Bengaluru, June 13 (IANS) Amazon India on Sunday announced the launch of ML Summer School which will provide an integrated learning experience for students to gain applied Machine Learning (ML) skills.

A batch of students from select tech campuses in India will be presented with the opportunity to engage through virtual classroom tutorials followed by interactive Q&A sessions with scientists at Amazon.

For students with prior exposure to certain areas of ML, the programme can act as a refresher course, while additionally providing a practical perspective on ML applications in industry, the company said in a statement.

“With the pace of advancements in ML, we are proactively helping students to learn about the latest trends in the field of ML and apply them to solve real-world problems,” said Rajeev Rastogi, VP, India Machine Learning at Amazon.

“Our aim is to prepare students for science roles — this will help to reduce the gap between the growing demand for ML roles across companies and the talent pool with applied ML skills,” he added.

ML Summer School is open to engineering students in their pre-final/final year of Bachelors, Masters or PhD studies across select tech campuses in 2021.

Participants of ML Summer School will be identified through an online assessment.

They will also have access to the Amazon Research Days (ARD) conference where they can learn about technology trends in industry through presentations from renowned ML leaders around the world.

The curriculum of ML Summer School will cover the fundamental concepts in ML while linking them to practical industry applications through an immersive three-day course.

Students will get to learn first-hand on how advanced ML techniques such as Deep Learning and Probabilistic Graphical Models can be leveraged to solve specific business problems in the e-commerce domain such as demand forecasting, catalogue quality, product recommendations, search ranking and online advertising.

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You can call it the ‘revenge of the computer scientist.’ An algorithm that made headlines for mastering the notoriously difficult Atari 2600 game Montezuma’s Revenge, can now beat more games, achieving near perfect scores, and help robots explore real-world environments. Pakinam Amer reports.

This is Scientific American’s 60 Second Science. I’m Pakinam Amer.

Whether you’re a pro gamer or you dip your toes in that world every once in a while, chances are you got stuck while playing a video game once, or was even gloriously defeated by one.

I know I have.

Maybe, in your frustration, you kicked the console a little. Maybe you took it out on the controllers or—if you’re an 80’s kid like me—made the joystick pay.

Now, a group of computer scientists from UberAI are taking revenge for all of us who’ve been in this situation before.     

Using a family of simple algorithms, tagged ‘Go-Explore’, they went back and beat some of the most notoriously difficult Atari games whose chunky blocks of pixels and 8-bit tunes had once challenged, taunted and even enraged us.

<swish>

But what does revisiting those games from the 80s and 90s accomplish, besides fulfilling a childhood fantasy?

According to the scientists, who published their work in Nature, experimenting with solving video games that require complex, hard exploration gives way to better learning algorithms. They become more intelligent and perform better under real-world scenarios.

Joost Huinzinga: One of the nice things of Go-Explore is that it’s not just limited to video games, but that you can also apply it to practical applications like robotics.

That was Joost Huinzinga, one of the principal researchers at UberAI. Joost developed Go-Explore with Adrien Ecoffet and other scientists.

So how does it actually work?

Let’s start with the basics. When AI processes images of the world in the form of pixels, it does not know which changes should count and which should be ignored. For instance, a slight change in the pattern of the clouds in the sky in a game environment is probably unimportant when exploring said game, but finding a missing key certainly is —butto the AI, both involve changing a few pixels in that world.

This is where deep reinforcement learning comes in. It’s an area of machine learning that helps an agent analyze an environment to decide what matters and which actions count through feedback signals in the form of extrinsic and intrinsic rewards.

Joost Huinzinga: This is something that animals, basically, constantly do. You can imagine, if you touch a hot stove, you immediately get strong negative feedback like, ‘hey, this is something you shouldn’t do in the future.’ If you eat a bar of chocolates, assuming you like chocolates, you immediately get a positive feedback signal like, ‘hey, maybe I should seek out chocolate more in the future.’ The same is true for machine learning. These are problems where the agent has to take some actions, and then maybe it wins a game.

Creating an algorithm that can navigate rooms with traps, obstacles to jump over, rewards to collect and pitfalls to avoid, means that you have to create an artificial intelligence that is curious and that can explore an environment in a smart way.

This helps it decide what brings it closer to a goal, or how to collect hard-to-get treasures.

Reinforcement learning is great for that but it isn’t perfect in every situation.

