AI has finally come full circle.

A new suite of algorithms by Google Brain can now design computer chips—those specifically tailored for running AI software—that vastly outperform those designed by human experts. And the system works in just a few hours, dramatically slashing the weeks- or months-long process that normally gums up digital innovation.

At the heart of these robotic chip designers is a type of machine learning called deep reinforcement learning. This family of algorithms, loosely based on the human brain’s workings, has triumphed over its biological neural inspirations in games such as Chess, Go, and nearly the entire Atari catalog.

But game play was just these AI agents’ kindergarten training. More recently, they’ve grown to tackle new drugs for Covid-19, solve one of biology’s grandest challenges, and reveal secrets of the human brain.

In the new study, by crafting the hardware that allows it to run more efficiently, deep reinforcement learning is flexing its muscles in the real world once again. The team cleverly adopted elements of game play into the chip design challenge, resulting in conceptions that were utterly “strange and alien” to human designers, but nevertheless worked beautifully.

It’s not just theory. A number of the AI’s chip design elements were incorporated into Google’s tensor processing unit (TPU), the company’s AI accelerator chip, which was designed to help AI algorithms run more quickly and efficiently.

“That was our vision with this work,” said study author Anna Goldie. “Now that machine learning has become so capable, that’s all thanks to advancements in hardware and systems, can we use AI to design better systems to run the AI algorithms of the future?”

The Science and Art of Chip Design

I don’t generally think about the microchips in my phone, laptop, and a gazillion other devices spread across my home. But they’re the bedrock—the hardware “brain”—that controls these beloved devices.

Often no larger than a fingernail, microchips are exquisite feats of engineering that pack tens of millions of components to optimize computations. In everyday terms, a badly-designed chip means slow loading times and the spinning wheel of death—something no one wants.

The crux of chip design is a process called “floorplanning,” said Dr. Andrew Kahng, at the University of California, San Diego, who was not involved in this study. Similar to arranging your furniture after moving into a new space, chip floorplanning involves shifting the location of different memory and logic components on a chip so as to optimize processing speed and power efficiency.

It’s a horribly difficult task. Each chip contains millions of logic gates, which are used for computation. Scattered alongside these are thousands of memory blocks, called macro blocks, which save data. These two main components are then interlinked through tens of miles of wiring so the chip performs as optimally as possible—in terms of speed, heat generation, and energy consumption.

“Given this staggering complexity, the chip-design process itself is another miracle—in which the efforts of engineers, aided by specialized software tools, keep the complexity in check,” explained Kahng. Often, floorplanning takes weeks or even months of painstaking trial and error by human experts.

Yet even with six decades of study, the process is still a mixture of science and art. “So far, the floorplanning task, in particular, has defied all attempts at automation,” said Kahng. One estimate shows that the number of different configurations for just the placement of “memory” macro blocks is about 10^2,500—magnitudes larger than the number of stars in the universe.

Game Play to the Rescue

Given this complexity, it seems crazy to try automating the process. But Google Brain did just that, with a clever twist.

If you think of macro blocks and other components as chess pieces, then chip design becomes a sort of game, similar to those previously mastered by deep reinforcement learning. The agent’s task is to sequentially place macro blocks, one by one, onto a chip in an optimized manner to win the game. Of course, any naïve AI agent would struggle. As background learning, the team trained their agent with over 10,000 chip floorplans. With that library of knowledge, the agent could then explore various alternatives.

During the design, it worked with a type of “trial-and-error” process that’s similar to how we learn. At any stage of developing the floorplan, the AI agent assesses how it’s doing using a learned strategy, and decides on the most optimal way to move forward—that is, where to place the next component.

“It starts out with a blank canvas, and places each component of the chip, one at a time, onto the canvas. At the very end it gets a score—a reward—based on how well it did,” explained Goldie. The feedback is then used to update the entire artificial neural network, which forms the basis of the AI agent, and get it ready for another go-around.

The score is carefully crafted to follow the constraints of chip design, which aren’t always the same. Each chip is its own game. Some, for example, if deployed in a data center, will need to optimize power consumption. But a chip for self-driving cars should care more about latency so it can rapidly detect any potential dangers.

The Bio-Chip

Using this approach, the team didn’t just find a single chip design solution. Their AI agent was able to adapt and generalize, needing just six extra hours of computation to identify optimized solutions for any specific needs.

“Making our algorithm generalize across these different contexts was a much bigger challenge than just having an algorithm that would work for one specific chip,” said Goldie.

It’s a sort of “one-shot” mode of learning, said Kahng, in that it can produce floorplans “superior to those developed by human experts for existing chips.” A main throughline seemed to be that the AI agent laid down macro blocks in decreasing order of size. But what stood out was just how alien the designs were. The placements were “rounded and organic,” a massive departure from conventional chip designs with angular edges and sharp corners.

