DeepMind, the Alphabet-backed machine learning lab that’s tackled chess, Go, Starcraft 2, Montezuma’s Revenge, and beyond, believes the board game Diplomacy could motivate a promising new direction in reinforcement learning research. In a paper published on the preprint server Arxiv.org, the firm’s researchers describe an AI system that achieves high scores in Diplomacy while yielding “consistent improvements.”

AI systems have achieved strong competitive play in complex, large-scale games like Hex, shogi, and poker, but the bulk of these are two-player zero-sum games where a player can win only by causing another player to lose. That doesn’t reflect the real world, necessarily; tasks like route planning around congestion, contract negotiations, and interacting with customers all involve compromise and consideration of how preferences of group members coincide and conflict. Even when AI software agents are self-interested, they might gain by coordinating and cooperating, so interacting among diverse groups requires complex reasoning about others’ goals and motivations.

The game Diplomacy forces these interactions by tasking seven players with controlling multiple units on a province-level map of Europe. Each turn, all players move all their units simultaneously within one of 34 provinces, and one unit may support another unit owned by the same or another player to allow it to overcome resistance by other units. (Alternatively, units — which have equal strength — can hold a province or move to an adjacent space.) Provinces are supply centers, and units capture supply centers by occupying the province. Owning more supply centers allows a player to build more units, and the game is won by owning a majority of the supply centers.

Due to the interdependencies between units, players must negotiate the moves of their own units. They stand to gain by coordinating their moves with those of other players, and they must anticipate how other players will act and reflect these expectations in their actions.

“We propose using games like Diplomacy to study the emergence and detection of manipulative behaviors … to make sure that we know how to mitigate such behaviors in real-world applications,” the coauthors wrote. “Research on Diplomacy could pave the way towards creating artificial agents that can successfully cooperate with others, including handling difficult questions that arise around establishing and maintaining trust and alliances.”

DeepMind focused on the “no press” variant of Diplomacy, where no explicit communication is allowed. It trained reinforcement learning agents — agents that take actions to maximize some reward — using an approach called Sampled Best Responses (SBR), which handled the large number of actions (10⁶⁴) players can take in Diplomacy, with a policy iteration technique that approximates the best responses to players’ actions as well as fictitious play.

At each iteration, DeepMind’s system creates a data set of games, with actions chosen by a module called an improvement operator that uses a previous strategy (policy) and value function to find a policy that defeats the previous policy. It then trains the policy and value functions to predict the actions the improvement operator will choose as well as the game results.

The aforementioned SBR identifies policies that maximize the expected return for the system’s agents against opponents’ policies. SBR is coupled with Best Response Policy Iteration (BRPI), a family of algorithms tailored to using SBRs in many-player games, the most sophisticated of which trains the policies to predict only the latest BR and explicitly averages historical checkpoints to provide the current empirical strategy.

To evaluate the system’s performance, DeepMind measured the head-to-head win rates against six agents from different algorithms and against a population of six players independently drawn from a reference corpus. They also considered “meta-games” between checkpoints of one training run to test for consistent improvement and examined the exploitability (the margin by which an adversary would defeat a population of agents) of the game-playing agents.

The system’s win rates weren’t especially high — averaged over five seeds of each game, they ranged between 12.7% and 32.5% — but DeepMind notes that they represent a large improvement over agents trained with supervised learning. Against one algorithm in particular — DipNet — in a 6-to-1 game, where six of the agents were controlled by DeepMind’s system, the win rates of DeepMind’s agents improved steadily through training.

In future work, the researchers plan to investigate ways to reduce the agents’ exploitability and build agents that reason about the incentives of others, potentially through communication. “Using [reinforcement learning] to improve game-play in … Diplomacy is a prerequisite for investigating the complex mixed motives and many-player aspects of this game … Beyond the direct impact on Diplomacy, possible applications of our method include business, economic, and logistics domains … In providing the capability of training a tactical baseline agent for Diplomacy or similar games, this work also paves the way for research into agents that are capable of forming alliances and use more advanced communication abilities, either with other machines or with humans.”

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Artificial intelligence (AI) is ushering in a paradigm shift in the conservative insurance industry, as more market players introduce new sets of time-saving AI-powered platforms to enhance business efficiency.

