A closer look at how it offers the ability to go beyond typical predictive maintenance strategies.

Industry 4.0 is transforming the manufacturing world as companies take advantage of new smart connectivity technologies to optimize their factories. In the next four years alone, these new technologies will help manufacturers and suppliers add nearly $4 trillion in value, one recent reportpredicted.

Machine learning in particular has vast potential, allowing industrial engineers to go beyond traditional analytical tools and extract game-changing insights from the voluminous amounts of data they’re often already collecting on the factory floor. But amid all the hype, it’s crucial to understand exactly what machine learning can do, and what it can’t.

If you’ve already seen machine learning applied in a maintenance context, chances are that it was being used for anomaly detection—in other words, highlighting rare events, with the idea being predicting breakages before they happen. Predictive maintenance is a common use case for machine learning, because it seems so intuitive. If you know in advance when something will break down, you should be able to save a lot of money and time that you’d otherwise spend doing costlier reactive maintenance.

But there’s one problem with this technique: it doesn’t work reliably.

Anomaly detection works by dumping all your historical and real-time data for a given machine into the software, so the machine learning models can learn the machine’s “normal” behavior. When the machine exceeds the specified range—for example, spiking to a temperature of 250 degrees, in contrast to the usually observed 200—the software sends you a notification that something isn’t right.

However, factories are complex. Machine temperature fluctuates based on hundreds of factors, from production volume to the season. This has major implications for machine learning models. They often make mistakes when learning “normal” behavior, resulting in numerous false positives.

The biggest cost of a false positive (in addition to wasting your time) is that it erodes trust in the technology. In extreme cases, this can result in a cry wolf scenario like theBP leak, which happened because there were so many false positives, the engineers shut down the alarms.

Seeing the Big Picture

While predicting events might sound tempting, you’ll get sounder results by usingexplainable machine learning to forecast trends. Imagine if the weather app told you: Today, the pressure is much higher than the seasonal average, so today you can expect an abnormal weather event.

Compare that with the typical weather forecast: Next Monday, we’re likely to see light rain, based on various factors such as temperature and pressure.

Which is more useful?

Explainable machine learning works the same way. By analyzing trends over time, these models can predict when certain machines will need maintenance, and even reveal how to prolong their lifetime.

After detecting erratic sensor data from a heat exchanger, for instance, it might suggest that the device will need to be serviced in six weeks’ time. If you have a scheduled maintenance shutdown in two weeks, you know that the machine can be safely ignored until the next shutdown, letting you focus on other high-priority tasks.

You can also use machine learning to figure out how to extend a device’s lifetime, delaying the need for costly repairs. In a typical factory, many factors contribute to degradation. By using explainable machine learning to see what makes specific equipment age faster, you can take action to prolong its usefulness.

And while anomaly detection methods typically rely on a mysterious black box algorithm, explainable machine learning allows you to take advantage of domain experts on the factory floor, who can tell the software which trends to forecast based on their experience. The software can then analyze these trends over time and make predictions that relate directly to engineers’ needs and priorities.

Machine learning has vast potential to transform daily maintenance operations. It will help you save time, money, and effort and understand the interconnected nature of your factory operations, which will be increasingly critical as we move towards a smartly connected future.

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Quantum AI and quantum computing are transformational technologies enabling a revolutionary future.

Quantum AI refers to the use of quantum computing for the computation of machine learning algorithms. With the computational advantages of quantum computing, quantum AI can now achieve results that were not possible with classical computers.

Alan Turing published a paper on Computing Machinery and Intelligence in 1950, and since then computers have come a long way. In the current modern age, computer limitations are gradually fading away, and machine learning has the ability to learn from its experiences. Traditionally, this type of intelligence was only achievable by using multiple computers and complicated machine learning algorithms. However, Nature Nanotechnology journal had a paper published recently where scientists proposed a new method – designing a computer with embedded intelligence and using the atom’s quantum spins to revolutionize computing as we know.

