Not be an alarmist, but if bees go extinct its likely that coffee would become a rare and expensive luxury commodity. And I dont want to live in that world.

Luckily, ApisProtect today announced its entry into the US market where it will provide its unique AI-powered hive monitoring system to beekeepers and farmers.

If youre unfamiliar, ApisProtect is a European startup that uses simple proprietary devices and a unique stack of AI and software to, essentially, give beekeepers a spy on the inside.

This means keepers can get ahead of health issues that otherwise could remain hidden. According to ApisProtect:

Beekeepers often rely on costly, time-consuming manual hive checks to understand their operation. However, ApisProtect research shows that 80% of manual hive inspections do not require any action on hives but disrupt the bees and risk the loss of a queen.

With ApisProtect, commercial beekeepers can now safely identify and respond to disease, pests, and other hive problems faster than ever before, thereby increasing colony size and preventing colony loss. ApisProtect lets beekeepers know immediately when specific hives need attention within their operation, as well as which hives are most productive.

Quick take: While specific projections may vary, its safe to say that honeybees are endangered to the point where solutions like this should be considered environmental safety efforts. 2020 was a crappy year for beekeepers coming off of a crappy decade for bees.

This is exactly the kind of thing AI is best suited for. Bees can adapt to just about anything but murder hornets and humans invading their spaces, and it turns out ApisProtect safeguards hives against both.

Pdraig Whelan, Co-Founder and Chief Science Officer of ApisProtect, said, in a press release:

ApisProtect technology could be a useful tool for the detection of murder hornets as a potential new threat to bee hives this pollination season.

Murder hornets can wipe out a bee hive in a matter of hours. Our platform can pinpoint the date at which a hive dies and distinguish whether this has been gradual or sudden. If a hive is healthy one day and dead the next, the beekeeper is alerted rather than having to wait for the next scheduled manual inspection.

The beekeeper can then prioritize visiting the hive and identify the tell-tale signs of a hornet attack. Precision beekeeping ensures the beekeeper can quickly take preventative measures to ensure the safety of their hives and others in the area.

The fact of the matter is that whether its murder hornets, climate change, or disease thats causing the problem, we need to fix it. Bees are incredibly important to the future survival of humans.

While the threat may have been blown out of proportion its unlikely well go extinct just because there are no more bees the loss of our honey-making friends would be a bonafide catastrophe.

According to the National Resources Defense Council:

If honeybees did disappear for good, humans would probably not go extinct (at least not solely for that reason). But our diets would still suffer tremendously. The variety of foods available would diminish, and the cost of certain products would surge.

The California Almond Board, for example, has been campaigning to save bees for years. Without bees and their ilk, the group says, almonds simply wouldnt exist.

Wed still have coffee without bees, but it would become expensive and rare. The coffee flower is only open for pollination for three or four days. If no insect happens by in that short window, the plant wont be pollinated.

For more information check out ApisProtects website here.

Published December 10, 2020 20:28 UTC

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Save the bees, save the world: How ApisProtect uses AI and IoT to protect hives - The Next Web

SANTA CLARA, Calif., May 28, 2021 /PRNewswire/ -- The Edge AI and Vision Alliance today announced the two winners of this year's Vision Tank Start-Up Competition. The annual competition showcases the best new ventures developing visual AI and computer vision products. During the final round of the competition, five finalists pitched their companies and products to a panel of judges in front of a live audience. The judges picked the winner of the Judges' Award, while attendees chose the winner of the Audience Choice Award.

JUDGES' AWARD: Retrocausal An industry leader in systems that help manufacturing workers avoid assembly mistakes, be more efficient at their daily jobs and improve the processes they drive: http://www.retrocausal.ai

AUDIENCE CHOICE AWARD: Opteran TechnologiesA brain biomimicry spin-out from the University of Sheffield, leveraging over eight years of research and 600 million years of evolution to understand how insect brains navigate and enable a new dawn for autonomy in machines: opteran.com

"We are seeing an amazing number and variety of new ventures using computer vision and visual AI to power products and solutionsacross all industries," said Jeff Bier, Founder of the Edge AI and Vision Alliance and General Chair of the Embedded Vision Summit. "I'm delighted to congratulate Retrocausal and Opteran Technologies for their progress towards bringing truly innovative technologies and solutions to fruition."

As winner of the Vision Tank Judges' Award, Retrocausal receives a $5,000 cash prize, and both winners receive a one-year membership in the Edge AI and Vision Alliance. In addition, the companies get one-on-one advice from the judges, and introductions to potential investors, customers, employees and suppliers.

Now celebrating its tenth year, the Embedded Vision Summit was held online, May 25-28. The conference is focused exclusively on practical, deployable computer vision and AI and attracts a global audience of professionals developing vision-enabled products.

About the Edge AI and Vision AllianceThe Edge AI and Vision Alliance is a worldwide industry partnership bringing together technology providers and end product companies who are creating and enabling innovative and practical applications for computer vision and edge AI. Membership is open to any company that supplies or uses technology for edge AI and vision systems and applications. For more information, visit edge-ai-vision.com.

MEDIA CONTACT:Brianna Crowl Mob: +1 (760) 687-5110Email: [emailprotected]

SOURCE Edge AI and Vision Alliance

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The Edge AI and Vision Alliance Announces the 2021 Vision Tank Start-Up Competition Winners at the Embedded Vision Summit - PRNewswire

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Artificial intelligence -- which encompasses everything from service robots to medical diagnostic tools to your Alexaspeaker -- is a fast-growing field that is increasingly playing a more critical role in many aspects of our lives. A country's AI prowess has major implications for how its citizens live and work -- and its economic and military strength moving into the future.

With so much at stake, the narrative of an AI "arms race" between the US and China has been brewing for years. Dramatic headlines suggest that China is poised to take the lead in AI research and use, due to its national plan for AI domination and the billions of dollars the government has invested in the field, compared with the US' focus on private-sector development.

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But the reality is that at least until the past year or so, the two nations have been largely interdependent when it comes to this technology. It's an area that has drawn attention and investment from major tech heavy hitters on both sides of the Pacific, including Apple, Google and Facebook in the US and SenseTime, Megvii and YITU Technology in China.

Generation China is a CNET series that looks at the areas of technology where the country is looking to take a leadership position.

"Narratives of an 'arms race' are overblown and poor analogies for what is actually going on in the AI space," said Jeffrey Ding, the China lead for the Center for the Governance of AI at the University of Oxford's Future of Humanity Institute. When you look at factors like research, talent and company alliances, you'll find that the US and Chinese AI ecosystems are still very entwined, Ding added.

But the combination of political tensions and the rapid spread of COVID-19 throughout both nations is fueling more of a separation, which will have implications for both advances in the technology and the world's power dynamics for years to come.

"These new technologies will be game-changers in the next three to five years," said Georg Stieler, managing director of Stieler Enterprise Management Consulting China. "The people who built them and control them will also control parts of the world. You cannot ignore it."

You can trace China's ramp up in AI interest back to a few key moments starting four years ago.

The first was in March 2016, when AlphaGo -- a machine-learning system built by Google's DeepMind that uses algorithms and reinforcement learning to train on massive datasets and predict outcomes -- beat the human Go world champion Lee Sedol. This was broadcast throughout China and sparked a lot of interest -- both highlighting how quickly the technology was advancing, and suggesting that because Go involves war-like strategies and tactics, AI could potentially be useful for decision-making around warfare.

The second moment came seven months later, when President Barack Obama's administration released three reports on preparing for a future with AI, laying out a national strategic planand describing the potential economic impacts(all PDFs). Some Chinese policymakers took those reports as a sign that the US was further ahead in its AI strategy than expected.

This culminated in July 2017, when the Chinese government under President Xi Jinping released a development plan for the nation to become the world leader in AI by 2030, including investing billions of dollars in AI startups and research parks.

In 2016, professional Go player Lee Sedol lost a five-game match against Google's AI program AlphaGo.

"China has observed how the IT industry originates from the US and exerts soft influence across the world through various Silicon Valley innovations," said Lian Jye Su, principal analyst at global tech market advisory firm ABI Research. "As an economy built solely on its manufacturing capabilities, China is eager to find a way to diversify its economy and provide more innovative ways to showcase its strengths to the world. AI is a good way to do it."

Despite the competition, the two nations have long worked together. China has masses of data and far more lax regulations around using it, so it can often implement AI trials faster -- but the nation still largely relies on US semiconductors and open source software to power AI and machine learning algorithms.

And while the US has the edge when it comes to quality research, universities and engineering talent, top AI programs at schools like Stanford and MIT attract many Chinese students, who then often go on to work for Google, Microsoft, Apple and Facebook -- all of which have spent the last few years acquiring startups to bolster their AI work.

China's fears about a grand US AI plan didn't really come to fruition. In February 2019, US President Donald Trump released an American AI Initiative executive order, calling for heads of federal agencies to prioritize AI research and development in 2020 budgets. It didn't provide any new funding to support those measures, however, or many details on how to implement those plans. And not much else has happened at the federal level since then.

Meanwhile, China plowed on, with AI companies like SenseTime, Megvii and YITU Technology raising billions. But investments in AI in China dropped in 2019, as theUS-China trade war escalated and hurt investor confidence in China, Su said. Then, in January, the Trump administration made it harder for US companies to export certain types of AI software in an effort to limit Chinese access to American technology.

Just a couple weeks later, Chinese state media reported the first known death from an illness that would become known as COVID-19.

In the midst of the coronavirus pandemic, China has turned to some of its AI and big data tools in attempts to ward off the virus, including contact tracing, diagnostic tools anddrones to enforce social distancing. Not all of it, however, is as it seems.

"There was a lot of propaganda -- in February, I saw people sharing on Twitter and LinkedIn stories about drones flying along high rises, and measuring the temperature of people standing at the window, which was complete bollocks," Stieler said. "The reality is more like when you want to enter an office building in Shanghai, your temperature is taken."

