebook and Microsoft, sometimes pay millions of dollars a year to A.I. experts, effectively cornering the market for this hard-to-find talent. The shortage isn’t going away anytime soon, just because mastering these skills takes years of work. The industry is not willing to wait. Companies are developing all sorts of tools that will make it easier for any operation to build its own A.I. software, including things like image and speech recognition services and online chatbots. “We are following the same path that computer science has followed with every new type of technology,” said Joseph Sirosh, a vice president at Microsoft, which recently unveiled a tool to help coders build deep neural networks, a type of computer algorithm that is driving much of the recent progress in the A.I. field. “We are eliminating a lot of the heavy lifting.” This is not altruism. Researchers like Mr. Dean believe that if more people and companies are working on artificial intelligence, it will propel their own research. At the same time, companies like Google, Amazon and Microsoft see serious money in the trend that Mr. Sirosh described. All of them are selling cloud-computing services that can help other businesses and developers build A.I. “There is real demand for this,” said Matt Scott, a co-founder and the chief technical officer of Malong, a start-up in China that offers similar services. “And the tools are not yet satisfying all the demand.” This is most likely what Google has in mind for AutoML, as the company continues to hail the project’s progress. Google’s chief executive, Sundar Pichai, boasted about AutoML last month while unveiling a new Android smartphone. Eventually, the Google project will help companies build systems with artificial intelligence even if they don’t have extensive expertise, Mr. Dean said. Today, he estimated, no more than a few thousand companies have the right talent for building A.I., but many more have the necessary data. “We want to go from thousands of organizations solving machine learning problems to millions,” he said. Google is investing heavily in cloud-computing services — services that help other businesses build and run software — which it expects to be one of its primary economic engines in the years to come. And after snapping up such a large portion of the worlds top A.I researchers, it has a means of jump-starting this engine. Neural networks are rapidly accelerating the development of A.I. Rather than building an image-recognition service or a language translation app by hand, one line of code at a time, engineers can much more quickly build an algorithm that learns tasks on its own. By analyzing the sounds in a vast collection of old technical support calls, for instance, a machine-learning algorithm can learn to recognize spoken words. But building a neural network is not like building a website or some run-of-the-mill smartphone app. It requires significant math skills, extreme trial and error, and a fair amount of intuition. Jean-Fransois Gagna, the chief executive of an independent machine-learning lab called Element AI, refers to the process as “a new kind of computer programming. In building a neural network, researchers run dozens or even hundreds of experiments across a vast network of machines, testing how well an algorithm can learn a task like recognizing an image or translating from one language to another. Then they adjust particular parts of the algorithm over and over again, until they settle on something that works. Some call it a “dark art,” just because researchers find it difficult to explain why they make particular adjustments. But with AutoML, Google is trying to automate this process. It is building algorithms that analyze the development of other algorithms, learning which methods are successful and which are not. Eventually, they learn to build more effective machine learning. Google said AutoML could now build algorithms that, in some cases, identified objects in photos more accurately than services built solely by human experts. Barret Zoph, one of the Google researchers behind the project, believes that the same method will eventually work well for other tasks, like speech recognition or machine translation. This is not always an easy thing to wrap your head around. But it is part of a significant trend in A.I. research. Experts call it “learning to learn” or “meta-learning.” Many believe such methods will significantly accelerate the progress of A.I. in both the online and physical worlds. At the University of California, Berkeley, researchers are building techniques that could allow robots to learn new tasks based on what they have learned in the past. “Computers are going to invent the algorithms for us, essentially,” said a Berkeley professor, Pieter Abbeel. “Algorithms invented by computers can solve many, many problems very quickly — at least that is the hope.” This is also a way of expanding the number of people and businesses that can build artificial intelligence. These methods will not replace A.I. researchers entirely. Experts, like those at Google, must still do much of the important design work. But the belief is that the work of a few experts can help many others build their own software. Renato Negrinho, a researcher at Carnegie Mellon University who is exploring technology similar to AutoML, said this was not a reality today but should be in the years to come. “It is just a matter of when,” he said.

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Game with US Game Jam

A cool online event that some of my student in various classes might have an interest in. Read there description below:

A game jam is an event where people get together and make games over the 48 hours of a weekend. While that may not sound like much time, you’d be surprised at what you can do with a motivated team and a short amount of time. Participants prototype, design and create a video game based on a secret theme that is announced at the start of the event. The constraints of the time limit and secret theme encourages creative thinking between participants with different backgrounds and skill sets, and results in small but innovative or experimental games. Don’t stress that you might not have a finished product by the end of the weekend. Game jams are about discovery, innovation, and creativity. Not everyone has a finished product at the end – one of your biggest takeaways is a valuable learning experience. The “jam” in “Game Jam” is a reference to the musical jam sessions performed by bands and artists. The biggest reason to join a game jam is to have fun. Either exploring a new gaming concept or practicing your artistic skills, you will have a blast learning and making new friends – all while developing and playing video games!