Human designers thought “there was no way that this is going to be high quality. They almost didn’t want to evaluate them,” said Goldie.

But the team pushed the project from theory to practice. In January, Google integrated some AI-designed elements into their next-generation AI processors. While specifics are being kept under wraps, the solutions were intriguing enough for millions of copies to be physically manufactured.

The team plans to release its code for the broader community to further optimize—and understand—the machine’s brain for chip design. What seems like magic today could provide insights into even better floorplan designs, extending the gradually-slowing (or dying) Moore’s Law to further bolster our computational hardware. Even tiny improvements in speed or power consumption in computing could make a massive difference.

“We can…expect the semiconductor industry to redouble its interest in replicating the authors’ work, and to pursue a host of similar applications throughout the chip-design process,” said Kahng.

“The level of the impact that [a new generation of chips] can have on the carbon footprint of machine learning, given it’s deployed in all sorts of different data centers, is really valuable. Even one day earlier, it makes a big difference,” said Goldie.

The post A Google AI Designed a Computer Chip as Well as a Human Engineer—But Much Faster appeared first on Artificial Intelligence.

Will computer vision take human jobs in the upcoming years with cutting-edge technologies?

There is a worldwide controversy whether computer vision will take over human jobs that can increase the rate of unemployment. The integration of cutting-edge technologies with AI algorithms into the existing computer system has introduced advanced computer vision. AI algorithms transform multiple sets of real-time data into appropriate business insights without any human intervention. But AI algorithms and computers require human assistance to complete multiple tasks efficiently and effectively. Have you ever wondered why there is an increase in human jobs in the field of AI in these recent years? Human skills are needed for the advancement in software development as well as innovating new technologies to boost productivity. Reputed companies and startups provide job opportunities such as computer vision engineer, computer vision scientist, deep learning specialist, software developer, data scientist, software engineer, lead scientist, data analytics lead, computer vision research engineer, and many more with lucrative salary package. That being said, we can claim that computer vision will not take human jobs but will ease the workload effectively to achieve higher ROI.

• PLANNING ON BECOMING A COMPUTER VISION ENGINEER? HERE’S WHAT YOU NEED TO KNOW
• INTEL’S MOVE TO LEVERAGE COMPUTER VISION SOLUTIONS
•  SIGNIFICANT BREAKTHROUGHS AND COUNTRIES IN COMPUTER VISION TECHNOLOGY
• COMPUTER VISION VS HUMAN VISION: FILLING THE VOID IS INDEED DIFFICULT

How does computer vision ease the workload in human jobs?

Computers analyze multiple sets of raw data from digital images and videos to provide appropriate decisions by understanding the environment through the new form of AI known as computer vision. The data is more accessible and affordable due to digital transformation and globalization. Computer vision is thriving in recent years because of the accuracy rates for image pattern recognition than humans. Deep learning neural networks enable the iterative learning process in computers to acquire, process, and analyze image patterns efficiently and effectively than the human visual cognitive system. Convolutional Neural Network (CNN) is used in computer vision technology for appropriate image pattern recognition. These neural networks scan the available image pixel by pixel to identify patterns and memorize the ideal output from different characteristics such as contours and colors. Humans are needed to develop smart machines for completing automated tasks with visual cognition.

The computer vision system can be used for object classification, object identification, and object tracking. It takes much lesser time to analyze thousands of images as well as to detect any defect or issue with hi-tech cameras, data, and AI algorithms than the naked human eyes. Let’s explore the potential of computer vision across several industries to boost productivity.

• Automotive: Computer vision through ADAS, RADAR as well as LIDAR technologies provide visual representations, high visibility, and 3D representations of the surroundings respectively. Automotive Gesture Recognition also monitors the facial and hand gestures of drivers with audible and visual alerts.
• Retail: Computer vision helps in security through CCTVs, spillage detection, theft control, video analytics, enhancing the shopping experience, optimizing operations, alerting in-shelf productivity, and better customer engagement
• Manufacturing: Computer vision helps factory workers in predictive maintenance, identifying defects and eliminating risks as well as product quality inspection for minimal waste of products
• Healthcare: Computer vision can detect any unusual image pattern in reports and X-rays accurately, early-stage tumors, arteriosclerosis, and many more for doctors and nurses to operate at the right time
• Agriculture: Computer vision detects pests and plant diseases, information about high-quality crops, provides facial recognition to identify the individual animal, and analyses grain quality for farmers efficiently

Thus, we all can confirm that computer vision is not taking away human jobs but there is a high probability that it will generate more human job opportunities in the upcoming years. The aim of computer vision is to collaborate with humans to enhance the workload with more appropriate outcomes without any failure or mistake. We have to remember that humans are the main creator behind all these achievements of computer vision.