A staff member introduces an AI digital infrared thermometer at a building in Beijing in March.

The US and other nations are grappling with the same technologies -- and the privacy, security and surveillance concerns that come along with them -- as they look to contain the global pandemic, said Elsa B. Kania, adjunct fellow with the Center for a New American Security's Technology and National Security Program, focused on Chinese defense innovation and emerging technologies.

"The ways in which China has been leveraging AI to fight the coronavirus are in various respects inspiring and alarming," Kania said. "It'll be important in the United States as we struggle with these challenges ourselves to look to and learn from that model, both in terms of what we want to emulate and what we want to avoid."

The pandemic may be a turning point in terms of the US recognizing the risks of interdependence with China, Kania said. The immediate impact may be in sectors like pharmaceuticals and medical equipment manufacturing. But it will eventually influence AI, as a technology that cuts across so many sectors and applications.

Despite the economic impacts of the virus, global AI investments are forecast to grow from $22.6 billion in 2019 to $25 billion in 2020, Su said. The bigger consequence may be on speeding the process of decoupling between the US and China, in terms of AI and everything else.

The US still has advantages in areas like semiconductors and AI chips. But in the midst of the trade war, the Chinese government is reducing its reliance on foreign technologies, developing domestic startups and adopting more open-source solutions, Su said. Cloud AI giants like Alibaba, for example, are using open-source computing models to develop their own data center chips. Chinese chipset startups like Cambricon Technologies, Horizon Robotics and Suiyuan Technology have also entered the market in recent years and garnered lots of funding.

But full separation isn't on the horizon anytime soon. One of the problems with referring to all of this as an AI arms race is that so many of the basic platforms, algorithms and even data sources are open-source, Kania said. The vast majority of the AI developers in China use Google TensorFlow or Facebook PyTorch, Stieler added -- and there's little incentive to join domestic options that lack the same networks.

The US remains the world's AI superpower for now, Su and Ding said. But ultimately, the trade war may do more harm to American AI-related companies than expected, Kania said.

Now playing: Watch this: Coronavirus care gets help from AI

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"My main concern about some of these policy measures and restrictions has been that they don't necessarily consider the second-order effects, including the collateral damage to American companies, as well as the ways in which this may lessen US leverage or create much more separate or fragmented ecosystems," Kania said. "Imposing pain on Chinese companies can be disruptive, but in ways that can in the long term perhaps accelerate these investments and developments within China."

Still, "'arms race' is not the best metaphor," Kania added. "It's clear that there is geopolitical competition between the US and China, and our competition extends to these emerging technologies including artificial intelligence that are seen as highly consequential to the futures of our societies' economies and militaries."

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The US, China and the AI arms race: Cutting through the hype - CNET

Artificial Intelligence (AI) especially machine learning is a technology that is spreading rapidly around the world.

AI will become a standard tool to help steer cars, improve medical care or automate decision making within public authorities. Although intelligent technologies are drivers of innovation and growth, the global proliferation of them is already causing serious harm in its wake.

Last month, a leaked white paper showed that the European Union is considering putting a temporary ban on facial recognition technologies in public spaces until the potential risks are better understood.

But many AI technologies in addition to facial recognition warrant more concern, especially from European policymakers.

More and more experts have scrutinised the threat that 'Deep Fake' technologies may pose to democracy by enabling artificial disinformation; or consider the Apple Credit Card which grants much higher credit scores to husbands when compared to their wives, even though they share assets.

Global companies, governments, and international organisations have reacted to these worrying trends by creating AI ethics boards, charters, committees, guidelines, etcetera, all to address the problems this technology presents - and Europe is no exception.

The European Commission set up a High Level Expert Group on AI to draft guidelines on ethical AI.

Unfortunately, an ethical debate alone will not help to remedy the destruction caused by the rapid spread of AI into diverse facets of life.

The latest example of this shortcoming is Microsoft, one of the largest producers of AI-driven services in the world.

Microsoft, who has often tried to set itself apart from its Big Tech counterparts as being a moral leader, has recently taken heat for its substantial investment in facial recognition software that is used for surveillance purposes.

"AnyVision" is allegedly being used by Israel to track Palestinians in the West Bank. Although investing in this technology goes directly against Microsoft's own declared ethical principles on facial recognition, there is no redress.

It goes to show that governing AI - especially exported technologies or those deployed across borders - through ethical principles does not work.

The case with Microsoft is only a drop in the bucket.

Numerous cases will continue to pop up or be uncovered in the coming years in all corners of the globe given a functioning and free press, of course.

This problem is especially prominent with facial recognition software, as the European debate reflects. Developed in Big Tech, facial recognition products have been procured by government agencies such as customs and migration officers, police officers, security forces, the military, and more.

This is true for many regions of the world: like in America, the UK, as well as several states in Africa, Asia, and more.

Promising more effective and accurate methods to keep the peace, law enforcement agencies have adopted the use of AI to super-charge their capabilities.

This comes with specific dangers, though, which is shown in numerous reports from advocacy groups and watchdogs saying that the technologies are flawed and deliver more false matches disproportionately for women and darker skin tones.

If law enforcement agencies know that these technologies have the potential to be more harmful to subjects who are more often vulnerable and marginalised, then there should be adequate standards for implementing facial recognition in such sensitive areas.

Ethical guidelines neither those coming from Big Tech nor those coming from international stakeholders are not sufficient to safeguard citizens from invasive, biased, or harmful practices of police or security forces.

Although these problems have surrounded AI technologies in previous years, this has not yet resulted in a successful regulation to make AI "good" or "ethical" terms that mean well but are incredibly hard to define, especially on an international level.

This is why, even though actors from private sector, government, academia, and civil society have all been calling for ethical guidelines in AI development, these discussions remain vague, open to interpretation, non-universal, and most importantly, unenforceable.

In order to stop the faster-is-better paradigm of AI development and remedy some of the societal harm already caused, we need to establish rules for the use of AI that are reliable and enforceable.

And arguments founded in ethics are not strong enough to do so; ethical principles fail to address these harms in a concrete way.

As long as we lack rules that work, we should at least use guidelines that already exist to protect vulnerable societies to the best of our abilities. This is where the international human rights legal framework could be instrumental.

We should be discussing these undue harms as violations of human rights, utilising international legal frameworks and language that has far-reaching consensus across different nations and cultural contexts, is grounded in consistent rhetoric, and is in theory enforceable.

AI development needs to promote and respect human rights of individuals everywhere, not continue to harm society at a growing pace and scale.

There should be baseline standards in AI technologies, which are compliant with human rights.

Documents like the Universal Declaration of Human Rights and the UN Guiding Principles which steer private sector behaviour in human-rights compliant ways need to set the bar internationally.

This is where the EU could lead by example.

By refocusing on these existing conventions and principles, Microsoft's investment in AnyVision, for example, would be seen as not only a direct violation of its internal principles, but also as a violation of the UN Guiding Principles, forcing the international community to scrutinise the company's business activities more deeply and systematically, ideally leading to redress.

Faster is not better. Fast development and dissemination of AI systems has led to unprecedented and irreversible damages to individuals all over the world. AI does, indeed, provide huge potential to revolutionise and enhance products and services, and this potential should be harnessed in a way that benefits everyone.

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Why EU will find it difficult to legislate on AI - EUobserver

Artificial intelligence (AI) and machine learning (ML) have huge potential in many areas of business, particularly where there is a need to automate repetitive tasks.

This is of strategic importance for the IT security sector. Growing organisations dont always have the capability to scale up back-office compliance and security teams at a rate that is proportional to their expansion, leaving the existing function to do more with less; automating wherever possible reduces these pressures without compromising compliance.

Of course, AI and ML solutions are not new. We are already witnessing the success of adopting AI to automate everyday tasks such as identifying potential fraud, authenticating users and removing user access. It is ideal for repetitive tasks such as pattern analysis, source data filtering to determine factors such as whether something is an incident and, if so, whether it is critical, so tasks such as reviewing blocked emails, websites and images no longer have to be performed manually (ie by individuals).

AIs ability to simultaneously identify multiple data points that are indicators of fraud, rather than potential incidents having to be investigated line by line, also helps hugely with pinpointing malicious behaviour.

Predicting events before they occur is harder, but ML can help enterprises to stay ahead of potential threats using existing datasets, past outcomes and insight from security breaches with similar organisations all contribute to an holistic overview of when the next attack may occur. Fraud management solutions, security incident and event monitoring(SIEM), network traffic detection and endpoint detection all make use of learning algorithms to identify suspicious activity (based on previous usage data and shared pattern recognition) to establish normal patterns of use and flag outliers as potentially posing a risk to the organisation.

This capability is also critical in counteracting cyber attacks. Rather than manually trawling through a vast number of log files after an event has occurred, known intrusion methods can be identified in real time and mitigating action taken before much of the damage can occur.

To date, the main focus for the use of AI has been on the more technical security elements such as detection, incident management and other repeatable tasks. But these are early days, and there are many other areas that would benefit from its adoption. Governance, risk and compliance (GRC), for example, requires security professionals to crunch large amounts of data to spot risk trends and understand where non-compliance is causing incidents.

First discussions around AI saw it promise to revolutionise information security operations and reduce the amount of work that would need to be performed manually.

As outlined above, it has undoubtedly enabled new areas to be explored, while detecting attacks faster than any human manually looking through data. However, it is not a silver bullet and it comes with overheads, which are often forgotten.

It used to be that organisations installed logging systems that captured critical audit trails the challenge was in finding the time to look at the logs generated, a task that is now undertaken by AI scripts. However, while its easy enough to connect an application to an AI tool so that it can scan for suspicious activity, the AI system must first be set up so that it understands the format of the logs, and what qualifies as an event that needs flagging. In other words, to be effective, it needs training for the specific needs of each enterprise.