It has been years since AI started appearing in almost all industries due to its usefulness in a wide range of applications and human-like versatility.

Most globally leading companies have gone all-out to incorporate the big data-driven technology into their platforms, in a desperate bid to take the lead in the promising growth area.

Despite the global AI sensation, local insurers have so far remained less agile in jumping on the bandwagon. But starting this year, they began to utilize the technology in their own ways by developing automated, data-driven AI software.

On the development front, the Korea Insurance Development Institute (KIDI) recently developed an AI-driven car repair estimation software program. This will streamline insurance payment processes, as the platform _ called AOS Alpha _ can calculate the cost of repairs by analyzing images of a damaged car.

The institute developed the AI platform by analyzing 1 million images via deep learning. It said AOS Alpha can estimate repair costs for 195 cars _ ranging from sedans to SUV. The figure will increase down the road, as the platform increases its datasets. The accuracy of the platform will also improve against the same backdrop.

The nation’s 12 non-life insurers, which operate car insurance, plan to bring in the service for their businesses. KIDI said they are training staffs before introducing the platform.

“The introduction of the platform in the local insurance industry will help improve insurance culture by reducing distrust and conflict between interested parties, as AOS Alpha is expected to set a standard in offering car repair costs,” said Park Jin-ho, research head at KIDI’s auto technology laboratory.

Staring from mid-January, Hanwha Life Insurance, one of the so-called big three life insurers here, also introduced an AI-driven insurance claim review system. This was the first time in the industry that a cloud-based platform was introduced for real-time insurance claim analysis here.

According to the company, the system can automatically make decisions on whether to provide insurance payments to its users. The company has racked up 11 million insurance claim datasets over the past three years, ever increasing the accuracy of the system.

For now, the system processes 25 percent of insurance claims, but the company plans to increase the figure to 50 percent in the near future. The Hanwha affiliate expects the platform to reduce fixed costs of more than 10 billion won ($8.16 million) for the next five years.

Kyobo Life Insurance and Samsung Fire & Marine Insurance are also joining the AI drive by utilizing the technology in their insurance contract review process.

In October 2019, Kyobo Life developed BARO insurance contract review system. According to the company, BARO is capable of making human-like decisions in whether to approve an insurance contract. The AI system analyzes whether an applicant meets the standards of its insurance product.

The Samsung affiliate is also operating the AI-powered insurance contract review system. The software can automatically approve or reject users’ applications for the firm’s health and cancer insurance products by conducting computerized screening procedures without human labor, the company said.

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Machine vision, natural language processing, data analytics and other deep learning applications will propel global AI software revenues over the next five years via a growing list of industry segments spanning automotive and health care to financial services and retail.

Market tracker Omdia forecasts AI software revenues will surge through 2025 to $126 billion, a 12-fold increase over a $10.1 billion industry in 2018. “The narrative is shifting from asking whether AI is viable to declaring that AI is now a requirement for most enterprises that are trying to compete on a global level,” said Keith Kirkpatrick, principal analyst with Omdia.

“AI is likely to trigger major transformations in industries where there is a clear case for incorporating AI, rather than in pie-in-the-sky use cases that may not generate a return on investment for many years,” Kirkpatrick added.

Omdia estimates that more than half of AI revenues will be generated by machine vision and language applications, with deep learning deployments driving the AI market. Deep learning models are proving more capable for perception applications since they can operate without expensive training and are able to tap very large data sets to evolve. Omedia sees those attributes as attractive for applications like cybersecurity, health care and investment trading.

Hence, the market tracker predicts deep learning will account for an estimated $74.5 billion in AI software sales by 2025, or 59 percent of total AI revenues.

The consumer sector has seeded the AI software market via early applications such as digital assistants, smart speakers and automotive applications. Voice and speech recognition apps have so far generated the most AI software revenue.

Those consumer applications tapped into large data sets that resulted in improved AI algorithms and processing engines. Omdia expects other sectors to apply these early uses cases to more ambitious, data-driven applications centered around Internet of Things deployments. Meanwhile, the shift to edge computing and the efforts of infrastructure vendors to move computing and storage resources closer to where data resides are expected to spur development of specialized deep learning algorithms and improved processing capabilities at the network edge.