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Teaching coding in Canadian schools: How do the provinces measure up? 

As Canada’s tech industry grows, people with coding skills are in increasingly higher demand, which means young people entering the workforce are concentrating more on computers and how to master the code and manipulate the data they run on. A 2016 study by the Information and Communications Technology Council predicts 182,000 skilled information and communications technology (ICT) workers will be needed in 2019, with another 36,000 required in 2020. Caroline Burgess, a STEM education and career consultant in Hamilton, Ont., says a bidding war has erupted amongst companies searching for computer-savvy employees. She says coding has become an “essential skill.” Morgan Rodwell, a chemical engineer with the Alberta firm Fluor, said that’s true of his industry. “You can’t just rely on a bunch of computer scientists, who understand how the computer works, but don’t understand the domain and the problem you’re really trying to solve outside the code,” he said. Burgess argues that to set children up for success, coding should be taught early in Canadian schools along with core curriculum such as math, English and science. So, how is coding taught in Canadian schools? Nova Scotia, New Brunswick and BC: Making coding mandatory As of August 2017, coding is already a mandatory part of the curriculum in Nova Scotia up to Grade 6.  In a statement, the education ministry says primary to Grade 3 students “use floor robots to learn sequencing and programming,” while students from grades 4 to 6 work with “invention kits.” A spokesperson for the ministry says coding is optional through grades 7 to 12, as the province works to further renew its curriculum with coding in mind. There are also activities like the Hour of Code that allow students to take part in maker-spaces and robotic competitions, which is a program that New Brunswick also participates in. In a statement, a spokesperson for New Brunswick’s education ministry said coding has been made mandatory as part of its Middle School Technology Education course for Grades 6 to 8. Outside of those grades, the province has introduced it as an elective. Students can also take part in a “virtual co-op” with information and communications technology (ICT) businesses that have partnered with the government. “We are providing training to interested teachers to foster more technology-related teaching, including the use of coding, in all areas of instruction,” said Kelly Cormier, communications officer with the Department of Education and Early Childhood Development. The B.C. government has also announced plans to make coding mandatory in schools across the province. That decision was made in 2016, by the then-Liberal government led by Christy Clark. In June 2016, the ministry provided school districts with a $6-million injection to “support coding and curriculum implementation.” The current NDP government plans to keep that promise, saying it plans to introduce coding as a core part of the curriculum in the 2018-19 school year for students in Grades 6 to 9. “The skills coding teaches can be used in almost any field and basic coding can be the launch pad to a career in the tech sector,” a spokesperson from the B.C. Education Ministry said in a statement. Alberta and Manitoba: Looking at their options Alberta Education Minister David Eggen said in a statement the ministry is meeting with Albertans on the topic and has also sat down with researchers to discuss the importance of “including coding in the curriculum.” “We know that the world is changing and just like critical thinking, computational thinking prepares students to address real-world problems and provides more economic opportunities after graduation,” the statement read. Manitoba, meanwhile, has said it is “studying the approach taken in other provinces.” In the meantime, it is examining the effects of a pilot program called Coding Quest, which was launched in cooperation with The Learning Partnership, to create a more “systematic approach to teaching coding in elementary schools.” Four provinces are taking part, including Ontario. The superintendent of education at Pembina Trails School division in Manitoba, which is one of the school boards included in the pilot, suggested his students have become more engaged in their own learning after being taught these skills. “We believe strongly in giving our students an advantage – a leg up, if you will – on advancements, on innovation, on creativity,” Ted Fransen said, “and coding is something that, I believe, every student should have at least a rudimentary awareness or knowledge of, because we live in a digital society – and their real world is digital.” Ontario and Saskatchewan: an optional part of the curriculum Ontario and Saskatchewan have both included coding as an optional part of the curriculum to varying degrees. Ontario said in a statement that as of August 2017, coding is not a mandatory part of the curriculum but that teachers are encouraged to “use information and technology tools in their teaching practice.” It says resources are available to teachers and that the Teach Ontario program is there to help educators find “innovative ways” to engage with students through coding and programming. High school students also have the option to take computer science classes that include lessons on engineering and programming. Susan Nedelcov-Anderson, executive director of the Student Achievement and Supports Branch of Saskatchewan’s Education Ministry, said in her province, teachers of all levels are encouraged to go beyond the curriculum. “Teachers have a flexibility to incorporate a variety of instructional techniques and a variety of resources,” Nedelcov-Anderson said. “So, definitely coding, bringing in robots, would be an example of the flexibility that exists.” She says computer science classes, which include coding as part of the lesson plan, are optional for high school students but that they have “not had any conversations about mandatory coding in Saskatchewan.” Global News reached out to the governments of Quebec and The Northwest Territories but did not receive a response by publication time. 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