It is important not to underestimate these setup costs, along with the resource requirements to monitor the analytics AI provides. Incident management processes still need to be manually detailed so that once an event has been detected it can be investigated to make sure it wont impact the organisation.

Once AI is up and running it is a transformative tool for the organisation, but training it to interpret what action needs to be undertaken as well as rule out false positives is a time-consuming exercise that needs to be factored in to planning and budgets.

AI and ML introduce unprecedented speed and efficiency into the process of maintaining a secure IT estate, making them ideal tools for a predictive IT security stance.

But AI and ML cannot eliminate risk, regardless of how advanced they are, especially when there is an over-reliance on the capabilities of the technology, while its complexities are under-appreciated. Ultimately, risks such as false positives, as well as failure to identify all the threats faced by an organisation, are ever-present within the IT landscape.

Organisations deploying any automated responses therefore need to maintain a balance between specialist human input and technological solutions, while appreciating that AI and ML are evolving technologies. Ongoing training enables the team to stay ahead of the threat curve a critical consideration given that attackers also use AI and ML tools and techniques; defenders need to continually adapt in order to mitigate.

Successful AI and ML will mean different things to different organisations. Metrics may revolve around the time saved by analysts, how many incidents are identified, the number false positive removed, and so on. These should be weighed up against the resource required to configure, manage and review the performance of the tools. As with almost any IT security project, the overall value needs to be viewed through the eyes of the business and its role in achieving corporate objectives to reduce risk.

View original post here:

Security Think Tank: Ignore AI overheads at your peril - ComputerWeekly.com

Game theory

After the humans' gutsy attack plan failed, Libratus spent the rest of the competition inflating its virtual winnings. When the game lurched into its third week, the AI was up by a cool $750,000. Victory was assured, but the humans were feeling worn out. When I chatted with Kim and Les in their hotel bar after the penultimate day's play, the mood was understandably somber.

"Yesterday, I think, I played really bad," Kim said, rubbing his eyes. "I was pretty upset, and I made a lot of big mistakes. I was pretty frustrated. Today, I cut that deficit in half, but it's still probably unlike for me to win." At this point, with so little time left and such a large gap to close, their plan was to blitz through the remaining hands and complete the task in front of them.

For these world-class players, beating Libratus had gone from being a real possibility to a pipe dream in just a matter of days. It was obvious that the AI was getting better at the game over time, sometimes by leaps and bounds that left Les, Kim, McAulay and Chou flummoxed. It wasn't long before the pet theories began to surface. Some thought Libratus might have been playing completely differently against each of them, and others suspected the AI was adapting to their play styles while they were playing. They were wrong.

As it turned out, they weren't the only ones looking back at the past day's events to concoct a game plan for the days to come. Every night, after the players had retreated to their hotel rooms to strategize, the basement of the Supercomputing Center continued to thrum. Libratus was busy. Many of us watching the events unfold assumed the AI was spending its compute cycles figuring out ways to counter the players' individual play styles and fight back, but Professor Sandholm was quick to rebut that idea. Libratus isn't designed to find better ways to attack its opponents; it's designed to constantly fortify its defenses. Remember those major Libratus components I mentioned? This is the last, and perhaps most important, one.

"All the time in the background, the algorithm looks at what holes the opponents have found in our strategy and how often they have played those," Sandholm told me. "It will prioritize the holes and then compute better strategies for those parts, and we have a way of automatically gluing those fixes into the base strategy."

If the humans leaned on a particular strategy -- like their constant three-bets -- Libratus could theoretically take some big losses. The reason those attacks never ended in sustained victory is because Libratus was quietly patching those holes by using the supercomputer in the background. The Great Wall of Libratus was only one reason the AI managed to pull so far ahead. Sandholm refers to Libratus as a "balanced" player that uses randomized actions to remain inscrutable to human competitors. More interesting, though, is how good Libratus was at finding rare edge cases in which seemingly bad moves were actually excellent ones.

"It plays these weird bet sizes that are typically considered really bad moves," Sandholm explained. These include tiny underbets, like 10 percent of the pot, or huge overbets, like 20 times the pot. Donk betting, limping -- all sorts of strategies that are, according to the poker books and folk wisdom, bad strategies." To the players' shock and dismay, those "bad strategies" worked all too well.

On the afternoon of January 30th, Libratus officially won the second Brains vs AI competition. The final margin of victory: $1,766,250. Each of the players divvied up their $200,000 spoils (Dong Kim lost the least amount of money to Libratus, earning about $75,000 for his efforts), fielded questions from reporters and eventually left to decompress. Not much had gone their way over the past 20 days, but they just might have contributed to a more thoughtful, AI-driven future without even realizing it.

Through Libratus, Sandholm had proved algorithms could make better, more-nuanced decisions than humans in one specific realm. But remember: Libratus and systems like it are general-purpose intelligences, and Sandholm sees plenty of potential applications. As an entrepreneur and negotiation buff, he's enthusiastic about algorithms like Libratus being used for bargaining and auctions.

"When the FCC auctions spectrum licenses, they sell tens of billions of dollars of spectrum per auction, yet nobody knows even one rational way of bidding," he said. "Wouldn't it be nice if you had some AI support?"

But there are bigger problems to tackle ones that could affect all of us more directly. Sandholm pointed to developments in cybersecurity, military settings and finance. And, of course, there's medicine.

"In a new project, we're steering evolution and biological adaptation to battle viral and bacterial infections," he said. "Think of the infection as the opponent and you're taking sequential actions and measurements just like in a game." Sandholm also pointed out that such algorithms could even be used to more helpfully manage diseases like cancer, both by optimizing the use of existing treatment methods and maybe even developing new ones.

Jason, Dong, Daniel and Jimmy might have lost this prolonged poker showdown, but what Sandholm and his contemporaries have learned in the process could lead to some big wins for humanity.

Originally posted here:

How an AI took down four world-class poker pros - Engadget

A new social app called Giggle is pitching itself as a girls-only networking platform. To sign up, users have to take a selfie. And while that might not sound too invasive, the app then uses bio-metric gender verification software to determine whether that person is a woman. If that wasnt already bad enough, the technology doesnt work if youre trans.

[G]iggle is for all girls, the company points out on its website, before adding, Due to the gender-verification software that giggle uses, trans-girls will experience trouble with being verified. Its the stuff of a dystopian novel.

Giggle, founded by Australian screenwriter Sall Grover, supposedly looks at the bone structure of a persons face to determine their gender. Thats problematic on a number of fronts, not least of which is that bone structure is clearly a poor indicator of gender identity. Nevertheless, Giggle says the science is sound. Its Bio-Science, not pseudo-science like phrenology, the website declares.

Except it sounds a lot like pseudo-science. On Twitter, people called out the apps inherent transphobia. We shall await our judgement like sheep, one user wrote. All it takes is one selfieif giggle lets us in, we are welcomed into the society of women, to pass forevermore. If not, we shall be abandoned in a heap of offals and excrement.

Grover responded to the criticism, tweeting that shed consulted trans women while building the app and determined it was best to openly admit the softwares limits. We worked with trans girls who decided it was best to be upfront with a flaw so there wasnt any hurtful misgendering, she explained. Later, she said she was grateful for the feedback and agreed that some of the wording on the website was hideous.

The apps privacy policy is also a cause for concern, however. As one Twitter user pointed out, Giggle can collect a ton of personal information, including peoples images, location, preferences, and browsing data. Giggle is able to then share that information with third-party websites and services, including facial recognition providers, chat room providers, and marketers. It also collects sensitive information including peoples sexual practices or sex life, their criminal records, and their private health information.

Its unclear why Giggle would need access to such granular data, given that its goal is primarily to connect women with potential roommates or travel buddies. But in an era of ever-expanding surveillance, with companies like Clearview AI identifying peoples faces without their knowledge or consent, an app built on dubious biometric screening and extensive data collection should be cause for concern. While Giggles website says the app is designed to give girls choice, control and connection, its technology seems to do just the opposite.

Giggle did not immediately respond to a request for comment.

Read more from the original source:

This girls-only app uses AI to screen users genders what could go wrong? - The Verge

China is developing its artificial intelligence (AI) industry to accelerate its national strategy of China Made 2025. Source: Shutterstock

Artificial intelligence (AI) has already been identified as a crucial technology front, as nations and companies jockey to gain the edge in developing AI-driven applications. The potential impact of AI cannot be understated in todays business, with AI being considered a force multiplier because of its capacity to amplify company resources and to maximize output.

Technology powerhouses are well aware of the ability of AI to transform businesses in a variety of ways, which explains why so much money is being poured into AI startups. Spend on AI systems is expected to top US$77.6 billion in 2022, according to one IDC report, while another commissioned by Microsoft illustrated that AI will almost double the rate of innovation and workforce productivity in the Asia Pacific (APAC) region in the next three years.

With plenty of innovation being driven by AI, protecting these artificial intelligence inventions becomes crucial as well. And not just by organizations the US government has pledged to boost spending on AI next year by as much as US$1.5 billion, with US chief technology officer, Michael Kratsios, implicitly stating that the Trump administration had taken unprecedented action to prioritize American leadership in AI [] as the technology is increasingly seen as having strategic implications for the innovation leaders.

For now, the US maintains a wide lead in AI development as well as in the number of artificial intelligence patents that have been granted. But over the past two years, Chinese and South Korean technology firms have significantly increased their filing of AI patent applications.

According to patent application statistics released last week, Chinas patent office processed 4,636 AI patent applications over the last two years or 64.8% of all such IP claims since 2018. Patent figures compiled by RS Components also list Chinese companies and universities as dominating the list of top patent filers.

Chinas patent office has processed around two-thirds of all AI applications in the past couple of years, but the single entity with the most AI patents filed is LG Electronics of South Korea with 731 applications. Mirroring the 5G patent battle where Korean and Chinese firms are also the leading patent filers not just in APAC but also in the world, the second most patents have been applied for by Ping An Technology, a Chinese AI technology developer and cloud provider, with 308 patent applications in total.