The market tracker also foresees hybrid AI deployments in which deep learning models are combined with machine vision, natural language processing and “machine reasoning.”

That combination is seen overtaking the role of machine learning for data analytics applications. Hence, deep learning applications will help drive “AI in the long run due to the wide range of use cases that will be enabled now and well into the future,” Omdia said.

That bullish AI software forecast squares with other prognostications. For example, Fortune Business Insights predicted earlier this year that the global market for all AI technologies would grow at a 33-percent clip through 2026, reaching more than $202 billion.

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Artificial intelligence chip startup Cerebras Systems claims it has the “world’s fastest AI supercomputer,” thanks to its large Wafer Scale Engine processor that comes with 400,000 compute cores.

The Los Altos, Calif.-based startup introduced its CS-1 system at the Supercomputing conference in Denver last week after raising more than $200 million in funding from investors, most recently with an $88 million Series D round that was raised in November 2018, according to Andrew Feldman, the founder and CEO of Cerebras who was previously an executive at AMD.

The CS-1 system is based on the startup’s Wafer Scale Engine chip, which it says is “the only trillion transistor wafer scale processor in existence,” measuring 56.7 times larger and containing 78 more compute cores than the largest GPU.

This brings total funding to more than $200 million for Cerebras, which showed off its CS-1 system at the Supercomputing conference in Denver last year. The system is based on its Wafer Scale Engine chip, which it says is “the only trillion transistor wafer scale processor in existence,” measuring 56.7 times larger and containing 78 more compute cores than the largest GPU.

With Wafer Scale Engine’s 400,000 compute cores and 18 GB of on-chip memory, the startup said the CS-1 can deliver compute performance with less space and power than any other system, representing one-third of a standard data center rack while replacing the need for hundreds of thousands of GPUs.

“The CS-1 is the industry’s fastest AI computer, and because it is easy to install, quick to bring up and integrates with existing AI models in TensorFlow and PyTorch, it delivers value the day it is deployed,” Feldman said in a recent statement. “Depending on workload, the CS-1 delivers hundreds or thousands of times the performance of legacy alternatives at one-tenth the power draw and one-tenth the space per unit compute.”

Cerebras is targeting deep learning workloads, both for training and inference. The startup said the large size of its Wafer Scale Engine allows it to “process information more quickly” than other AI accelerators like GPUs, reducing training work from months to minutes. Inference, in the meantime, “is thousands of times faster,” with the Wafer Scale Engine capable of making a single image classification in microseconds, which is equal to one one-thousandth of a millisecond.

Among Cerebras’ dozens of customers are the U.S. Department of Energy’s Argonne National Laboratory and Lawrence Livermore National Laboratory. Argonne, in particular, is using the CS-1 to accelerate neural networks for cancer studies, study the properties of black holes and treat traumatic brain injury.

The startup doesn’t have plans to sell its chips or systems through channel partners for now, according to Cerebras’ spokesperson.

Marc Fertik, vice president of technology solutions at Elk Grove Village, Ill.-based Ace Computers, No. 261 on CRN’s 2019 Solution Provider 500 list, said with the CS-1 likely costing a “fortune,” it wouldn’t make sense for Cerebras to work with resellers until it starts selling less expensive systems at higher volumes.

“As soon as you move down in price point to increase your volume, you need to build your channel, because that’s when your business development and sales force can’t do it alone,” he said.

Even then, he said, prebuilt systems aren’t for everyone in the channel, citing Nvidia’s GPU-accelerated DGX deep learning system as an example. While some partners have built practices around the platform, others, like Ace Computers, would rather focus on building their own GPU-accelerated systems using parts from server vendors such as Supermicro, according to Fertik.

“We have military guys that don’t care about the extra software support, but they care about the hardware cost,” he said. “We have successfully convinced them and sold them multiple times something that is 30 percent cheaper.”

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SAN FRANCISCO — Google’s latest phone and smart-home devices came packaged with a not-so-subtle message: Google cares about your privacy. Does it?

The tech company has had a complicated relationship with user information in the past. Google’s latest steps offer consumers some additional protections, although it’s unclear how much more secure users will feel.

Google unveiled a new Pixel smartphone and other hardware devices on Tuesday, all aimed at getting people more hooked on services powered by the company’s Google Assistant and other artificial-intelligence technology.