Next-Gen Computing

To understand this concept, let cover the basics of neuromorphic computing. In layman’s language, neuromorphic computing attempts to imitate the way a human brain works. From a technical perspective, neuromorphic computing is concerned with computer engineering where the elements of a computer, both hardware, and software, are wired according to the human nervous system and cerebral system.

Engineers study several disciplines like computer science, biology, mathematics, electronic engineering, and physics to create accurate neural structures. Neuromorphic computing aims to create devices that can learn, retain information, and make logical deductions the way a human brain does, a cognition machine. Alongside, it also attempts to prove how the human brain works by scavenging new information.

As a step forward in artificial intelligence technology, neuromorphic computing allows robots embedded with small computing hardware to make their own decisions in the future.

The Quantum Brain

The Quantum brain is a prime example of neuromorphic computing, the future of computing. Our human brains use signals sent by our neurons to make all kinds of computations. Similarly, the quantum brain uses cobalt atoms on a superconducting black phosphorus surface to imitate the process of human brain signals.

Cobalt atoms have quantum properties like unique spin states which carry information to stimulate ‘neuron firing’ with applied voltages. This helped the atoms to achieve a self-adaptive behavior based on the external stimuli.

Can AI Work With A Quantum Brain?

Artificial intelligence is an evolving technology, but it still has not overcome technological limitations. But with quantum computing, obstacles to achieving artificial general intelligence, AGI, can be discarded. Quantum computing can rapidly train machine learning models to generate optimized algorithms. Quantum computing can power an optimized and steady AI to complete analysis in a short time, as opposed to years of work that would delay any and all technological advancements.

According to researchers, a realistic aim for quantum AI is to replace traditional algorithms with quantum algorithms. These quantum algorithms can have several use cases to further advancements.

• Developing quantum algorithms for traditional learning models can provide possible boosts to the deep learning training process. Quantum computing can help machine learning by presenting the optimal solution set of the weights of artificial neural networks, quickly.

• When traditional decision-making problems are formulated with decision trees, the next course of action to reach the solution sets is by creating branches for a particular point. However, this method becomes complicated when the problem is too complex. Quantum algorithms can solve the problem faster.

Can neuroscience-inspired quantum computing and AI mesh? Yes, says several similarities between the brain and machine learning techniques like deep learning. Is that future near? Yes and no. Right now, the quantum AI industry needs to work to eliminate immaturities in the technology and achieve crucial milestones such as less error-prone and more powerful computing, developing the right AI applications where quantum computing can outperform traditional computing, and creating a widely adopted open-source modeling and training frameworks. These milestones will push quantum AI towards future developments.

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Within a short span of time and with few instructions, people can solve complex problems from scratch, for instance, loading the trunk of a car with seemingly too many objects. This is the core of human intelligence—its rapid and flexible nature. What is the cognitive scheme that allows us to create novel and complex strategies? And do “intelligent” machines use similar or fundamentally different schemes?

To answer these questions, scientists at the Champalimaud Centre for the Unknown in Portugal, and the University of California, Berkeley, created Hexxed. This mobile game consists of a series of fun and challenging puzzles designed to provide unique insight into how intelligence works. This free app has compatible versions for both iPhone and Android.

Taking science out of the lab

“Hexxed joins a global trend of citizen science games in which individuals around the world can contribute to scientific discoveries by simply playing,” says Gautam Agarwal, one of the scientists who developed the game as part of his research project in the lab of Zachary Mainen at Champalimaud.

Why move the experiment outside of the lab? According to Agarwal, this is the best way to gather data sets that are diverse and large enough to tap into difficult questions, such as how age and cultural background shape human thinking. “Experiments in laboratory conditions have a limited number of subjects, and are often repetitive and dull. In contrast, online games can be played by an unrestricted number of people worldwide, and inspire players to participate fully by immersing them in an evolving stream of experiences.”

Human vs. machine intelligence

The diversity of the data goes even further, as the team plans to use the game to learn not only about human intelligence, but about machine intelligence, as well. Mattia Bergomi, a researcher involved in the study, points out that video games are commonly used to test the capacity of artificial intelligence, but often fall short of the mark.