China has also recently shown signs of realizing AIs strategic importance, with the government just amending a list of technologies to include AI that is now restricted or banned from being exported out of the country.

It is worth noting that while South Korea and China especially are coming on strong in filing so many AI patent applications, the runaway leader in terms of AI-related patents is still Intel Corp., which has been granted nearly 45,600 patents around artificial intelligence alone.

As it stands, both China and South Korea have earmarked AI as one of the cornerstone technologies to help revitalize their post-pandemic economic recovery. In fact, recent AI talent analysis by MacroPolo found that China is the largest source of top-tier AI researchers, with 29% of top AI talent coming from Chinese universities, ahead of 20% from the US.

Joe Devanesan| @thecrystalcrown

Joe's interest in tech began when, as a child, he first saw footage of the Apollo space missions. He still holds out hope to either see the first man on Mars, or Jetsons-style flying cars in his lifetime.

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Are China and South Korea quietly dominating AI innovation? - Tech Wire Asia

AI can now accurately predict death, but is that a prediction we want to hear?

In almost every industry, artificial intelligence (AI) is on the fast track to outpacing human endeavor. Machine learning technologies are already better than the average person at gaming, creating content and even building AI, and it appears they are only going from strength to strength.

As a result of their developing intelligence, the most common question AI critics have been asking is whether its ethical to be putting ourselves out of a job. YouTube video essayist CGP Grey put it best when he said that, by investing in AI development, we are steaming ahead towards a market in which humans need not apply without adequately preparing the population for that scenario.

However, there is another ethical question to ask about superhuman AI: do we truly want all our questions answered? Is there some knowledge that, given the option, wed actually prefer not to have? Perhaps the most profound piece of knowledge any one of us could have would be knowing when we die. The idea that we could predict death with 100% accuracy has been the subject of art and literature from Ancient Greece to modern science fiction and beyond, and its no wonder. The preservation of life is an evolutionary instinct and knowing whether and when that life will end is necessarily part of preserving it.

With regard to preserving and prolonging life, AI already has a very good track record. Frances AI in the hands of medical experts is a truly powerful tool to detect and deter disease. Deep learning technology based on retinal scans was shown to be a good indicator of cardiovascular health and a predictor of potential heart attacks, and also supremely accurate at indicating diabetes with the addition of expert assessment.

The greatest advantage of these early warning systems was the ability to anticipate treatment plans, particularly for conditions with potentially precipitous declines. One such disease is Alzheimers, the appearance of which can be hard to notice before the effects are irreversible. Thats why a 2017 study attempted to use machine learning to identify incipient Alzheimers dementia in patients. The system predicted the progression of dementia within the next 24 months and was accurate 84% of the time.

Considering all of this, its not all that surprising that AI is getting very good at predicting death. The most-quoted example of this was the University of Nottinghams study last year, which developed a deep- and machine-learning algorithm to predict premature death in patients aged 40 to 69.

Based on health data from 2006 to 2010 from over half a million people within the age range, the deep learning program was significantly more accurate in predicting death than the standard prediction models developed by a human expert. What this means in numbers is that the two AI algorithms were able to accurately identify 76% and 64% of subjects who died, respectively, while the human-generated prediction model predicted only 44%.

One of the lessons from the University of Nottingham study is that AI can be used to enhance human predictive models. The two systems used in the study arrived at their predictions by looking at different variables than the human model. While the human model leaned heavily on the ethnicity, gender, age, and physical activity of the subjects, one algorithm focussed on factors like body fat percentage and fruit and vegetable intake, while the most accurate algorithm looked mostly at job-related hazards and the consumption of alcohol and medication.

This means, that far from replacing scientists and healthcare professionals, AI can be used to shed new light on old problems, creating a partnership of humans and machines that could lead to new innovations.

However, the question still stands, how much do we want to know about our own mortality? Of course, the ability to identify life-risking habits and behaviors is an invaluable way to prevent unnecessary death and ease the burden on the healthcare industry worldwide. As systems become more sophisticated they will likely be able to identify specific actions and individual decisions that lead to a prolonged or foreshortened life. Insofar as prolonging life is the purpose of healthcare, AI certainly has a future as a tool to enhance the vital work of doctors and health scientists.

But at what point do we begin to shape our lives around the algorithm? Progressing to its logical conclusion, AI systems will likely soon have the ability to accurately predict the life expectancy of anyone. If you know you have 40 more years to live, how will that change the way you live those 40 years? What if it was 2 years?

Furthermore, is it possible that we are building a world in which we allow the predictions of machines to interfere with our ethical choices? A pregnant mother could know with near certainty that their baby will be born disabled from the moment of conception. How will that affect the ethical debate on abortion?

I do not have the answers to any of these questions I dont think anyone does but they begging to be asked. As we develop our technological abilities further, we need to assess how they affect our social and ethical lives.

Sources

Molly Crockett writes for UK writings and Academized. She is also an editor for Essay Roo. As a marketing writer, she shares her lifestyle and personal development advice with readers.

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The Ethics Of AI And Death - Big Easy Magazine

Machine learning models require human labor for data labeling

The hidden secret of artificial intelligence is that much of it is actually powered by humans. Well, to be specific, the supervised learning algorithms that have gained much of the attention recently are dependent on humans to provide well-labeled training data that can be used to train machine learning algorithms. Since machines have to first be taught, they cant teach themselves (yet), so it falls upon the capabilities of humans to do this training. This is the secret achilles heel of AI: the need for humans to teach machines the things that they are not yet able to do on their own.

Machine learning is what powers todays AI systems. Organizations are implementing one or more of the seven patterns of AI, including computer vision, natural language processing, predictive analytics, autonomous systems, pattern and anomaly detection, goal-driven systems, and hyperpersonalization across a wide range of applications. However, in order for these systems to be able to create accurate generalizations, these machine learning systems must be trained on data. The more advanced forms of machine learning, especially deep learning neural networks, require significant volumes of data to be able to create models with desired levels of accuracy. It goes without saying then, that the machine learning data needs to be clean, accurate, complete, and well-labeled so the resulting machine learning models are accurate. Whereas it has always been the case that garbage in is garbage out in computing, it is especially the case with regards to machine learning data.

According to analyst firm Cognilytica, over 80% of AI project time is spent preparing and labeling data for use in machine learning projects:

Percentage of time allocated to machine learning tasks (Source: Cognilytica)

(Disclosure: Im a principal analyst at Cognilytica)

Fully one quarter of this time is spent providing the necessary labels on data so that supervised machine learning approaches will actually achieve their learning objectives. Customers have the data, but they dont have the resources to label large data sets, nor do they have a mechanism to insure accuracy and quality. Raw labor is easy to come by, but its much harder to guarantee any level of quality from a random, mostly transient labor force. Third party managed labeling solution providers address this gap by providing the labor force to do the labeling combined with the expertise in large-scale data labeling efforts and an infrastructure for managing labeling workloads and achieving desired quality levels.

According to a recent report from research firm Cognilytica, over 35 companies are currently engaged in providing human labor to add labels and annotation to data to power supervised learning algorithms. Some of these firms use general, crowdsourced approaches to data labeling, while others bring their own, managed and trained labor pools that can address a wide range of general and domain-specific data labeling needs.

As detailed in the Cognilytica report, the tasks for data labeling and annotation depend highly on the sort of data to be labeled for machine learning purposes and the specific learning task that is needed. The primary use cases for data labeling fall into the following major categories:

These labeling tasks are getting increasingly more complicated and domain-specific as machine learning models are developed that can handle more general use cases. For example, innovative medical technology companies are building machine learning models that can identify all manner of concerns within medical images, such as clots, fractures, tumors, obstructions, and other concerns. To build these models requires first training machine learning algorithms to identify those issues within images. To train the machine learning models requires lots of data that has been labeled with the specific areas of concern identified. To accomplish that labeling task requires some level of knowledge as to how to identify a particular issue and the knowledge of how to appropriately label it. This is not a task for the random, off-the-street individual. This requires some amount of domain expertise.

Consequently, labeling firms have evolved to provide more domain-specific capabilities and expanded the footprint of their offerings. As machine learning starts to be applied to ever more specific areas, the needs for this sort of domain-specific data labeling will only increase. According to the Cognilytica report, the demand for data labeling services from third parties will grow from $1.7 Billion (USD) in 2019 to over $4.1B by 2024. This is a significant market, much larger than most might be aware of.

Increasingly, machines are doing this work of data labeling as well. Data labeling providers are applying machine learning to their own labeling efforts to perform some of the work of labeling, perform quality control checks on human labor, and optimize the labeling process. These firms use machine learning inferencing to identify data types, things that dont match the structure of a data column, potential data quality or formatting issues, and provides recommendations to users for how they could clean the data. In this way, machine learning is helping the process of improving machine learning. AI applied to AI. Quite interesting.

For the foreseeable future, the need for human-based data labeling for machine learning will not diminish. If anything, the use of machine learning continues to grow into new domains that require new knowledge to be built and learned by systems. This in turn requires well-labeled data to learn in those new domains, and in turn, requires the services of the hidden army of human laborers making AI work as well as it does today.

Continued here:

The Human-Powered Companies That Make AI Work - Forbes

Disruption is inevitable, but also deeply feared. Weve seen this with every significant technological leap -- from the printing press to automobiles to computers. But, as we enter the next iteration of technology with AI, we know it will have a profound, transformative effect on global business and society. However, we must reflect on how we want this transformation to occur.

Early adoption has already begun: AI is transforming everyday activities and processes such as virtual assistants, fraud detection and driverless cars. Various forms of AI solutions are already in the market, including automation, speech recognition, machine learning, decision-making and natural language processing. Organizations that are already investing in these technologies are better positioned for long-term success.