Joost Huinzinga: In practice, reinforcement learning works very well, if you have very rich feedback, if you can tell, ‘hey, this move is good, that move is bad, this move is good, that move is bad.’

In Atari games like Montezuma’s Revenge, the game environment offers little feedback and its rewards can intentionally lead to dead ends. Randomly exploring the space just doesn’t cut it.

Joost Huinzinga: You could imagine, and this is especially true in video games like Montezuma’s revenge, that sometimes you have to take a lot of very specific actions, you have to dodge hazards, jump over enemies, you can imagine that random actions like, ‘hey, maybe I should jump here,’ in this new place, is just going to lead to a ‘Game Over’ because that was a bad place to jump … especially if you’re already fairly deep into the game. So let’s say you want to explore level two, if you start taking random actions in level one and just randomly dying, you’re not going to make progress on exploring level two.

You can’t rely on ‘intrinsic motivation’ alone, which in the context of artificial intelligence typically comes from exploring new or unusual situations.

Joost Huinzinga: Let’s say you have a robot and it can go left into the house and right into the house, let’s say at first it goes left, it explores left, meaning that it gets this intrinsic reward for a while. It doesn’t quite finish exploring left and at some point, the episode ends and it starts anew in the starting room. This time it goes right, it goes fairly far into the room on the right, it doesn’t quite explore it. And then it goes back to the starting room. Now the problem is because it has gone both left and right and basically it’s already seen the start, it no longer gets as much intrinsic motivation from going there.

In short, it stops exploring and counts that as a win.

Detaching from a place that was previously visited after collecting a reward doesn’t work in difficult games, because you might leave out important clues.

Go-Explore goes around this by not rewarding some actions, such as going somewhere new.

Instead, it encourages “sufficient exploration” of a space, with no or little hints, by enabling its agent to explicitly ‘remember’ promising places or states in a game.

Once the agent keeps a record of that state, it can then reload it and intentionally explore–what Adrien and Joost call, the “first return, then explore” principle.

According to Adrien, leaning on another form of learning called imitation learning, where agents can mimic human tasks, their AI can go a long way, especially in the field of robotics.

Adrien Ecoffet: You have a difference between the world that you can train in and the real world. So one example would be if you’re doing robotics … you know, in robotics, it’s possible to have simulations of your robotics environments. But then, of course, you want your robot to run in the real world, right? And so what you can do, then? If you’re in a situation like that, of course, the simulation is not exactly the same as the environment, so just having something that works in simulation is not necessarily sufficient. We show that in our work … What we’re doing is that we’re using existing algorithms that are called ‘imitation learning’. And what it is, is it just takes an existing solution to a problem and just makes sure that you can reliably use that solution, even when, you know, there are slight variations in your environment, including, you know, it being the real world rather than a simulation.

Adrien and Joost say their model’s strength lies in its simplicity.

It can be adapted and expanded easily into real-life applications such as, language learning or drug design.

That was 60 Seconds Science, and this is Pakinam Amer. Thank you for listening.

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Technology is indispensable, a critical factor for innovation, could adding robotics to the school curriculum add to the making young minds more ready for the gen next technology? There is considerable anecdotal evidence that students show a well interpreted enthusiasm involving robot programming.

To learn robotics, the internet offers plenty of resources both for the parents and teachers, for instance, robot kits such as Lego Mindstorms and Vex Robotics, offer simple programmable robots like Sphero balls, and lesson plans. Besides sophisticated, engaging robots such as the NAO robot are also available for the young minds.

Robotics in School Academia

Robotics as a subject is gaining momentum across schools worldwide. Robotics as a study field inculcates teamwork incorporating a range of skills, and subsequently promoting learning environment for people encompassing different talents. Robotics promotes a strong sense of teamwork, useful for students who struggle to learn in traditional classroom settings.

Robotics provides a launching pad for students to explore different learning pathways. Robotics lets students to develop an interest in coding and 3D printing, whereby students can build their own robotics learning pathways, offering an open platform where students can experiment and learn.

Robotics curriculum is instrumental in creating future leaders of tomorrow, by sharpening their communication and coding skills. Through the team work exercise, of putting the robot walk and work students learn to communicate as a team, coming together as an assortment of leaders, developing their personal strengths which would be essential throughout their lives.