The post WILL COMPUTER VISION TAKE HUMAN JOBS? appeared first on Artificial Intelligence.

Despite its expertise in human-like automation, AI still has some shortcomings.

The concept of artificial intelligence began with the notion of making machines act like humans. At present, artificial intelligence has improved to such an extent that AI-based machines and robots can paint, write poems and easily do many things that a human can do. Recently AI technology has been able to solve an important issue with the concept of protein folding, that scientists had been working on in vain for a long time. So, one can say that AI stands in the middle of humans and machines. However, despite its expertise in human-like automation, there are still some simple but vital aspects of human nature that are yet to be achieved by AI.

Common Sense

Even though AI robots and machines are capable of solving difficult problems of mathematics, physics, and engineering, they mostly cannot solve some simple problems. For example, ‘Rani went to the shop and chose a red dress. She paid 500 rupees.’ – This statement has no direct indication that Rani bought the red dress. A human would easily understand it because one has background knowledge that when someone is choosing an object and then paying the money, this means that the person has bought the object. However, the AI machine will not be able to automatically come to the conclusion about Rani buying the red dress. It does not have that background knowledge that the combined act of choosing and paying money refers to the act of buying. This background knowledge can be referred to as common sense. Common sense grows within a human through experience and the practice of retaining that experience, which AI is still not capable of doing.

Dave Gunning of DARPA has stated in an interview with Forbes, “The absence of common sense prevents an intelligent system from understanding its world, communicating naturally with people, behaving reasonably in unforeseen situations, and learning from new experiences.”

Some might say that this common sense can be implemented by uploading databases of everyday facts into the machine. In 1984, such an operation was started with the name Cyc. The basic problem with this operation is that common knowledge has its own array of exceptions and a variety of ideas, which would be impossible to put in a machine.

Adaptability

When a human child grows up, he or she learns new things and gradually adapts to the environment. The AI machine is made with inputting loads of data sets and once it is deployed, it does not have the capability of learning anymore, which prevents the machine from achieving adaptability. Machines with artificial intelligence are incapable of simultaneously learning from the environment and automatically adapting to those new changes in knowledge. Machines can be updated from time to time. The ability to continually learn over time by accommodating new knowledge while retaining previously learned experiences is referred to as continual or lifelong learning. Such a continuous learning task has represented a long-standing challenge for neural networks and, consequently, for the development of artificial intelligence.

If the AI-based- robot is sent to an unknown environment, its human creator may not have any idea of what the robot might face at that place. So, the human creator would not be able to input any database into the robot regarding this. As a result, the robot will not be able to adapt to the environment of that unknown place automatically. It cannot come up with instant ideas or instincts that would differ from a human who can always adapt to unknown environments by adding its newly acquired knowledge with the previously retained ones.

Logical Reasoning

AI is also unable to connect between cause and effect, which prevents it from understanding the basic dynamics of the world. An AI robot can always perform the instructed activity, but it cannot inwardly understand the cause of the activity or what effect it might have in the future. For example, as per instruction, in the morning time, the machine may serve breakfast, but it would not necessarily understand the connection of breakfast with the morning. Causal reasoning is an essential part of human intelligence, shaping how we make sense of and interact with our world. We know that dropping a vase will cause it to shatter, drinking coffee will make us feel energized, and exercising regularly will make us healthier.

Brenden Lake of New York University stated, “Our minds build causal models and use these models to answer arbitrary queries, while the best AI systems are far from emulating these capabilities.”

Scientists are consistently working towards making artificial intelligence capable of human-like attributes and activities. With the rate of improvement and advancement, robots like Sofia give hints that in the future these shortcomings may also be recovered by the machines. The concrete aspect of this situation is that in being the bridge between humans and machines, artificial intelligence is running extremely fast to catch up with human intelligence very soon.

The post SHORTCOMINGS OF AI: THE BRIDGE BETWEEN MACHINE AND HUMAN appeared first on Artificial Intelligence.

SAMEER S SOMALPABLO A RUZ SALMONES

BIG DATA has been on the tip of everyone’s tongue for the past several years now, and for good reason. As digital devices and touchpoints proliferate, so too does the amount of data we each create. This information can be used to help us better understand clients and customers, make more effective decisions, and improve our business operations – but only if we can make sense of it all.

By choosing the right Big Data sources and applications, we can put our organisations at a competitive advantage. But to do that, we need to understand Big Data’s definition, capabilities, and implications.

Big Data already has widespread applications. From Netflix recommendations to healthcare monitoring, it drives all types of predictive models that improve our daily lives. But the more we depend on it, the more we need to question how it shapes our lives and whether we should be relying on it so much.