But privacy has emerged as a bigger issue with these products thanks to the growing popularity of always-listening “smart speakers” and similar devices. Google, Microsoft, Amazon and Apple have all recently acknowledged employing human contractors to listen to and transcribe some voice recordings captured by AI software.

Most such AI work, from interpreting voice requests to answering questions to turning on your lights, takes place in the cloud, not on your device. Users have very little control of what happens to their data in the cloud.

On Tuesday, though, Google emphasized that much of what you do on its new phones will stay there. Its new facial recognition unlock feature won’t transmit details to Google servers for processing, for instance, and its Assistant can also handle many queries directly on the phone. A new recording transcription feature and radar technology that recognizes gestures are also done on the device.

“You need to know what your data is safe,” Rick Osterloh, Google senior vice president of hardware, said at the company’s New York launch event Tuesday. “When computing is always available, designing for computing and privacy becomes more important than ever.”

Apple and Amazon have also emphasized their privacy commitments at recent product launches.

The goal is to give people more choice over privacy settings, Osterloh said. Nest speakers and cameras now come with physical switches to turn off cameras and mics, for example.

Still, Google relies heavily on customer information to build user-specific profiles it uses to target digital advertising, which produces the vast majority of its income.

The Assistant, akin in basic function to Apple’s Siri and Amazon’s Alexa , is emerging as Google’s latest digital data collector. It can learn more about you from your queries and can direct you to other Google services such as maps and search, which also feed into Google’s multi-billion dollar advertising business.

“Their end game is trying to collect all this data and target you with advertising,” said Victoria Petrock, principle analyst at eMarketer. “The voice is a whole new way to capture people’s behaviors.”

The more helpful the Assistant becomes, the more likely people are to use it.

On the hardware front, Google’s new Pixel 4 features a fancier camera that will recognize people who’ve appeared previously in your photos in order to automatically focus on them in new shots.

The new phone also comes with motion-sensing technology that allows people to skip songs or switch apps by gesturing near the phone.

The Pixel 4 will carry a starting price tag of $799 — $100 more than the entry-level iPhone 11 — and will go on sale Oct. 24. The larger XL version will cost $899, or about $200 less than the similar-sized iPhone 11 Pro Max.

Google’s phones have been well reviewed, but have yet to make much of a splash in the market dominated by Apple, Huawei and Samsung. In fact, Google’s hardware products have never been big moneymakers. Rather, they offer a way for Google to showcase its money-making services.

The company also unveiled true wireless earbuds, called Pixel Buds, Google’s answer to Apple’s AirPods. The new model, which will go on sale early next year for $179, does away with the wire that connects the two buds.

Google introduced Nest Mini, the smaller version of its smart speaker. It comes out next Tuesday for $49. Google’s refreshed Wi-Fi router, Nest Wi-Fi, will be available in the coming weeks for $269. A new Pixelbook Go laptop goes on sale in January staring at $649.

Google hardware team, including many former Google Glass engineers, work from a light-filled, architecturally impressive building near the company’s main campus in Mountain View, California. The building is complete with a “color lab” for finding the perfect device hues, a materials library for all sorts of elemental inspiration and a small model shop to build device prototypes on site.

“We started by defining what it feels like to hold Google in your hands,” hardware design executive Ivy Ross said. “The good thing about coming a little bit late to the hardware arena is you get to stand back and look at everyone else.”

One of the challenges this time around was finding a way to make the products more sustainable, a feat especially notable on the Nest Mini, which has a “fabric” casing made of yarn created from plastic water bottles.

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Voices for the need of an ethical Artificial Intelligence are getting louder. AI has a great many applications, and has been solving various problems for humans. But such a powerful technology raises equal concerns about its possible misuse. And there have already been multiple cases of AI being used for malicious purposes.

Take Deepfakes for example. It is an advanced AI software that creates an artificial version of a person’s face accurately matching that of another. The software has been abused to create fake pornographic content using facial data of Hollywood celebrities such as Gal Gadot, Arianna Grande and Taylor Swift, among others.

Just recently, MIT experimented with an AI that was fed with data from the “darkest corners of Reddit.” The result was the world’s first psychopath AI, Norman.