Robots and Robotic Technologies Students Must Know

NAO Robot

The NAO humanoid robot is the ideal platform for teaching STEM concepts to students at all levels. NAO humanoid provides hands-on experience connecting theory with practice to help students discover a range of applications. NAO humanoid robot is designed to meet the instructional goals in education. NAO is widely used in global education market enabling new ways for pedagogy in classrooms.

3D Printers

3D printers have rapidly caught the attention of students and academia. 3D printing technology brings STEAM to life with 3D printed objects, engaging the entire classroom and sparking curiosity. Students can learn to build professional 3D design skills that simulate real-world engineering problems through advanced design thinking.

Pepper Robot

Robotics is the fastest growing and most advanced technology used in education and research today. Pepper is versatile and high-performing robot designed to meet the expectations of both teachers and researchers. Pepper offers an advanced platform for the in-depth study that includes cognitive computing, autonomous navigation and human-machine interaction etc.

Misty Robot

Misty robot lets the young minds bring coding and STEM to life. Misty is the autonomous roaming robot that students can program to move around for human interactions. Misty Robot allows students develop prototypes with the hardware and extend the robot’s functionality.

Summing up, it is generally agreed, that robots are a motivating tool for students to pursue STEM studies, and a pedagogical tool for STEM. In particular, they strengthen scientific and technological culture in academia. Additionally, they facilitate the knowledge transfer through trans-disciplinary activity-based projects. In today’s technological world, it is more important than ever to develop a strong technological pedagogy among students with the most in demand prebiotics education. Teaching robotics lessons to students can increase their ability to be future leaders, be innovative thinkers, develop teamworking skills and more productive members of society.

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The University of Virginia has received state clearance for its data science school.

The State Council of Higher Education for Virginia last week gave final approval to the creation of UVA’s School of Data Science, eight months after the Charlottesville university received a $120 million donation for the creation for the school.

“I am delighted that the School of Data Science has cleared its final hurdle and can officially move forward,” said UVA President Jim Ryan in a statement. “I want to thank the State Council of Higher Education for Virginia for sharing our excitement in this proposal, and Phil Bourne and his team at the Data Science Institute for their hard work.”

The university announced in January plans to open a School of Data Science in what will be UVA’s first new school since 2007 and its 12th in total. The majority of the funding is coming from a $120 million donation from the Charlottesville-based Quantitative Foundation, the largest private gift in UVA history.

“This is a historic moment for the University of Virginia and for the field of data science,” said Provost Elizabeth Magill. “The school is coming online at a time when the amount of available data in the world is more than doubling every two years, and there is soaring demand for qualified data scientists who can analyze and interpret vast amounts of data.”

Magill added: “A new school will catalyze research on daunting societal challenges and point the way toward their solutions. It will also allow us to prepare students to become responsible and creative leaders who are capable of using data science in a wide range of fields.”

Data Science Institute Director Philip E. Bourne has been appointed dean of the school.

UVA said in January that it plans for the new school to have a Ph.D. program, undergraduate programs in data science and data science certificates. The university also plans to expand its existing on-campus and online master’s program in data science.

“We envision the new School of Data Science at UVA as a ‘school without walls,’” Bourne said. “In its very structure and foundation, we will build collaborative and interdisciplinary opportunities through partnerships and physical spaces for shared research and education programs. The new school will combine a focus on research, education and service to build bridges across academic, geographic, commercial and cultural boundaries using responsible, open data science.”

The university will celebrate the official launch of the school at 4 p.m. Tuesday at the Dell 1 building on Central Grounds.

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Where does money go in public education? And how is that money spent?

These questions prompted the Office of the State Auditor to create a system to make data-driven decisions on spending in Utah schools, and BYU McKay School’s Educational Leadership and Foundations Department is collaborating with the state auditor to explore ways to effectively use this data.

At a recent Educational Leadership sponsored event, state auditor John Dougall said money generally doesn’t make a difference because schools don’t align spending with their goals.

“My belief, though, is money does make a difference when applied in the right place in the right manner with the right accountability,” said Dougall.

The data system, dubbed Project KIDS (Key Integrated Data Systems), will help schools track where their money is going and if the outcomes of their spending align with their goals. To do this, they are collecting big data from every school district and charter school in Utah. Big data refers to data sets that are too large or too complex to process with traditional software and thus require advanced analytic techniques (i.e., big data analytics).