While progress is inevitable and something to embrace, Big Data’s contribution should not be measured by how many companies apply it, but by how much better off it makes society as a whole.

Big data is more than just large datasets. It is defined by the three Vs of data management:

• Volume: Big data is often measured in terabytes.
• Variety: It can contain structurally different datasets, such as text, images, audio, and so on.
• Velocity: Big data must be processed quickly because of the increasing speed at which data is generated.

As the volume, variety, and velocity of data expand, they morph into Big Data and become too much for humans to handle without assistance. So we leverage artificial intelligence (AI) and machine learning to help parse it. While the terms Big Data and AI are often used interchangeably and the two go hand-in-hand, they are, in fact, distinct.

Simply put, Big Data powers AI with the fuel it needs to drive automation. But there are risks.

Realising Big Data’s business potential

Properly applied, Big Data helps companies make more informed – and therefore better – business decisions.

Melvin Greer, Intel’s chief data scientist says in an article: “A few examples include the hyper-personalisation of a retail experience, location sensors that help companies route shipments for greater efficiencies, more accurate and effective fraud detection, and even wearable technologies that provide detailed information about how workers are moving, lifting or their location to reduce injuries and increase safety.”

But this crucial competitive advantage is underused because so many companies struggle to sift through all the data and distinguish the signal from the noise.

Five principal challenges keep companies from realising big data’s full potential, according to Mr Greer:

• Resources: Not only are data scientists in short supply, the current pool also lacks diversity.
• Data aggregation: Data is constantly being created and it is a challenge to collect and sort it from all the disparate channels.
• Erroneous or missing data: Not all data is good or complete. Data scientists need to know how to separate the misleading from the accurate.
• Unfinished data: Cleaning data is time-consuming and can slow down processing. AI can help manage this.
• Truth seekers: We should not assume data analysis will yield a definitive answer. “Data science leads to the probability that something is correct,” Mr Greer writes. “It’s a subtle but important nuance.”

Addressing the first challenge is of paramount importance. The only way to solve the other issues is to first create the necessary human capital and provide them with the necessary tools.

The true promise of Big Data

Data is a wonderful instrument, but it is not a cure-all. Indeed, “too much of a good thing” is a real phenomenon.

Jacqueline Nolis, Saturn Cloud head of data science, writes in an article: “In my years working with many businesses, I have indeed seen some companies that fell into the situation of not using data enough. However, these occurrences paled in comparison to the number of times I have seen the reverse issue: companies with an over-reliance on data to the point that it was detrimental. The idea that data is needed to make a good decision is a destructive one.”

To illustrate her point, Ms Nolis describes Coca-Cola’s introduction of Cherry Sprite. What motivated the decision? Data. People were adding cherry-flavoured “shots” to Sprite at self-service soda dispensers. So score one for Big Data.

But as Ms Nolis points out, the very similar-tasting Cherry 7UP already existed – and had since the 1980s. So the data team might have come up with the new flavour more efficiently simply by perusing the soft drink aisle at the local grocery store. The lesson: Too heavy a reliance on data can be a barrier to common sense decision making.

Big Data applications: When and how

So how do we know when to put big data to work for our business? That decision needs to be made on a case-by-case basis according to the demands of each individual project.

The following guidelines can help determine whether it is the right course:

• Consider the desired outcome. If it’s to catch up with a competitor, investing in something the competitor has already done may not be a good use of resources. It might be better to let their example serve as guidance or inspiration and reserve Big Data analysis for more complicated projects.
• If disruption is the goal, Big Data can be applied to test new ideas and hypotheses and maybe reveal other possibilities. But we need to beware of the downsides: Data can kill creativity.
• If a business decision is urgent, the “data is still being analysed” is not an excuse to delay it.

Amid a public relations crisis, for example, we won’t have the time to mine the available data for insights or guidance.

We have to rely on our existing knowledge of the crisis and our customers and take immediate action.

Of course, sometimes Big Data is not just useful but essential.

Some scenarios call for Big Data applications:

• To determine if a strategy is working as planned, only the data will tell the story. But before we measure whether success has been achieved, we first have to establish our metrics and define the business rules that determine what success looks like.
• Mining Big Data may uncover sales or marketing performance anomalies that would not be otherwise diagnosable. Similarly, AI can help improve energy efficiency and offer insights into customer and employee behaviours.
• Big Data can help process and create models out of vast amounts of information. So as a general rule, the larger and more data-intense the project, the greater the likelihood Big Data could be helpful.

Big Data might be the trendy topic in technology today, but it is more than a buzzword.

Its potential to improve our businesses and our lives over the long term is real.

But that potential needs to be leveraged purposefully and in a targeted fashion. Big Data is not the business equivalent of a wonder drug.

We need to be mindful of where its applications can help and where they are superfluous or harmful.