Another recent example is Google’s Duplex AI, a natural-sounding robo assistant that makes calls on your behalf. Even though Google is still developing this AI, people have raised concerns over privacy and possible misuse by marketers or political campaigns.

Perhaps this is why the likes of Elon Musk have long warned about an AI apocalypse. In the case of Musk, he has even gone on to say that AI can one day lead to World War 3.

So, where do we go from here? There are two options we can consider: Abandon AI completely and forget about any benefits it can deliver, or explore the solutions that can prevent a possible apocalypse. Although governments across the world are yet to formulate regulations for AI, technology firms are already working towards implementing an “ethical AI”.

Google is already setting the standard by pledging that it will not allow its AI technology to be used for weapons or combat. Google will not pursue AI applications in “technologies that cause or are likely to cause overall harm. Where there is a material risk of harm, we will proceed only where we believe that the benefits substantially outweigh the risks, and will incorporate appropriate safety constraints”, wrote CEO Sundar Pichai in a blog post.

“AI algorithms and datasets can reflect, reinforce, or reduce unfair biases. We recognise that distinguishing fair from unfair biases is not always simple, and differs across cultures and societies. We will seek to avoid unjust impacts on people, particularly those related to sensitive characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious belief,” he added.

But Google is not alone in this crusade. Technology firms like Microsoft and Accenture are already making giant strides in the direction of ethical AI.

Brad Smith, the President of Microsoft who co-authored ‘The Future Computed: Artificial Intelligence and its role in society’ with Harry Shum, laid out six principles for the AI to consider – fairness; reliability and safety; privacy and security; inclusiveness; transparency; and accountability. According to Smith, just a consensus on ethical AI is not enough.

“Because if we have a consensus on ethics and that is the only thing we do, then what we are going to find is that only ethical people will design AI systems ethically. That will not be good enough … We need to take these principles and put them into law,” he said at an event during a visit to Singapore in April this year. “Only by creating a future where AI law is as important a decade from now as, say, privacy law is today, will we ensure that we live in a world where people can have confidence that computers are making decisions in ethical ways.”

Accenture is taking a slightly different approach by monitoring the data input at the very initial levels.

“Accenture’s ‘Teach and Test’ methodology ensures that AI systems produce the right decisions in two phases. The ‘Teach’ phase focuses on the choice of data, models and algorithms that are used to train machine learning. This phase experiments and statistically evaluates different models to select the best performing model to be deployed into production, while avoiding gender, ethnic and other biases, as well as ethical and compliance risks,” the company said in a note.

“The adoption of AI is accelerating as businesses see its transformational value to power new innovations and growth,” said Bhaskar Ghosh, group chief executive, Accenture Technology Services. “As organisations embrace AI, it is critical to find better ways to train and sustain these systems – securely and with quality – to avoid adverse effects on business performance, brand reputation, compliance and humans.”

The general outlook is that the onus is on humans to build an AI that meets ethical parameters.

“As AI algorithms are created and trained by humans, there is a very high possibility of human bias being built into these algorithms. AI systems are superior to humans in speed and capability which, when used in malicious ways, can cause damage that is much higher in magnitude. Just as in humans, mistakes are unavoidable for AI algorithms too. The issue is the lack of accountability that would otherwise be a deterrent to negative actions,” said Arjun Pratap, CEO & Founder, EdGE Networks, a HR Technology Solutions provider that uses Data Science and Artificial Intelligence.

Back in India, people have started taking AI seriously. Niti Aayog recently released a discussion paper on the technology. Titled “National Strategy for Artificial Intelligence”, the paper touched on the issue of ethics along with exploring the massive benefits of the AI.

“As AI-based solutions permeate the way we live and do business, questions on ethics, privacy and security will also emerge. Most discussions on ethical considerations of AI are a derivation of the FAT framework (Fairness, Accountability and Transparency). A consortium of Ethics Councils at each Centre of Research Excellence can be set up and it would be expected that all COREs adhere to standard practice while developing AI technology and products,” said the paper.

“Data is one of the primary drivers of AI solutions, and thus appropriate handling of data, ensuring privacy and security is of prime importance. Challenges include data usage without consent, risk of identification of individuals through data, data selection bias and the resulting discrimination of AI models, and asymmetry in data aggregation. The paper suggests establishing data protection frameworks and sectorial regulatory frameworks, and promotion of adoption of international standards,” it added.