“The majority of games fall into one of two categories,” he explains. “On one extreme, there are challenging games that can only be solved with complex strategies. This results in problem-solving approaches that are difficult to formulate mathematically and therefore difficult to compare across subjects. On the other extreme, you have simple games that can easily be described mathematically. But then, those are not challenging enough to draw out intelligent problem-solving schemes.”

Hexxed was developed to bridge between the two extremes: it’s quite challenging, but it can still be described by simple mathematical constructs. “This unique design will allow us to compare strategies adopted by humans with those generated by machines in a systematic and comprehensive manner,” Agarwal adds.

Game on!

So what is intelligence and how does it vary across humans and machines? Play Hexxed and we may have an answer soon enough.

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Ninjas in Pyjamas is partnering with Esports Charts, a data-mining and analytical agency for esports, the organization announced today. Esports Charts is “one of the largest public sources of streaming analytics” and it also covers traditional sports and entertainment.

Esports Charts “will provide up-to-date analytical data, which will help Ninjas in Pyjamas make the right decisions,” according to NiP’s announcement.

“As esports continues down the path of professionalization, the value of accurate data grows exponentially,” said Michael Tidebäck, head of product and relations at Ninjas in Pyjamas. “More is unquestionably better, and ESCharts is market-leading when it comes to match tracking statistics.”

Esports Charts is also partnered with Alliance, Fnatic, Team Liquid, StarLadder, TSM, and Astralis. The agency collects, researches, processes, and analyzes data and statistics of live tournaments, real-time in-game events, player and team performances, and even spectator reactions and emotional contexts.

Data analysis is a step toward professionalization in esports. It’s been highly used in traditional sports. A famous example is the 2011 movie Moneyball featuring Brad Pitt and Jonah Hill. 

Moneyball is based on Michael Lewis’ 2003 nonfiction book of the same name about the Oakland Athletics baseball team and its general manager, Billy Beane. The book and movie portray the start of statistics and analytics being used in baseball, known as sabermetrics.

NiP, founded in 2000, is home to players in CS:GO, Dota 2, VALORANT, FIFA, and Rainbow Six, according to its website.

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Artificial Intelligence can now beat all 57 Atari 2600 games. Alphabet subsidiary DeepMind has revealed that their Agent57 can beat humans on the classic 1977 console. This is pretty big news, but not unsurprising. A.I. has been heading this way for quite a while, especially after supercomputer AlphaGo won the final match against the best human Go player in the past decade, Lee Sedol, in 2016. According to DeepMind, “Agent57 combines an algorithm for efficient exploration with a meta-controller that adapts the exploration and long vs. short-term behavior of the agent.”

In other words, Agent57 uses machine learning called deep reinforcement, which allows it to learn from mistakes and keep improving over time. There’s footage of Agent57 playing the Alien Atari game and it’s pretty remarkable to watch as the computer nearly dominates the video game throughout the 30-minute video. DeepMind released a research paper explaining why video games are such a good way to test A.I. You can read a portion of it below.

DeepMind uses the same machine learning for Agent57 that the aforementioned AlphaGo utilized to master Go. While other A.I. systems have been tackling Atari games for a while, Agent57 is outdoing them all and is well on its way to being able to beat humans at all of the games without any trouble. Montezuma’s Revenge, Pitfall, Solaris, and Skiing are all games that A.I. struggles with due to the strategy involved, which is usually trouble for A.I. systems.

While most A.I. systems have struggled with more of the strategy-based games, DeepMind’s Agent57 was able keep learning from mistakes. According to the research paper, the longer the system was able to run, the better the results, just like human learning. There are some drawbacks to this type of learning though, which is gone over in the research paper. The research goes on below.