Indeed, the full promise of Big Data can only be realised when it is guided by thoughtful human expertise.

• Sameer S Somal, CFA, is the CEO and cofounder of Blue Ocean Global Technology. Pablo A. Ruz Salmones is the co-founder and CEO of Grupo Ya Quedó, a software development and artificial intelligence (AI) company.

The post When to use Big Data and when not to: it should be guided by thoughtful human expertise appeared first on Artificial Intelligence.

Artificial intelligence (AI) has made it possible for the first time to create fully artificial human genome sequences that are indistinguishable from the DNA of real donors. A European team just created entire sequences of human DNA, using this AI. Their work was published in the journal PLOS Genetics.

An algorithm that can generate artificial human genomes

“Generative neural networks have been used effectively in many different fields over the past decade, including photorealistic imaging,” say the authors of this new work. Applying a similar concept with genetic data, the researchers built their neural networks using the sequences of 2,500 people stored in databases. The system had to generate sequences with similar characteristics and then mix their creations with real ones to see if they could tell the difference. Through training, the artificial genomes created turned out to faithfully reproduce features of the real genomes, such as allele frequencies (the different versions of a gene). One of the biggest challenges of this work was to verify their reliability, said Aurélien Decelle, co-author of this work and a researcher at the University of Paris-Saclay. “So we spent some time studying the statistical properties of the generated sequences,” he explains.

Only sequences, not whole genomes

These “realistic” and “high-quality” genomes are a first, the researchers note in the paper. This type of neural network has already been used in genetics to generate short sequences “on the order of tens or hundreds of base pairs” (the building blocks of our DNA, of which there are about 3 billion in humans), explains Flora Jay, who co-led this work at the University of Paris-Saclay. “But the generation of such long sequences (about 10,000 variants comprising several million base pairs) and in the context of population genetics is new and represents a major step forward,” she adds.

As a result, these artificial genomes “are indistinguishable from the other genomes in the biobank that we used for our algorithm, except for one detail: they do not belong to any real donor,” Luca Pagani, co-author of the study, explains in a press release.

However, the process still needs to be perfected. “One of the main drawbacks is that these models cannot yet be used to create whole artificial genomes due to computational limitations,” and they must be limited to bits and pieces, the authors explain. In addition, very rare alleles are difficult to represent with the algorithm. The final challenge is to “closely monitor the originality of the generated data, i.e., whether they are sufficiently different from the genomes of real donors,” Flora Jay says, adding that this is an ongoing research topic.

Human genome study without concerns for privacy

Far from being without a purpose other than the scientific achievement itself, this type of artificial intelligence can solve the ethical problems associated with genetic databases. “In population genetics, researchers need to regularly compare the data they produce to some reference genomes or sometimes even to a large reference panel. Ideally, these genomes should reflect genetic diversity,” says Flora Jay. Artificial genomes could perform this function reliably and safely.

“Existing genomic databases are an invaluable resource for biomedical research, but they are not publicly available or are protected by lengthy and exhaustive application procedures due to legitimate ethical concerns,” explains author Burak Yelmen. “Artificial genomes can help us overcome this problem within a safe ethical framework.” Looking ahead, Flora Jay predicts that these artificial genomes “will contribute to applications as diverse as understanding our evolutionary past or medical epidemiology by incorporating greater genetic diversity”.

The post French Researchers Develop the First Artificial Intelligence Capable of Creating Human Genomes Sequences appeared first on Artificial Intelligence.

AI can exploit the vulnerabilities of a person’s decision-making habits and patterns.

A new study by researchers from the Commonwealth Scientific and Industrial Research Organisation’s (CSIRO) Data61, in partnership with the Australian National University and researchers from Germany, has revealed that artificial intelligence (AI) can influence human decision-making.

Spearheaded by CSIRO scientist Amir Dezfouli, the study [PDF] involved running three experiments where participants played games against a computer.

The first two tests involved participants clicking on red or blue coloured boxes to win a fake currency. In the third experiment, participants were given two options as to how they could invest some fake currency. In the scenario, participants played the role of the investor while the AI played the role of the trustee.

As all three games went on, the AI learned the participants’ choice patterns that eventually saw it guide the players towards specific choices. For instance, by the third game, the AI was learning how to get participants to give it more money.  

Dezfouli said the study highlighted that AI could influence human decision-making by exploiting the vulnerabilities of an individual’s habits and patterns.

“Although the research was theoretical, it advances our understanding of how people make choices. This knowledge is valuable because it allows us to mitigate our vulnerabilities so we can better detect and avoid flawed choice as a result of potential misuse of AI,” he said.

He added that the way future AI operates will be dependent on its creators.

“Ensuring AI and machine learning are used as a force for good — to improve outcomes for society — will ultimately come down to how responsibly we set them up in the first place,” Dezfouli said.