Related: Why Small Business Should Be Paying Attention to Artificial Intelligence

As a society, we must accept the fact that AI is here to stay, and realize thoughtful adoption of the technology is critical.

But, what does this mean for the workforce? For software developers, data scientists, engineers and the full spectrum of information technology workers, AI is perceived to either be putting their jobs at risk, or changing their responsibilities to accommodate its rapid advancement. While its difficult to predict the pace of AI adoption, some of the technologys most influential leaders and early adopters agree that its advancing faster than anticipated. As AIs development accelerates and implementations spread, it raises the question for workers in tech and other industries: Are my skills still relevant?

A positive, counterintuitive side effect of early AI adoption is that its requiring companies to invest in their employees. Bringing AI into the enterprise calls for investments in software and technologies that support its implementation, but also in the training and skill building for employees working alongside it. Companies cant go all-in on AI without balancing the investment ratio between technology and human workers.

Recentresearchby Infosys revealed that globally, 76 percent of decision makers agree AI is fundamental to the success of their organizations strategy. More optimistically, 80 percent of respondents say theyll retrain or redeploy employees whose roles are replaced or plan to be replaced with new technologies. This is why its essential to rethink our approach to education and employee development and lay a foundation for continuous lifelong learning.

Related: How to Learn Anything in the Age of AI

This shift in learning is necessary not only for the workforce today, but for future generations. We are developing and deploying AI systems that will become so advanced they will become part of the fabric of every industry. Students, academics and workers will need the skills and expertise to work intimately with AI systems. This new mentality requires a curious mindset and a thirst for knowledge and learning.

Decades from now, AI may replace cognitive tasks such as identifying and solving problems. Today, AI can identify patterns and anomalies in environments and production and notify humans about that information, which may not have been uncovered otherwise. However, human creativity and ingenuity will always be required tofindthe problems AI can solve in the first place.

After all, humans do not simply endure technological disruption -- they help shape it as part of our future. The advent of the automobile didnt just help us travel faster and further; rather, it led to roads, highways and entirely new industries.

Similarly, AI can be a great enabling force that amplifies and empowers people, improves the quality of life for all and opens up opportunities for the underprivileged. Its not a question of man versus machine, but manandmachine.

Related: Will a Robot Take My Job?

Providing employees with the opportunity to pursue learning and training programs to enhance their careers and help them understand new AI applications benefits employers as well. It encourages a more knowledgeable workforce thats inspired and motivated. It also creates the type of employees that become problem-finders seeking out the unknown unknowns, and begin the work of turning these problems into solutions. Increasingly, this will involve the aid of AI.

To reach the full human potential offered by AI, education and training must be a priority. For this to happen, digitalliteracy is fundamental for every future generation. Each child must have access to computer science courses. But, doing this requires a new perspective on education by both government and the private sector -- otherwise the education and skill sets of employees now and in the future wont rise to meet the rapid adoption of AI.

This also means rethinking education, recasting it as a life-long process,and deemphasizing rewarding memorization and routine in favor of curiosity and experimentation. We must modernize courses to encourage creative problem finding and solving, and learning through doing, with mandatory computer science learning as the bedrock for enabling digital literacy. Organizations also need to make life-long learning resources available for employees to enhance skills development and can dedicate a percentage of their annual revenue to reskilling staff.

Its a pivotal point in human history. AI is under construction before our eyes as the next great technological evolution, and we must be prepared to evolve alongside it.

Abdul Razack heads the Platforms Group at Infosys, focusing on overseeing platforms and reusable components across services, Big Data, automation, and the analytics business. Prior to Infosys, he worked at SAP, as senior vice president for...

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How to Prepare Employees to Work With AI - Entrepreneur

Americas solar farms have a bird problem. Utility companies have been finding bird carcasses littering the ground at their facilities for years, a strange and unexpected consequence of the national solar boom. No one was quite sure why this was happening, but it was clearly a problem for a type of energy that was billed as being environmentally friendly. So in 2013, a group of utilities, academics, and environmental organizations came together to form the Avian Solar Working Group to develop strategies to mitigate avian deaths at solar facilities around the US.

There was very little research about the impacts of solar on birds, says Misti Sporer, the lead environmental scientist at Duke Energy, an electric utility in North Carolina, and member of the working group. What does it mean when you find a dead bird? Nobody really knew. But simply getting the data on avian deaths at solar facilities proved challenging.

In 2016, a first-of-its-kind study estimated that the hundreds of utility-scale solar farms around the US may kill nearly 140,000 birds annually. Thats less than one-tenth of one percent of the estimated number of birds killed by fossil-fuel power plants (through collisions, electrocution, and poisoning), but the researchers expected that number to nearly triple as planned solar farms come online. The link between solar facilities and bird deaths is still unclear. One leading theory suggests birds mistake the glare from solar panels for the surface of a lake and swoop in for a landing, with deadly results. But that hypothesis is from a human perspective, says Sporer. Do birds even see the same way people do? We need to collect more data to form a complete picture.

Earlier this year, the Department of Energy awarded a team of researchers at Argonne National Laboratory in Illinois a $1.3 million contract to develop an artificial intelligence platform dedicated to studying avian behavior at large-scale solar facilities around the US. The researchers hope the data gathered by their system will help ornithologists unravel the mystery of why our feathered friends are dying in droves at solar farms. The important thing is to reduce solars environmental impact in every form, says Yuki Hamada, a biophysical scientist at Argonne who is leading the project. These avian issues are a concern and something that the renewable energy industry wants to understand and mitigate.

Only a few regions in the US have regulations that require solar operators to report avian deaths at their facilities; most of Americas large-scale solar farms dont bother with this time-consuming and morbid calculus. Those that do are limited in their ability to collect quality data and may only send surveyors to count bird carcasses at a solar farm once a month. While this helps solar plant operators understand how many birds are dying, it doesnt offer much insight into why theyre dying. For that, they need some real-time observations.

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Why Do Solar Farms Kill Birds? Call in the AI Bird Watcher - WIRED

By and

August 14, 2017, 1:44 PM EDT

Chris Lattner, a legend in the world of Apple software, has joined another rival of the iPhone maker: Alphabet Inc.s Google, where he will work on artificial intelligence.

Lattner announced the news on Twitter on Monday, saying he will start next week. His arrival at Mountain View, California-based Google comes after a brief stint as head of the automated driving program at Tesla Inc., which he left in June. Lattner made a name for himself during a decade-plus career at Apple Inc., where he created the popular programming language Swift.

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Lattner said he is joining Google Brain, the search giants research unit. There he will work on a different software language: TensorFlow, Googles system designed to simplify the programming steps for AI, according to a person with knowledge of the matter. Since Google released the software for free last year, it has become a key part of its strategy to spread and make money from AI. Last May, Google introduced a specialized chip set catered for the software, called a TPU, that rents through its cloud-computing service.

A Google spokesman didnt immediately respond to a request for comment.

After leaving Apple in January, Lattner went to Tesla, a recruiting coup for Chief Executive Officer Elon Musk. Lattner left after six months. In the end, Elon and I agreed that he and I did not work well together and that I should leave, so I did, he wrote in an update to his resume.

For more on artificial intelligence, check out the Decrypted podcast:

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Google Hires Former Star Apple Engineer for Its AI Team - Bloomberg

As artificial intelligence has become a growing force in business, todays top AI companies are leaders in this emerging technology.

Often leveraging cloud computing and edge computing, AI companies mix and match myriad technologies to meet and exceed use case expectations in the home, the workplace, and the greater community. Machine learning leads the pack in this realm, but todays leading AI firms are expanding their technological reach through other technology categories and operations, ranging from predictive analytics to business intelligence to data warehouse tools to deep learning, alleviating several industrial and personal pain points.

Entire industries are being reshaped by AI. RPA companies have completely shifted their platforms. AI in healthcare is changing patient care in numerous and major ways.

AI companies attract massive investment from venture capitalist firms and giant firms like Microsoft and Google that see the potential for further growth in corporate and personal use. Academic AI research is growing quickly in quantity and complexity, as are AI job openings across a multitude of industries. All of this growth and the exciting potential for new growth are documented in the AI Index, produced by Stanford Universitys Human-Centered AI Institute.

Consulting giant Accenture argues that AI has the potential to boost rates of profitability by an average of 38% and could lead to an economic boost of a whopping $14 trillion in additional gross value added (GVA) by 2035.

Especially during the COVID-19 pandemic, fields like healthcare have grown their interest and investment in AI, hoping to propel patient experiences forward in telemedicine, digital imaging, and a variety of other areas that give the patient greater access to medical resources they need.

Artificial intelligence clearly holds many possibilities, but IT professionals and other users should be cautious of a plethora of risks, such as job displacement. It will have a huge economic impact but also change society, and its hard to make strong predictions, but clearly job markets will be affected, said Yoshua Bengio, a professor at the University of Montreal, and head of the Montreal Institute for Learning Algorithms.

To keep up with the AI market, we have updated our list of top AI companies playing a key role in shaping the future of AI.

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Even during the COVID-19 pandemic where most industries reduced their total expenses to stay afloat, many companies actually increased their AI investments in 2020.

The AI vendors are leading the market by providing AI and ML through their popular cloud platforms, enabling companies to incorporate AI into applications and systems without the expense of in-house development.

The clear leader in cloud computing, AWS offers both consumer and business-oriented AI products and services, and many of its professional AI services build on the Ai services available in consumer products. Amazon Echo brings artificial intelligence into the home through the intelligent voice server, Alexa. For AWS, the companys primary AI services include Lex, a business version of Alexa; Polly, which turns text to speech; and Rekognition, an image recognition service.

Google, a leader in AI and data analytics, is on a massive AI acquisition binge, having acquired a number of AI startups in the last several years. Google is deeply invested in furthering artificial intelligence capabilities. In addition to using AI to improve its services, Google Cloud sells several AI and machine learning services to businesses. It has an industry-leading software project in TensorFlow, as well as its own Tensor AI chip project.