Computation and time will more than likely be what DeepMind starts working on next. Whatever the case may be, this is a pretty big breakthrough that will only get better in time, which is could be scary at the same time. Do we really need these computers to be thinking for themselves and beating humans at every single Atari game, including E.T.? You can check out Agent57 dominating the Alien game below, thanks to the DeepMind YouTube channel.

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Players of the science-fiction video game StarCraft II faced an unusual opponent this summer. An artificial intelligence (AI) known as AlphaStar — which was built by Google’s AI firm DeepMind — achieved a grandmaster rating after it was unleashed on the game’s European servers, placing within the top 0.15% of the region’s 90,000 players.

The result, published on 30 October in Nature¹, shows that an AI can compete at the highest levels of StarCraft II, a massively popular online strategy game in which players compete in real time as one of three factions — the human Terran forces or the aliens Protoss and Zerg — battling against each other in a futuristic warzone.

DeepMind, which previously built world-leading AIs that play chess and Go, targeted StarCraft II as its next benchmark in the quest for a general AI — a machine capable of learning or understanding any task that humans can — because of the game’s strategic complexity and rapid pace.

“I did not expect AI to essentially be superhuman in this domain so quickly, maybe not for another couple of years,” says Jon Dodge, an AI researcher at Oregon State University in Corvallis.

In StarCraft II, experienced players multitask by managing resources, executing complex combat manoeuvres and ultimately out-strategizing their opponents. Professionals play the game at a breakneck pace, making more than 300 actions per minute. The machine-learning techniques underlying DeepMind’s AI rely on artificial neural networks, which learn to recognize patterns from large data sets, rather than being given specific instructions.

DeepMind first pitted AlphaStar against high-level players in December 2018, in a series of laboratory-based test games. The AI played — and beat — two professional human players. But critics asserted that these demonstration matches weren’t a fair fight, because AlphaStar had superhuman speed and precision.

Before the team let AlphaStar out of the lab and onto the European StarCraft II servers, they restricted the AI’s reflexes to make it a fairer contest. In July, players received notice that they could opt-in for a chance to potentially be matched against the AI. To keep the trial blind, DeepMind masked AlphaStar’s identity.

“We wanted this to be like a blind experiment,” says David Silver, who co-leads the AlphaStar project. “We really wanted to play under those conditions and really get a sense of, ‘how well does this pool of humans perform against us?’”

AlphaStar’s training paid off: it crushed low-ranking opponents and ultimately amassed 61 wins out of 90 games against high-ranking players.

Challenging complexity

StarCraft II’s complexity poses immense challenges to AIs. Unlike chess, StarCraft II has hundreds of ‘pieces’ — soldiers in the factions’ armies — that move simultaneously in real time, not in an orderly, turn-based fashion. Whereas a chess piece has a limited number of legal moves, AlphaStar has 10²⁶ actions to choose from at any moment. And StarCraft II, unlike chess, is a game of imperfect information — players often cannot see what their opponent is doing. This makes it unpredictable.

For nearly a decade, researchers have pitted StarCraft– and StarCraft II-playing AIs against one another in an annual competition. However, unlike AlphaStar, most of these ‘bots’ relied on hard-coded rules, rather than neural networks that can self-train. Oriol Vinyals, who now co-leads the AlphaStar project, was on the team from the University of California, Berkeley, that won the first competition in 2010.

“Back then, I kind of started thinking maybe we should just do [machine] learning, but it was just too early,” says Vinyals.

In 2016, Vinyals joined DeepMind, where he began working on AIs that could teach themselves how to play StarCraft II. AlphaStar started its training by learning to imitate from a set of nearly one million human games. To improve AlphaStar’s play further, DeepMind created a league where versions of the AI competed against one another. This method makes sense for a game like StarCraft II in which no one strategy is best — as well as for many other real-life applications of AI, says Kai Arulkumaran, an AI researcher at Imperial College London.

Perceptive players

DeepMind also put constraints on AlphaStar to make sure the AI was truly out-thinking and not just out-clicking its human opponents. Because the game rewards an ability to click rapidly, a computer that clicks at superhuman speed might beat humans without being more intelligent or making better decisions. So DeepMind limited the speed of AlphaStar’s reflexes to that of experienced human players.