The study has been published in the Proceedings of the National Academy of Sciences journal.

At the end of last year, Data61 researchers developed an implantable artificial intelligence monitoring and seizure detection helmet system designed to prevent seizure disorders for patients who have undergone decompressive brain surgery.

The detection system was developed and trained using traumatic brain injury data from Monash University to monitor brain activity for seizures while in standby mode before it is reactivated when a seizure is detected.

“Monitoring brain activity post-surgery is especially critical to a patient’s recovery as seizures can regularly occur, often leading to patients developing epilepsy,” CSIRO’s Data61 researcher Dr Umut Guvenc said at the time.

“These seizures are often difficult to detect, with current monitoring techniques only able to be used in a hospital using bulky devices for less than 24 hours, providing a brief snapshot of brain activity during that time only.

“This new method can continuously monitor brain activity wirelessly, allowing the patient to be mobile, comfortable, and more socially active.”

The post Artificial intelligence can influence human decision-making, new Data61 study reveals appeared first on Artificial Intelligence.

Could giving robots voice help them learn human commands?

Robots have become an integral part of human’s daily lives. They help us in numerous ways, from performing complex tasks to lifting heavy weights and assisting the elderly, playing with kids, and entertaining people at events. They can interact with people in any scenario. However, construing a human language still a challenge for robotic systems. Training them with real-world experiences and knowledge about the world could help robots understand natural language.

People use language to express emotions, direct behavior, ask and answer questions, provide information, and ask for help. Language-based interfaces for robots require minimal user training and expression of a variety of complex tasks.

In a paper, researchers from MIT describes a new way to train machines. They noted that children learn language by observing their environment, listening to the people around them, and understanding what they see and hear. With keeping that in mind, they created a tool called semantic parser that mimics the experience of children learning a language. Parsers are already being used for web searches, natural-language database querying, and voice assistants. The system observes captioned videos and links the words that speakers say with recorded objects and actions.

As parsers are trained in sentences annotated by humans, they could be used to improve natural interaction between humans and robots. According to the paper, a robot equipped with the parser could observe its environment to reinforce its understanding of spoken commands, even when the spoken sentences are not fully grammatical or clear.

Earlier, Analytics Insight reported that how giving voice to robots within healthcare influence human perception. Already, robots are delivering a wide range of healthcare services and opportunities to medical personnel and advancing patient care delivery. In this article, we noted how researchers at the University of Auckland and Singapore University of Technology & Design have been using speech synthesis techniques to create robots that sound more empathetic. As part of their study, researchers tested a hypothesis on how a robot’s voice can impact users’ understanding by conducting a simple experiment using a robot called Healthbot. They used a professional voice artist for the robot’s voice, which was recorded while reading dialogs in two voice variations: a flat monotone and an empathetic voice.

More broadly, teaching a machine to speak and making them able to recognize human voice is a crucial yet effective step as spoken language is the most intuitive form of interaction for humans. In 2018, it was reported that researchers in Japan attempted to bring audition, or power of listening, to robots. Proposed by Tokyo Institute of Technology Professor Kazuhiro Nakadai and Professor Hiroshi G. Okuno of Waseda University in 2000, “Robot Audition” is a research area. For this, they turned their research public and made it open-source software. This essentially helped them generate interest and diversified the research. Their research was officially registered in the IEEE Robotics and Automation Society.

So, when robots and robotics systems are able to learn and recognize the human language, they will have a more emphatic impact on people’s lives.

The post WHY DO ROBOTS NEED TO LEARN LANGUAGE? appeared first on Artificial Intelligence.

If ever there has been a need to adapt, 2020 has let it be known that this is the year. COVID-19 has brought about all kinds of workplace changes, from remote work with video meetings to temporary layoffs. It may seem like just another buzzword, but microservices can help human capital management (HCM) systems face the multitude of challenges that 2020 brings and help ensure workforce resiliency.

What microservices are

Darshan Kapadia, director of platform engineering at Lifion by ADP, explains that microservices are a way of designing and developing software so that each of the individual components can be worked on and deployed independently of the others. The key here is components are self-contained. This architecture allows for a rapid software release cycle because one component team doesn’t need to wait for another to finish before deploying.

Compare this model to more traditional monolithic architecture, which is still used widely: The product is developed as a whole by one large team and released as one large product. That means all the smaller teams working on components need to coordinate and deploy the product simultaneously.

Why microservices work so well

There are two reasons Kapadia touches on that explain why microservices have an edge.