The McKay School collaboration provides education prowess. As assistant professor Donny Baum explained, “Most of their team members have training in economics, statistics, and quantitative analysis in general, but they are looking for further insight specific to education and education policy,” said Baum. The department offers expertise through internships with education policy graduate students and formal research collaboration with faculty members.

Research analyst and BYU alumna, Anne Nollet, said that educational data exists in different places managed by different people. “We hope to bring it all together to one comprehensive data resource that districts and other stakeholders can use to make better decisions.” Nollet said they visualize the data in interactive, user-friendly dashboards.

One such dashboard is called “Spending per Student.” Users can see average spending per student as well as how much the school spends on a specific student when they search by identification number. Users can also look at spending for specific groups of students: low-income, ELL (English Language Learners), or special needs.

Nollet cautioned that her team is not making any judgments with this tool. “We’re hoping that the school district personnel … can use the data to start asking better questions, maybe finding a trend that they didn’t even know existed because they’ve never looked at it that way.”

The next step for Project KIDS is to collect data from the remaining Utah school districts within a year. The auditor’s office will then make these dashboards available to school districts, secondary stakeholders (researchers, advocacy groups, legislators), and eventually, the public, after sensitive data is anonymized.

“Behind every data point, there’s a story,” said Nollet. “We live in a world of big data. It’s out there, it’s not changing. … We want to help the school districts get on board with that and start using the data they’re collecting.”

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In this age of frequent deadly shootings, the majority of teens—and their parents—worry about the possibility of an attack at their own school. A new artificial intelligence security system is aiming to give parents and their kids some additional peace of mind.

Athena Security, created by Fortune 40 under 40 alum Lisa Falzone, is a camera that uses AI to recognize violent or criminal behavior, such as the pulling out a gun, and can report it to police. Falzone created the technology with her co-founder, Chris Ciabarra.

Current security cameras typically record via on-site servers, providing information to law enforcement only after a crime has been committed. Athena takes a preventative approach, using the cloud to relay information instantly. Whether it’s implemented at a business, school, or around a city, the system can send an alert directly to the owner’s phone when it recognizes dangerous motions like a gun being pointed, a knife being pulled, or people fighting.

Athena can also send the alert directly to police, if it’s configured to do so, providing them with real-time footage via an app. If there’s an attack at a school, police will know what’s happening and where to find the shooter when they arrive.

Athena’s pioneer system is already set up in one school, Archbishop Wood High School in Warminster, Pennsylvania, where it was implemented last week.

“We have a duty and responsibility to keep our students safe,” said Gary Zimmaro, the high school’s president, in a statement. “With 246 mass shootings in 2018 alone, Athena Security has calmed parent’s fears substantially.”

Athena’s most advanced systems, like the one at Archbishop Wood High School, are capable of coordinating with third party systems to lock doors, halt elevators, or communicate directly with people on site.

“Police can voice in and say, ‘Hey, you’ve been detected. Please put down the gun,’” says Falzone. “The feedback that we’ve gotten from law enforcement, especially for retailers, convenience stores, banks—criminals going after money—is that’ll be really helpful in deterring crime.”

Right now, Athena is only launching with gun detection, but soon it’ll be able to recognize other dangerous motions. And in a world of all-to-frequent false alarms, Athena claims its system is unique for its reliability: Falzone says Athena is 99% accurate in recognizing guns in a two-second window.

“We’ve basically perfected that, and so we’re already starting to work on fights, knives, and other crimes,” she tells Fortune. “We expect fights to be done in the next couple months, at least the first version of it.”

Security will be available in three tiers: Professional, Enterprise, and Unlimited Professional, with increasing feature sets. The price, paid per camera each month, ranges from $25 to $100, depending on the tier, says Falzone.

Company leadership is also putting together an advisory board to guide its growth. Current members include Greg Suhr, San Francisco’s former chief of police, who’s providing a law enforcement perspective; and Zac Bookman of OpenGov, who’s aiding with government partnerships.

While the security system is just getting started, Athena’s co-founders are no strangers to success: Falzone and Ciabarra are the founders of iPad point-of-sale company Revel Systems, for which they raised over $120 million and created 700 jobs.

The co-founders sold Revel Systems in early 2017, around the time of the Las Vegas shooting that left more than 50 dead. Falzone and Ciabarra began brainstorming how they could help prevent such crimes using their cloud tech expertise, and Athena was born.

“Our vision and mission is to use artificial intelligence for good,” says Falzone, “and to really help people and try to help save people’s lives.”

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