IBM has been a leader in the field of artificial intelligence since the 1950s. Its efforts in recent years center around IBM Watson, an AI-based cognitive service, AI software as a service, and scale-out systems designed for delivering cloud-based analytics and AI services. It has been acquisitive, purchasing several AI startups over several years. It benefits from having a strong cloud platform.

Microsoft offers a mix of consumer-facing and business/IT AI projects. On the consumer side, it has Cortana, the digital assistant that comes with Windows and is now available for smartphones other than Windows Phone, and the chatbot Zo that talks like a teenager. On its Azure cloud service, Microsoft sells AI services such as bot services, machine learning, and cognitive services.

The leading cloud computing platform in Asia, Alibaba offers clients a sophisticated Machine Learning Platform for AI. Significantly, the platform offers a visual interface for ease of use, so companies can drag and drop various components into a canvas to assemble their AI functionality. Also included in the platform are scores of algorithm components that can handle any number of chores, enabling customers to use pre-built solutions. Expect huge AI growth from Alibaba in the years to come.

These top AI providers are demonstrating that artificial intelligence can be used in a dazzling number of ways, across virtually every industry sector.

Palmer Luckey is one of the most intriguing figures in todays emerging tech. He co-founded Oculus, which Facebook bought for a cool $2 billion in 2014. Post-Facebook and at the ripe age of 27, he launched Anduril, which adds sophisticated sensors, vehicles, and drones to create a threat protection zone. Products include Sentry Tower (autonomous awareness), Ghost 4 sUAS (intelligent air support), and Anvil sUAS (precision kinetic intercept).

Formerly known as Sift Science, the company provides multiple online fraud management services in one platform. Sift mines thousands of data points from around the web to train in detecting fraud patterns. Its machine learning tools, bolstered by data analytics, seek insight into fraud before it happens.

Nauto offers an AI-powered driver behavior learning platform. So instead of self-driving cars, Nauto is an AI technology designed to improve the safety of commercial fleets and autonomous fleets. The platform assesses how drivers interact with the vehicle and the road ahead to reduce distracted driving and prevent collisions.

Tempus data-driven precision medicine uses AI to fight disease and bolster patient outcomes. It gathers and analyzes massive pools of medical and clinical data at scale to provide precision medicine that personalizes and optimizes treatments to each individuals specific health needs. Applications include neurology, psychiatry, and oncology.

In recent years, Salesforce has acquired a handful of AI companies and sharpened features of Salesforce Einstein, their artificial intelligence service. Their latest initiative, which includes an extensive team of data scientists, uses machine learning to help employees more efficiently perform tasks by simplifying and speeding them up. In addition to Salesforces employees, Einstein is available for customers who can build their own applications and are interested in features like Recommendation Builder, scorecards, and in-depth navigation insights.

A dominant vendor in the small but growing Robotic Process Automation market it actually coined the term RPA Automation Anywhere makes great use of AI. Its applications include attended RPA, which helps office employees do mundane, repetitive tasks much more efficiently, employing the power of machine learning. A vendor to watch.

SenSat builds digital copies of physical environments and applies AI modeling to understand the parameters of that environment and provide valuable feedback. For example, it can give spatial and volume statistics about a roadway that is about to undergo repair work. Boosting its fortunes, in October 2019, Tencent led a $10 million investment in SenSat.

Phrasee specializes in natural language generation for marketing copy. Its natural language generation system can generate millions of human-sounding variants of marketing at the touch of a button, allowing customers to tailor their copy to targeted customers. Retail/marketing and AI is a combination on a rapid growth curve in the AI sector. During the COVID-19 pandemic, several retailers, such as Walgreens, used Phrasee to boost customer engagement related to vaccination.

Using a combination of human freelancers and a system built with machine learning automation, Defined Crowd provides a data set that companies can leverage to improve the performance of their algorithms. This union of the human with AI is a brilliant stroke other startups are catching on, and you can expect many more startups to test out this combo.

Based in New York City, Pymetrics leverages AI to help companies hire the optimal candidates, by examining more than a resume scan. Customers have their best employees fill out the Pymetrics assessment, which then creates a model for what future ideal candidates should bring to the table. In essence, the AI-based system is attempting to find more new staff that will fit in well with the existing top staff, using AI and behavioral science.

Siemens, the famed legacy German multinational, focuses on areas like energy, electrification, digitalization, and automation. They also work to develop resource-saving and energy-efficient technologies and are considered a leading provider of devices and systems for medical diagnosis, power generation, and transmission. Yes, the Siemens website actually refers to AI at the beer garden.

Given how lucrative it is for hackers, will identity theft ever go away? Its unlikely, but New York City-based Socure is using AI to fight it. Its AI-enabled system monitors and checks the quality of countless data sources far more than a human, of course, but more importantly, far more than a legacy system that doesnt have the speed, flexibility, and insight of AI. Its motto is identify more real people in real-time. Socure was named a Cool Vendor 2020 in Gartners Cool Vendors in AI for Banking and Investments.

AEye builds the vision algorithms, software, and hardware used to guide autonomous vehicles. Its LiDAR technology focuses on the most important information in a vehicles sightline, such as people, other cars, and animals, while putting less emphasis on other landscape features like the sky, buildings, and surrounding vegetation. In February 2021, AEye entered into a merger agreement with CF Finance Acquisition Corp. III, so if/when the deal closes, expect more investment and innovation in the near future.

In a world with a vast ocean of podcasts and videos to transcribe, Rev uses AI to find its market. An AI-powered but human-assisted transcription provider, the company also sells access to developers, so tech-savvy folks can use its speech recognition technology. But the key part here is the combination of humans with AI, which is a sweet spot in the effective use cases for artificial intelligence. With a growing need for accessibility features in audiovisual production especially, expect more AI companies to take advantage of a similar business model in the future.

Its not enough that Suki offers an AI-powered software solution that assists doctors as they make voice notes on a busy day. Sukis aim using the power of AI to learn over time is to mold and adapt to users with repeated use, so the solution becomes more of a time saver and efficiency booster for physicians and healthcare workers over time. As a sign of the times, Suki was delivered with COVID-19 data and templates to speed the critically important vaccination and health tracking processes.

In the future, everything will be tracked by intelligent cameras. Verkada is working to create that future by offering a network of AI-assisted cameras that can handle sophisticated movement monitoring, through a software-first approach to security. Given all the uses for such cameras, which employ the cloud, its no surprise that the companys clients range from schools to shopping malls.

DataVisor uses machine learning to detect fraud and financial crime, utilizing unsupervised machine learning to identify attack campaigns before they result in any damage. DataVisor protects companies from attacks such as account takeovers, fake account creation, money laundering, fake social posts, fraudulent transactions, and more.

Founded in 2016, People.ais goal is to streamline the life of salespeople, assisting them in putting the reams of small details into relevant CRM systems, chiefly Salesforce. Think of all those pesky info bits from texting, your calendar, endless Slack conversations People.ai aims to help you with all of that. Plus: the system attempts to coach sales reps on the most effective ways to manage their time.

AlphaSense is an AI-powered search engine designed for investment firms, banks, and Fortune 500 companies. The search engine focuses on searching for important information within earnings call transcripts, SEC filings, news, and research. The technology also uses artificial intelligence to expand keyword searches for relevant content.

The remarkable truth about AI is that it keeps moving up the food chain in terms of the sophisticated tasks it can handle. Taking a big step up from simple automation, Icertis with a decade under its belt handles millions of business contracts through a method they call contract intelligence. Leveraging the cloud, the companys solution automates certain tasks and scans previous contract details. The company has gained some big clients like Microsoft and has been named a Gartner 2020 Leader.

Casetext is an AI-powered legal search engine that specializes in legal documents, with a database of more than 10 million statutes, cases, and regulations. A recent study comparing legal research platforms found that attorneys using Casetexts CARA AI finished their research more than 20% faster, required 4.4 times fewer searches to accomplish the same research task, and rated the cases they found as significantly more relevant than those found with a legacy research tool.

Blue River Technology is a subsidiary of Deere & Co. that combines artificial intelligence and computer vision to build smart farm tech clearly a growing need, given population growth. The companys See & Spray technology can detect individual plants and apply herbicide to the weeds only. This reduces the number of chemicals sprayed by up to 90% over traditional methods.

Nvidias emergence as an AI leader was hardly overnight. It has been promoting its CUDA GPU programming language for nearly two decades. AI developers have come to see the value in the GPUs massively parallel processing design and embraced Nvidia GPUs for machine learning and artificial intelligence. One area Nvidia is making a big push is in self-driving cars, but it is one of many efforts on the horizon.

Automation in factories has been progressing for years, even decades, but Bright Machines is working to push it a quantum leap forward. Based in San Francisco, the AI company is leveraging advances in robotics like machine learning and facial recognition to create an AI platform for digital manufacturing. Its solutions can accomplish any number of fine-grain tasks that might previously have required the exactitude of a skilled human.

Orbital Insight uses satellite geospatial imagery and artificial intelligence to gain insights not visible to the human eye. It uses data from satellites, drones, balloons, and other aircraft to look for answers or insight on things related to the agriculture and energy industries that normally wouldnt be visible. The company touts itself as the leader in geospatial analytics.

Once a standalone company and now a division of MasterCard, Brighterion offers AI for the financial services industry, specifically designed to block fraud rates. The companys AI Express is a fast-to-market solution within 6-8 weeks that is custom designed for customer use cases. Its solution is used by the majority of the 100 largest banks.

H2O.ai provides an open-source machine learning platform that makes it easy to build smart applications. Used by many thousands of data scientists across a large community of organizations worldwide, H2O claims to be the worlds leading open-source deep learning platform. H20.ai provides solutions for insurance, healthcare, telecom, marketing, financial service, retail, and manufacturing.