1. Releasing a new product in small pieces that can run independently, instead of one large product, allows teams to focus on their own smaller piece of code. There’s a lower chance of failure when a new product is deployed, “and the blast radius is small,” he says. In addition, bug fixes and performance updates can be deployed as they are ready on an individual basis vs having to sustain a longer cycle between updates.
2. With constant innovations in technology, microservices enable organizations to add new components without waiting for a complete overhaul of an outdated system. Kapadia explains how at Lifion, they went from self-managed databases to entirely genuine in just under nine months. “That is unimaginable if you are in a traditional monolithic system with multiple databases,” he says. “It isn’t possible because you have to coordinate everything before that can happen,” something that can take a year’s depending on system complexity.

Moreover, the research and improvements happening in the UX space are significant right now, and in order to get those tools into the hands of customers, UI needs to be a separate component that can be deployed independently.

Microservices also employ simple routing methods, receiving requests, processing those requests, and then responding accordingly. This is in contrast to architecture involving enterprise service buses which use more complex integration approaches.

The HCM pivot during COVID-19

Most, if not all, organizations have needed to pivot as the pandemic changed the way we work. In the case of HR products, there have been changes in taxation and new payroll terms added as well as return-to-work protocols.

Microservices can expedite these types of changes, making them easier to deploy than a traditional product, since the tech team is able to work continuously behind the scenes, releasing updates as they are ready.

Not only that, with everyone working from home for months at a time communication can become a problem no matter how many video calls are scheduled. With microservice architecture, however, “communication doesn’t become a barrier,” explains Kapadia. “That’s because the microservices are talking to each other with a very strict set of contracts that can’t be broken.” This allows team members to perform updates behind the scenes with minimal communication, he says.

Aside from the presence of the global health event, what makes microservices so valuable to HCM is the ability to mitigate costs by scaling services as needed and independently of others. Spikes in traffic for specific systems, such as payroll, time, or benefits, come at predictable times, Kapadia says, so scaling each one individually rather than altogether for every single one of those times can save money.

What you should have first

Kapadia explains that there are some circumstances in which a company won’t benefit from microservices. An organization should be completely on board with the microservice paradigm, since services will be divided into small chunks for different teams. All teams should be in agreement, he says, because while each piece is developed independently, they need to all work together. It’s also critical to ensure that the boundaries of microservices are well defined. If a large number of microservices need to communicate for performing a single task, it can cause performance issues due to network latency between services.

In addition, it’s important to have a robust Continuous Integration and Continuous Deployment (CI/CD) pipeline in place because developing, testing, and deploying products will take place independently. The right tools and platforms should also already be in place, with active support for flexible runtime deployment and automated monitoring, as well as container hot deployment.

Organizations need to be agile and willing to adapt to a larger variety of scenarios than ever. The ability to pivot and adapt quickly is exactly what microservice architecture is designed for.

The post How Microservice Architecture Is Transforming Human Capital Management appeared first on Artificial Intelligence.

A recent report by Vodafone Business found that COVID-19 has ignited a surge in Internet of Things (IoT) adoption, with 79 percent of U.S. businesses saying they’ve launched new IoT projects as a result of the ongoing health crisis. However, as IoT continues to digitally transform enterprises and their operations, business leaders must understand how their workers interact with IoT and equip their workforces with the training needed to deploy this disruptive technology effectively.

Unfortunately, despite the unique skill sets and processes that IoT demands, worker training is a consistently overlooked element of IoT. This is especially true for industrial IoT (IIoT) sensors and other devices used across the utilities and manufacturing sectors. Numerous industrial organizations are realizing the value of IIoT, but many of these businesses tend to over-rely on automation and fail to support their workers on the front lines whose jobs are immensely impacted by IIoT adoption, and the vast troves of data it creates.

As IIoT emerges as a go-to vehicle for driving operational visibility, efficiency and bottom-line savings, IIoT frontrunners are increasingly extending IoT data to the edges of the enterprise, where workers in the field interact with the physical equipment and the world around them. This means more and more industrial workers are using mobile apps, wearable technology and augmented reality solutions to carry out day-to-day tasks.

For oil and gas companies, for example, IIoT increases the real-time data monitoring of rigs and optimizes productivity, but this data still needs to be communicated to workers on rig sites to allow them to update processes accordingly. Similarly, across manufacturing, sensors on production lines can create efficiencies, yet there are often still field workers doing the heavy lifting, and the data created on these lines will prove critical to how they do their jobs. Therefore, if these essential workers are not properly trained on new IoT-related tasks, the skills gap across the industrial sector will widen dramatically and these organizations will likely face costly repercussions.

While most organizations have training programs in place that cover basic processes of each role, traditional training methods simply do not apply when providing digital training. More often than not, the training and ongoing support required to build a well-equipped, engaged industrial workforce is not in place. 

It’s important to train workers in the environment in which they will be working. For IoT tools used on the job, these digital trainings should be hands-on, self-guided simulations that occur within the IoT applications themselves. However, to carry out a strategic, full-scale IIoT transformation, organizations cannot view training as a “one and done” event.