Experts suggest that AI can have a positive impact, provided the government is able to implement the right regulations.

“In India, businesses have only started harnessing AI in the recent past. It is definitely an area India should focus on as AI can create a huge impact in a positive and ethical way – provided the Indian government implements the necessary regulations and standards to ensure its responsible usage. A team that audits and certifies AI systems on factors such as trust, safety, accountability would certainly help in keeping malicious use of AI under check. It would foster safe, explainable and unbiased use of AI,” said Arjun on the possible steps needed to have ethical AI in India.

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Ever since its introduction as an academic discipline back in the 50s, AI has led to a combination of disappointment and optimism.

In fact, technological options that were conveyed as impossible, such as optical character recognition, are, presently, run-of-the-mill. While other technologies are yet to be mastered, such as autonomous cars, AI is an excellent tool that can be used in the business realm. In fact, it has already started.

Whether we’re discussing direct or indirect AI adaptations, this promising technology is bound to change everything we do. That being said, let’s have a look at what is expected to happen.

Enabling Wholly Autonomous Processes

Even though most AI technologies would still require human supervision and intervention, this may change. Companies aim at developing technology that duplicates human cognitive capabilities. That would enable AI systems to be fully trusted in the case of autonomous processes.

In fact, AI solutions are expected to cope with intricate problems in transportation, energy and medicine. We could anticipate seeing AI technologies that can parallel the speed and cognitive ability of humans.

That’s why many analysts, including Bill Gates, are distressed regarding the fact that artificial intelligence systems will take over the majority of jobs. According to a studyby Oxford University from 2013, AI is expected to take over no less than half the jobs in the United States.

In fact, no less than 702 occupations will inevitably be affected by automation. While other jobs will spring, the working realm will dramatically change. People ought to find vocations that require cognitive complex tasks that couldn’t be completed by robots. Evidently, such positions would necessitate more schooling, which would result in a race between education and technology.

Revolutionize Time Management

Without a doubt, timing is a key element that could decide a company’s success or failure and can make a difference between a firm’s success and failure in 42 percent of the cases. It comes as no surprise that companies are eager to incorporate artificial intelligence based scheduling software.

Such software can assist companies in making precise predictions according to available emerging and historical information. Small businesses, as well, shouldn’t ignore the sector of artificial intelligence, and how it could help them develop. It would be best for each entrepreneur to brainstorm the potential benefits AI could help maximize the company’s productivity level.

Increased Efficiency and Precision at Low Costs

With the assistance of AI software, the margin of mistake and error that determine companies to lose money would be dramatically reduced. Therefore, eliminating human error would result in a higher efficiency and precision, without the risk of financial losses. The fact is that machines work much faster than humans, and they don’t require regular wages.

Automated Lead Generation

The expenses linked to costumer acquisition through lead generation are important for every company. Also, this implies extensive trial, error and exploration of various channels before pinpointing the right solution.

AI has the potential of changing that altogether and may increase the relevance of these campaigns, thus reducing costs. Technology can accomplish that by automating and learning from mere users.

In simplest words, what would require a sales associate weeks of work and research, a robot could accomplish in a couple of hours or days.

Prediction of Human Behavior

AI algorithms have the capability of pinpointing patterns in the elements that affect humans in the decision making process. Such algorithms can offer critical guidelines, by offering information on potential outcomes. Even today, one can use apps that analyze store deals and user behaviors.

Detection of Cybersecurity Issues

You might already know that the incidence of cyber attacks over the last years has been increasing and has everyone from small business owners to the government concerned.

In this respect, artificial intelligence holds outstanding promises in learning patterns of devices, networks, and systems and in decoding them. This could offer valuable information regarding in-progress attacks. As a matter of fact, more and more startups are eager to incorporate such software to obtain unprecedented levels of cybersecurity.

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Some people are afraid that heavily armed artificially intelligent robotsmight take over the world, enslaving humanity – or perhaps exterminating us. These people, including tech-industry billionaire Elon Musk and eminent physicist Stephen Hawking, say artificial intelligence technology needs to be regulated to manage the risks. But Microsoft founder Bill Gates and Facebook’s Mark Zuckerberg disagree, saying the technology is not nearly advanced enough for those worries to be realistic.