Under those conditions, and after 27 days of training, AlphaStar placed within the top 0.5% of all players on the European server.

After 50 games, however, DeepMind hit a snag. Some players had noticed that three user accounts on the Battle.net gaming platform had played the exact same number of StarCraft II games over a similar time frame — the three accounts that AlphaStar was secretly using. When watching replays of these matches, players noticed that the account owner was performing actions that would be extremely difficult, if not impossible, for a human. In response, DeepMind began using a number of tricks to keep the trial blind and stop players spotting AlphaStar, such as switching accounts regularly.

The final version of AlphaStar relied on a cumulative 44 days of training and frequently ran into professional players. The AI wasn’t able to beat the best player in the world, as AIs have in chess and Go, but DeepMind considers its benchmark met, and says it has completed the StarCraft II challenge.

Other AI scientists aren’t yet convinced that AlphaStar can claim complete victory. Dave Churchill, an AI researcher at Memorial University of Newfoundland in St John’s, Canada, thinks that AlphaStar still has a number of weaknesses, such as a vulnerability to strategies it hasn’t seen before.

“AlphaStar is very impressive, and is definitely the strongest AI system for any StarCraft game to date,” he says. “That being said, StarCraft is nowhere near being ‘solved’, and AlphaStar is not yet even close to playing at a world champion level.”

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In an e-commerce store, if you want to buy a black kurti, you do the search and use a few filters such as size, style and material to find the apparel of your choice. This data then gets stored by the company and is used to improve customer experience subsequently. However, offline stores are at a disadvantageous position as data never gets stored with them and, as a result, they end up depending on information provided by store staff and on their insight, which are not always so accurate.

Bengaluru-based Capillary Technologies is providing offline retailers the power to improve staff effectiveness and convert the customer queries into product sales through artificial intelligence (AI) and machine learning. Harnessing the power of AI, the company is building a series of products for its in-store vision to empower retailers.

“We will be coming out with a product called Campaign Personalisation by August for which we will be using machine learning algorithms to boost discovery and personalisation, whether over SMSs, emails or push notifications,” said Aneesh Reddy, Cofounder and CEO, Capillary Technologies.

The technology looks at the past behaviour, when the person bought a product, when the individual responded to the campaign and then starts suggesting about the product and the time slot in which it needs to be marketed.

“We have been able to get a 30 per cent higher hit rate from these campaigns,” said Ganesh Lakshminarayanan, Chief Operating Officer.

Textile manufacturer Arvind Lifestyle has already started using Campaign Personalisation as a pilot and has seen twice the customer response rate as compared to traditional marketing. Capillary would expand the technology to about 40 brands, including Bata and VF Corporation, and will run about 60 marketing campaigns in July. However, it will be ready for commercial launch in August, said Reddy.

The Warbug Pincus and Sequoia Capital-backed company is also working on the store staff segment and will be coming up with a solution dubbed as store sense in the next quarter, giving a sense of what’s happening at the retail store, to the brands. It will provide insights into each customer’s behaviour pattern in a store based on their interactions with the store employees to understand their requirements, category preferences, and propensity to purchase. “It is like a personalised trainer for the staff. We are building each user’s offline clickstream using computer vision and speech detection to provide personalised engagement and recommendations to each user in an offline world,” explained Reddy.

The company has already launched the first product for its in-store vision called the VisitorMetrix Plus earlier this year. It provides a detailed store visitor analytics, including the footfall trend, visitor demographics, visitor-fashion profiling to help brands optimise their store operations, to increase sales and conversion from each store. The technology is already being used by clients such as Wrangler, Lee and Shoppers Stop across 1,500 stores around the globe.

The company is also toying with the idea of facial recognition on the same lines as Chinese messaging app WeChat. In this new retail method, shoppers in China can create an account before they enter a store and link it to their WeChat account paying only with a face scan.

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