Rather, it’s important to implement a digital training solution that delivers ongoing employee education and real-time support that proactively reinforces learning. In addition, as IIoT rapidly evolves, these ongoing trainings will allow industrial organizations and their workforces to adapt accordingly and accommodate new trends, cultural expectations, best practices and security and compliance procedures.

IIoT is now a vital component of digital transformation in industry, especially as these organizations continue to grapple with the unique circumstances surrounding the COVID-19 pandemic. IoT technology holds great promise, but at the end of the day it’s the humans who are ultimately interacting with IoT systems and leveraging the actionable insights that they provide who business leaders cannot afford to lose sight of in their technology deployments.

Before overhauling traditional infrastructure in favor of IIoT technologies, organizations must ask themselves how they will upskill their workforce and empower them to retain these newly learned skills. Those that best prepare their employees with the initial and ongoing education needed to take on this new industrial revolution will be best prepared to surpass competitors and break into new markets. 

The post Investing in the Human Element of IIoT appeared first on Artificial Intelligence.

(Nanowerk News) Androids, or robots with humanlike features, are often more appealing to people than those that resemble machines — but only up to a certain point. Many people experience an uneasy feeling in response to robots that are nearly lifelike, and yet somehow not quite “right.” The feeling of affinity can plunge into one of repulsion as a robot’s human likeness increases, a zone known as “the uncanny valley.”

The journal Perception (“The Uncanny Valley Phenomenon and the Temporal Dynamics of Face Animacy Perception “) published new insights by Emory psychologists into the cognitive mechanisms underlying this phenomenon.

Since the uncanny valley was first described, a common hypothesis developed to explain it. Known as the mind-perception theory, it proposes that when people see a robot with human-like features, they automatically add a mind to it. A growing sense that a machine appears to have a mind leads to the creepy feeling, according to this theory.

“We found that the opposite is true,” says Wang Shensheng, first author of the new study, who did the work as a graduate student at Emory and recently received his PhD in psychology. “It’s not the first step of attributing a mind to an android but the next step of ‘dehumanizing’ it by subtracting the idea of it having a mind that leads to the uncanny valley. Instead of just a one-shot process, it’s a dynamic one.”

The findings have implications for both the design of robots and for understanding how we perceive one another as humans.

“Robots are increasingly entering the social domain for everything from education to healthcare,” Wang says. “How we perceive them and relate to them is important both from the standpoint of engineers and psychologists.”

“At the core of this research is the question of what we perceive when we look at a face,” adds Philippe Rochat, Emory professor of psychology and senior author of the study. “It’s probably one of the most important questions in psychology. The ability to perceive the minds of others is the foundation of human relationships. ”

The research may help in unraveling the mechanisms involved in mind-blindness — the inability to distinguish between humans and machines — such as in cases of extreme autism or some psychotic disorders, Rochat says.

Co-authors of the study include Yuk Fai Cheong and Daniel Dilks, both associate professors of psychology at Emory.

Anthropomorphizing, or projecting human qualities onto objects, is common. “We often see faces in a cloud for instance,” Wang says. “We also sometimes anthropomorphize machines that we’re trying to understand, like our cars or a computer.”

Naming one’s car or imagining that a cloud is an animated being, however, is not normally associated with an uncanny feeling, Wang notes. That led him to hypothesize that something other than just anthropomorphizing may occur when viewing an android.

To tease apart the potential roles of mind-perception and dehumanization in the uncanny valley phenomenon the researchers conducted experiments focused on the temporal dynamics of the process. Participants were shown three types of images — human faces, mechanical-looking robot faces and android faces that closely resembled humans — and asked to rate each for perceived animacy or “aliveness.” The exposure times of the images were systematically manipulated, within milliseconds, as the participants rated their animacy.
The results showed that perceived animacy decreased significantly as a function of exposure time for android faces but not for mechanical-looking robot or human faces. And in android faces, the perceived animacy drops at between 100 and 500 milliseconds of viewing time. That timing is consistent with previous research showing that people begin to distinguish between human and artificial faces around 400 milliseconds after stimulus onset.

A second set of experiments manipulated both the exposure time and the amount of detail in the images, ranging from a minimal sketch of the features to a fully blurred image. The results showed that removing details from the images of the android faces decreased the perceived animacy along with the perceived uncanniness.

“The whole process is complicated but it happens within the blink of an eye,” Wang says. “Our results suggest that at first sight we anthropomorphize an android, but within milliseconds we detect deviations and dehumanize it. And that drop in perceived animacy likely contributes to the uncanny feeling.”

The post Why human-like robots elicit uncanny feelings appeared first on Artificial Intelligence.