As someone who researches how AI works in robotic decision-making, drones and self-driving vehicles, I’ve seen how beneficial it can be. I’ve developed AI software that lets robots working in teams make individual decisions, as part of collective efforts to explore and solve problems. Researchers are already subject to existing rules, regulations and laws designed to protect public safety. Imposing further limitations risks reducing the potential for innovation with AI systems.

How is AI regulated now?

While the term “artificial intelligence” may conjure fantastical images of human-like robots, most people have encountered AI before. It helps us find similar products while shopping, offers movie and TV recommendations and helps us search for websites. It grades student writing, provides personalized tutoring and even recognizes objectscarried through airport scanners.

In each case, the AI makes things easier for humans. For example, the AI software I developed could be used to plan and execute a search of a field for a plant or animal as part of a science experiment. But even as the AI frees people from doing this work, it is still basing its actions on human decisions and goals about where to search and what to look for.

In areas like these and many others, AI has the potential to do far more good than harm – if used properly. But I don’t believe additional regulations are currently needed. There are already laws on the books of nations, states and towns governing civil and criminal liabilities for harmful actions. Our drones, for example, must obey FAA regulations, while the self-driving car AI must obey regular traffic laws to operate on public roadways.

Existing laws also cover what happens if a robot injures or kills a person, even if the injury is accidental and the robot’s programmer or operator isn’t criminally responsible. While lawmakers and regulators may need to refine responsibility for AI systems’ actions as technology advances, creating regulations beyond those that already exist could prohibit or slow the development of capabilities that would be overwhelmingly beneficial.

Potential risks from artificial intelligence

It may seem reasonable to worry about researchers developing very advanced artificial intelligence systems that can operate entirely outside human control. A common thought experiment deals with a self-driving car forced to make a decision about whether to run over a child who just stepped into the road or veer off into a guardrail, injuring the car’s occupants and perhaps even those in another vehicle.

Musk and Hawking, among others, worry that hypercapable AI systems, no longer limited to a single set of tasks like controlling a self-driving car, might decide it doesn’t need humans anymore. It might even look at human stewardship of the planet, the interpersonal conflicts, theft, fraud and frequent wars, and decide that the world would be better without people.

Science fiction author Isaac Asimov tried to address this potential by proposing three laws limiting robot decision-making: Robots cannot injure humans or allow them “to come to harm.” They must also obey humans – unless this would harm humans – and protect themselves, as long as this doesn’t harm humans or ignore an order.

But Asimov himself knew the three laws were not enough. And they don’t reflect the complexity of human values. What constitutes “harm” is an example: Should a robot protect humanity from suffering related to overpopulation, or should it protect individuals’ freedoms to make personal reproductive decisions?

We humans have already wrestled with these questions in our own, nonartificial intelligences. Researchers have proposed restrictions on human freedoms, including reducing reproduction, to control people’s behavior, population growth and environmental damage. In general, society has decided against using those methods, even if their goals seem reasonable. Similarly, rather than regulating what AI systems can and can’t do, in my view it would be better to teach them human ethics and values – like parents do with human children.

Artificial intelligence benefits

People already benefit from AI every day – but this is just the beginning. AI-controlled robots could assist law enforcement in responding to human gunmen. Current police efforts must focus on preventing officers from being injured, but robots could step into harm’s way, potentially changing the outcomes of cases like the recent shooting of an armed college student at Georgia Tech and an unarmed high school student in Austin.

Intelligent robots can help humans in other ways, too. They can perform repetitive tasks, like processing sensor data, where human boredom may cause mistakes. They can limit human exposure to dangerous materials and dangerous situations, such as when decontaminating a nuclear reactor, working in areas humans can’t go. In general, AI robots can provide humans with more time to pursue whatever they define as happiness by freeing them from having to do other work.

Achieving most of these benefits will require a lot more research and development. Regulations that make it more expensive to develop AIs or prevent certain uses may delay or forestall those efforts. This is particularly true for small businesses and individuals – key drivers of new technologies – who are not as well equipped to deal with regulation compliance as larger companies. In fact, the biggest beneficiary of AI regulation may be large companies that are used to dealing with it, because startups will have a harder time competing in a regulated environment.