With a long legacy as the top chipmaker, Intel has both hardware and software AI initiatives in the works. Its Nervana processor is a deep learning processor, while Movidius is geared toward neural networks and visual recognition. Intel is also working on natural language processing and deep learning through software and hardware. Further indicating their commitment to AI, one of the companys slogans is accelerate your AI journey with Intel.

Clarifai is an image recognition platform that helps users organize, filter, and search their image database. Images and videos are tagged, teaching the technology to find similarities in images. Its AI solution is offered via mobile, on-premise, or API. Beyond image recognition, Clarifai also offers solutions in computer vision, natural language processing, and automated machine learning.

Geared to assist the busiest of people, X.ais intelligent virtual assistant Amy helps users schedule meetings. The concept is simple if you receive a meeting request but dont have time to work out logistics, you copy Amy onto the email and she handles it. Through machine learning and natural language processing, Amy schedules the best time and location for your meeting based on your preferences and schedule. We all need a helper like this in our lives.

Zebra Medical Systems is an Israeli company that applies deep learning techniques to the field of radiology. It claims it can predict multiple diseases with better-than-human accuracy by examining a huge library of medical images and specialized examination technology. It recently moved its algorithms to Google Cloud to help it scale and offer inexpensive medical scans.

Iris.AI helps researchers sort through cross-disciplinary research to find relevant information, and as it is used more often, the tool learns how to return better results. Since its launch, countless people have tried the service, some becoming regular users. Its Iris.AI release includes the Focus tool, an intelligent mechanism to refine and collate a reading list of research literature, cutting out a huge amount of manual effort.

Freenome uses artificial intelligence to conduct cancer screenings and diagnostic tests to spot signs of cancer earlier than possible with traditional testing methods. It uses non-invasive blood tests to recognize disease-associated patterns. The companys solution has trained on cancer-positive blood samples, which enable it to detect problems using specific biomarkers.

Neurala claims that it helps users improve visual inspection problems using AI. It develops The Neurala Brain, a deep learning neural network software that makes devices like cameras, phones, and drones smarter and easier to use. AI tends to be power-hungry, but the Neurala Brain uses audio and visual input in low-power settings to make simple devices more intelligent.

Graphcore makes what it calls the Intelligence Processing Unit (IPU), a processor specifically for machine learning, used to build high-performance machines. The IPUs unique architecture allows developers to run current machine learning models orders of magnitude faster and undertake entirely new types of work not possible with current technologies.

CognitiveScale builds customer service AI apps for the healthcare, insurance, financial services, and digital commerce industries. Its products are built on its Cortex-augmented intelligence platform for companies to design, develop, deliver, and manage enterprise-grade AI systems. It also has an AI marketplace, which is an online AI collaboration system where business experts, researchers, data scientists, and developers can collaborate to solve problems.

iCarbonX is a Chinese biotech startup that uses artificial intelligence to provide personalized health analyses and health index predictions. It has formed an alliance with seven technology companies from around the world that specialize in gathering different types of healthcare data and will use algorithms to analyze genomic, physiological, and behavioral data. It also works to provide customized health and medical advice.

Human Resources can be a bifurcated digital workspace, with different apps for each task that HR handles. OneModel is a talent analytics accelerator that helps HR departments handle employees, career pathing, recruiting, succession, exits, engagement, surveys, HR effectiveness, payrolls, planning, and other HR features all in one place and in a uniform way. The companys core goal is to equip HR pros with machine learning smarts.

AI meets social media. Lobster Media is an AI-powered platform that helps brands, advertisers, and media outlets find and license user-generated social media content. Its process includes scanning major social networks and several cloud storage providers for images and video, using AI-tagging and machine learning algorithms to identify the most relevant content. It then provides those images to clients for a fee.

Next IT, now part of Verint, is one of the pioneers in customer service chatbots. It develops conversational AI for customer engagement and workforce support on any endpoint through intelligent virtual assistants (IVAs). The companys Alme platform powers natural language business products that are continually enhanced through AI-powered tools that empower human trainers to assess performance and end-user satisfaction.

Pointr is an indoor positioning and navigation company with analytics and messaging features that help people navigate busy locations, like train stations and airport terminals. Its modules include indoor navigation, contextual notifications, location-based analytics, and location tracking. Its Bluetooth beacons use customer phones to help orient them around the building.

One of the largest social media companies to come out of China, Tencent has an advanced AI lab that developed tools to process information across its ecosystem, including natural language processing, news aggregators, and facial recognition. They also have one of Chinas top video streaming platforms, Tencent Music. A giant in the field, they fund several AI efforts.

A fairly new startup in the AI copywriting space, Copy.ai uses basic inputs from users to generate marketing copy in seconds. It can create copy for a variety of different formats, including article outlines, meta descriptions, digital ads and social media content, and sales copy. In March 2021, it was announced that Copy.ai raised $2.9 million in investments from Craft Ventures and several other smaller investors. With its use of the GPT-3 language model to generate words, Copy.ai is a content-driven AI tool to keep an eye on.

Twilio is a cloud communications platform as a service (PaaS) company that allows software developers to integrate text messages, phone calls, and video calls into applications through the use of various APIs. Twilios services are accessed over HTTP and are billed based on usage. The Twilio Autopilot offering allows companies to build and train AI-driven chatbots.

ViSenzes artificial intelligence visual recognition technology works by recommending visually similar items to users when shopping online. Its advanced visual search and image recognition solutions help businesses in eCommerce, mCommerce, and online advertising by recommending visually similar items to online shoppers.

Based in Asia, SenseTime develops facial recognition technology that can be applied to payment and picture analysis. It is used in banks and security systems. Its valuation is impressive, racking several billion dollars in recent years. The company specializes in deep learning, education, and fintech.

Using machine learning to mine health data for cancer research, Flatiron finds cancer research information in near real-time, drawing on a variety of sources. The company raised more than $175 million in Series C funding before being acquired by cancer research giant Roache.

Deep 6 uses AI to, in its own words, find more patients in minutes, not months. The patients in this sense are participants in clinical trials a critical part of the research process in developing new medicine. Certainly one of the challenging issues that was faced during the quest for a COVID-19 vaccine was finding a community of appropriate candidates. Deep 6 finds these kinds of communities by using an AI-powered system to scan through medical records, with the ability to understand patterns in human health.

Considered one of the best AI-driven customer support tools out there, Directly counts Microsoft as a customer. It helps its customers by intelligently routing their questions to chatbots to answer their questions personally, or to customer support personnel. It prides itself on intelligent automation.

Based in Montreal, Element AI provides a platform for companies to build AI-powered solutions, particularly for firms that may not have the in-house talent to do it. Element AI says it supports app-building for predictive modeling, forecasting modeling, conversational AI and natural language processing, image recognition, and automatic tagging of attributes based on images. The company was founded in 2016.

Pony.ai develops software for self-driving cars and was created by ex-Google and Baidu engineers who felt that the big companies are moving too slow. It has already made its first fully autonomous driving demonstration. It now operates a self-driving ride-sharing fleet in Guangzhou, China, using cars from a local automaker. The company raised $400 million from Toyota.

Focusing on enterprise AI, C3.ai offers a wide array of pre-built applications, along with a PaaS solution, to enable the development of enterprise-level AI, IoT applications, and analytics software. These AI-fueled applications serve a wide array of sectors and industry verticals, from supply chains to healthcare to anti-fraud efforts. The goal is to speed and optimize the process of digital transformation.

Some of the best applications of AI look into the future to prevent future problems. Such is the goal with BigPanda, which leverages AI to lessen or stop IT outages before they take down a full business, an eCommerce operation, or a mission-critical application. In essence, this companys goal is the magic of AIOps, using AI to improve admin and IT operations. A major growth area.

Accubits, a top-rated AI development company, focuses most of its energy on helping businesses enable AI for new efficiencies in their existing systems. Some of their AI solutions include intelligent chatbots in CRMs and predictive health diagnostics, both of which are designed to mesh with your existing software infrastructure. Accubits works across industries like consumer technology, automotives, cybersecurity, healthcare, and fashion.

Stem is a veteran energy storage firm that has adopted AI to help automate energy management. It uses its industry-leading AI platform, Athena, to determine when to charge energy storage systems and when to draw on them. Athena focuses on energy forecasting and automated control.

The robots imagined by 1950s futurists were tin men that could walk and talk and probably become masters of the human race. It hasnt turned out that way (fortunately), but Bossa Nova Robotics is using AI to make todays robots more effective. Indeed, modern robots are rarely shaped like humans; Bossa Novas robots resemble tall vacuum cleaners. Ironically, Bossa Nova started as a robotic toymaker but now has full-scale robots in retailers like Walmart. The robots roll up and down the shelves, spotting inventory problems and allowing cost savings on human workers.

In a world run by data, in many cases, someone or some system has to prep that data so that its usable. Data prep is unglamorous but absolutely essential. Tamr combines machine learning and human tech staff to help customers optimize and integrate the highest value datasets into its operations. Referred to as an enterprise-scale data unification company, Tamr enables cloud-native, on-premise, or hybrid scenarios truly a good fit for todays data-driven, multi-cloud world.

Formerly known as InsideSales.com, Xant underwent a major rebrand and now focuses on the enterprise market. It is a sales acceleration platform with a predictive and prescriptive self-learning engine, assisting in a sale and providing guidance to the salesperson to help close the deal. At its core is machine learning.

Dataminr is a global real-time information discovery company that monitors news feeds for high-impact events and critical breaking news far faster than your Google newsfeed. It cuts through the clutter of non-news or irrelevant news to specific industries and only provides highly relevant news when it happens. For news-sensitive vendors, its goal is to detect early risks from media coverage.

Theres a gray area in our lives in terms of healthcare; we ask ourselves, does this problem Im having really require making a doctors appointment, or could a major dose of simple information be enough? K Healths AI solution operates in this area. Users can text with a doctor or find similar cases near them, which has been particularly useful for COVID-19. Using a model built from a vast store of anonymous health records, its system offers help based on how a users complaint correlates with this vast history of other patients. Think of K Health as the advanced edge of telemedicine.