The need for innovation

Humanity faced a similar set of issues in the early days of the internet. But the United States actively avoided regulating the internet to avoid stunting its early growth. Musk’s PayPal and numerous other businesses helped build the modern online world while subject only to regular human-scale rules, like those preventing theft and fraud.

Artificial intelligence systems have the potential to change how humans do just about everything. Scientists, engineers, programmers and entrepreneurs need time to develop the technologies – and deliver their benefits. Their work should be free from concern that some AIs might be banned, and from the delays and costs associated with new AI-specific regulations.

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As the wave of digital transformation and deep learning technologies continues to wash over the world of business, we are seeing significant changes in how companies are communicating with their customers.

With a recent report claiming that the AI software market will grow from $1.38 billion in 2016 to $59.75 billion by 2025, it’s clear that chatbots, machine learning and deep learning technologies will play a crucial role in the future of enterprise communications – but what exactly is it and what kind of impact will it have as the years go by?

In order to answer these questions, we decided to write a short yet informative piece on deep learning technology and what it brings to the table for businesses in post-digital world.

What is Deep Learning Technology?

AI is not an unfamiliar concept and the idea of machines that can think has existed for as far back as the 1950s. However, over the last decade or so, we have seen a monumental shift in the development of new AI technologies that are changing the way people communicate and taking Artificial Intelligence to a whole new level.

If we were to look at machine learning, a term used to describe the way a computer uses algorithms to analyse data, learn from it and then act on what it has learnt, we can see that AI works not by following a set of strict instructions set by a programmer, but instead functions independently to analyse wide sources of information available through the cloud and solve problems by itself.

Deep learning, then, could be said to be an extension of this that allows the computer to implement machine learning through a set of algorithms inspired by the structure of the human brain called artificial neural networks.

For example, by looking at Chatbots, we can see how the computer uses deep learning by feeding data (or a customer’s question) through various layers of these neural networks that ask a series of binary true/false questions to deduct the most logical, human like response by process of elimination.

What are the benefits of Deep Learning Technology?

With machine learning and deep learning technologies such as chatbots and voice recognition, businesses dealing in customer service are given a much more efficient, cost-effective communications solution that minimises call queues and ensures the customer’s problems are solved as quickly as possible.

Unlike the old days where largely ineffective IVRs were used to counter call queue traffic, chatbots offer a much more suitable alternative that will either solve the customer’s problem independently using those algorithms or direct the caller through to an agent specially trained to deal with that request.

As a result, the human operator is not replaced but is able to focus their attention specifically on areas of importance rather than spending valuable company time handling menial requests or redirecting calls through to other agents.

Deep Learning Technology Vendors to Watch

Deep learning technologies will no doubt become much more widely available in the future as the market continues to grow and the demand for them increases.

With this in mind, we have given a list of three interesting providers that are currently leading the way when it comes to AI in business.

The first to watch out for is AVISO – AVISO is a company that offers a cutting-edge AI-driven forecasting and sales visibility platform to business executives in order to help assist them in making critical revenue decisions and help them make the most out of every business opportunity.

The second is Deepgram – by using AI-based indexing and search in recorded audio, the technology will focus on keywords spoken by the customer and use the information to provide efficent phone support in customer service and contact centre settings.

Finally, there is Zoomi – with this, businesses can benefit from innovative machine learning and deep learning AI-driven technologies to boost employee training and ensure businesses can quickly train new staff to the best of their ability without disruption.

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Microsoft is setting up a new healthcare department at its ­Cambridge research facility, as part of plans to use its artificial intelligence software to ­enter the health market.

The computer giant has created the division as part of its commitment to “transform healthcare” using technologies such as machine learning and cloud computing.

Its research plans ­include monitoring systems that can help keep patients out of hospitals and alert them in a timely manner about problems, and large studies into ­diseases such as diabetes.

Microsoft has hired researcher Iain Buchan, formerly clinical professor in public health informatics at the ­University of Manchester, to lead the healthcare research division. A trained doctor and data scientist, he has researched how data can improve healthcare for the past 25 years in ­clinical and academic settings.

Mr Buchan said the team’s ­research could include developing predictive analytic tools, and personal health information systems, as well as using AI to ­target interventions.

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