Driving the AI revolution with the highly capable smartphone chips it makes, Qualcomm leverages a signal processor for image and sound capabilities. In March 2021, Qualcomm acquired NUVIA, a competitive CPU and technology design company, ultimately enhancing CPU opportunities for the future. Given its market size and power, its likely that Qualcomm will continue to be a key driver of AI functionality in the all-important consumer device market.

HyperScience is designed to cut down on the tedium of mundane tasks, like filling out forms or data entry of hand-written forms. It also processes the relevant information from forms rather than requiring that a human read through the whole form. It touts itself as intelligent document processing.

Vivints Smart Home is a popular smart home service in North America, with features like security cameras, heating and cooling management, door and window security, and a remote speaker to talk to people at the door. All of this is monitored by AI, which learns the residents behavioral patterns and adjusts management accordingly.

While Facebook is certainly better known in other areas as one of the largest social media networks in the world, the company is making great strides in its AI capabilities, especially in self-teaching for its newsfeed algorithms. Most significantly, the Facebook team has started using AI to screen for hate speech, fake news, and potentially illegal actions across posts on the site.

Symphony Ayasdi is a machine intelligence software company that offers intelligent applications to its clients around the world for using Big Data and complex data analytics problems. Its goal is to help customers automate what would be the manual processes of using their own unique data. In March 2021, Symphony AyasdiAI announced a new partnership with Sionic, leading to a greater focus on financial crime detection. Very much focused on the enterprise AI sector.

A well-known technology company in the contract world, DocuSign uses esignature technology to digitize the contracting process across a multitude of industries. Many users dont realize some of the AI features that DocuSign powers, such as AI-powered contract and risk analysis that gets applied to a contract before you sign. This AI process lends itself to more efficient contract negotiation and/or renegotiations.

This cloud-based SaaS firm focuses on endpoint security. Leveraging AI, CrowdStrikes Falcon platform enables it to identify what it calls active indicators of attack to detect malicious activity before a breach actually happens. It presents the network administrators with actionable intelligence of real-time findings for them to take necessary action.

Cylance, now a division of BlackBerry, develops security apps that prevent instead of reactively detecting viruses and other malware. Using a mathematical learning process, Cylance identifies what is safe and what is a threat rather than operating from a blacklist or whitelist. The company claims its machine learning has an understanding of a hackers mentality to predict their behavior.

Tetra Tech uses AI to take notes on phone calls, so people working in call centers can focus on discussions with the callers. It uses AI to generate a detailed script of dialogues using its speech recognition technology. Given the large market for call centers and the need to make them more effective at low cost this is a big market for AI.

Nuro makes very small self-driving electric delivery trucks designed for local deliveries, such as groceries or takeout. Its founders previously worked on Googles Waymo self-driving car project. Overall the companys goal is to boost the value of robotics in daily life.

SoundHound started as a Shazam-like song recognition app called Midomi, but it has expanded to answering complex voice prompts like Siri and Cortana. But instead of converting language into text like most virtual assistants, the apps AI combines voice recognition and language understanding into a single step.

Acquired in a $1.2 billion high profile deal by Amazon, Zoox is focused on self-driving cars or, in the larger sense, a self-driving fleet (hence Amazons interest). Their AI-based vehicle is geared for the robo-taxi market.

Founded in 2013, AI biotech company Zymergen describes itself as a biofacturer. One of their offerings is called Hyline, a bio-based polyimide film. Their work includes applications for pharmaceuticals, agriculture, and industrial uses. Based in Emeryville, California.

A company designed to help digital advertisers run targeted digital advertising campaigns, The Trade Desk uses AI to optimize its customers advertising campaigns for their appropriate audiences. Their AI, known as Koa, was built to analyze data across the internet to figure out what certain audiences are looking for and where ads should be placed to optimize reach and cost. The Trade Desk also allows you to launch your digital ads independently, but uses its AI to offer performance suggestions while your campaign is live.

Based in China, DJI is a big player in the rapidly growing drone market. The company is leveraging AI and image recognition to track and monitor the landscape, and its expected that the company will play a role in the self-driving car market. Impressively, DJI has partnered with Microsoft for a drone initiative.

Running AI is exceptionally data-intensive the more data the better and so todays chipmakers (like Intel and Nvidia) are star players. Add to that list HiSilicon. The company fabricated the first AI chip for mobile units. Impressively, the chip accomplishes tasks like high-speed language translation and facial recognition.

Insitro operates at the convergence of human biology and machine learning. More specifically, it uses artificial intelligence to build models of various human illnesses, using those models to forecast previously unknown solutions far beyond human intuition. These models use the power of ML to improve drug discovery and development. Founded by Daphne Koller, Insitro has drawn investment from an exhaustive array of VC and financial firms.

A leading RPA company, Blue Prism uses AI-fueled automation to do an array of repetitive, manual software tasks, which frees human staff up to focus on more meaningful work. The companys AI laboratory researches automated document reading and software vision. To further boost its AI functionality, Blue Prism bought Thoughtonomy, which has AI based in the cloud.

You have surely encountered the limited conversational elan of a chatbot; a few stock phrases delivered in a monotone. Rulai is working to change this using the flexibility and adaptability of AI. The company claims its level 3 AI dialog manager can create multi-round conversation, without requiring code from customers. Clearly a major growth area.

Think of these forward-looking AI companies as taking a particularly inventive approach to machine learning and AI.

OpenAI is a non-profit research firm that operates under an open-source type of model to allow other institutions and researchers to freely collaborate, making its patents and research open to the public. The founders say they are motivated in part by concerns about existential risk from artificial general intelligence.

With backing by some real heavyweights Jeff Bezos, Elon Musk, and Mark Zuckerberg Vicariouss goal is nothing less than to develop a robot brain that can think like a human. It hasnt been particularly forthcoming with details, but its AI robots, geared for industrial automation, are known to learn as they do more tasks.

Arguably the coolest application of AI on this entire list, Ubiquity6 has built a mobile app that enables augmented reality for several people at once. Users see and interact with objects presented by the fully dimensioned visual world of the Ubiquity app, immersing themselves in a creative or educational environment. The companys website is worth visiting for its visual creativity and wonderment alone.

Originally posted here:

Top Performing Artificial Intelligence (AI) Companies of 2021

Image: Getty Images/iStockphoto

Like most nebulous technologies marketed as the cure-all for the enterprise in the 21st century, artificial intelligence--and more specifically anyone tasked with selling it--promises a lot. But there are some major obstacles to adoption for both the public and private sector, and understanding them is key to understanding the limits and potential of AI technologies as well as the risks inherent in the Wild West of enterprise solutions.

Consulting firm Booz Allen Hamilton has helped the US Army use AI for predictive maintenance and the FDA to better understand and combat the opioid crisis, so it knows a thing or two about getting large, risk-averse organizations behind meaningful AI deployments.

For insights on where AI still stumbles, as well the hurdles it will have to clear, I reached out to Booz Allen'sKathleen Featheringham, Director of AI Strategy & Training. She identified the five greatest barriers to AI adoption, which apply equally to public and private sector organizations.

Note: The below answers to interview questions have been rearrangedand formatted slightly to obtain listicle perfection. All language is Kathleen's, with thanks for her keen insights.

AI governance or the lack thereof. As with any powerful technology, AI requires structure in its implementation, which should govern its capabilities, and ethical principles.

It's important to remember that AI solutions are built by imperfect humans. We've seen examples of models that unintentionally generate discriminatory outcomes because the underlying data was skewed towards a particular segment of the population. Whether they resulted from bias in the dataset (e.g., exclusion or sample bias) or from humans' unconscious biases, these outcomes rightly erode trust in the technology and slow adoption.

So how do we fix it?

We must balance freedom, ethics and privacy with efficiency and other benefits AI makes possible.This foundation for AI requires that people at all levels of an organization understand their role in building a governance structure. A strong governance system includes a set of ethical design and development principles that are regularly reviewed, creating a "feedback loop."

It's important to consider these three points when developing a governance framework for AI: 1. Prioritize ethics early. 2. Build robust, transparent, and explainable systems that clearly yield an audit trail with the understanding as the models learn these can adjust. 3. Ensure measured, monitored roll-outs with robust governance and oversight, guided by clearly document processes.

Although AI could be the most transformative technological development of our lifetime, a methodical approach to implementation and adoption is critical. This starts with readying the organization from a cultural standpoint, enabling adoption through effective education on the technology (and therefore trust in it) and offering the necessary technical training.

There has been too much concentration on one-and-done tool trainings which hamper the development of the next generation of data engineers, of which there is a critical shortage currently. Continual education and training over years is needed to evolve their tradecraft/skills and speed adoption.

So how do we fix it?

Successful and ethical adoption of AI relies on people who understand and are empowered to put this technology to work. This means building a diverse and AI-knowledgeable workforce, creating opportunities for upskilling and learning across disciplines.

It is equally important to communicate the organization's objectives clearly while giving employees a voice in how AI will affect the workplace.

The data and systems operated by AI must be protected from both accidental and malicious interference. There are bad actors who attempt to change AI outcomes by "poisoning" underlying data. A familiar example is a few pieces of tap that trick an autonomous car into seeing a speed limit road sign as a "Stop" sign. This and privacy are very real concerns given AI must be entrusted with a certain amount of autonomy to perform its tasks.

AI is still vulnerable to adversarial attacks where it can be "tricked" and its analytical capabilities put to nefarious use. Given the vast amounts of data AI's needs to perform, protecting that data becomes of paramount importance. And since AI's decision-making process is still largely a black box, this is a vulnerability that causes great concern.

The solution? It's all about transparency ...

Because AI is still evolving from its nascency, different end users may have wildly different understandings about its current abilities, best uses and even how it works. This contributes to a blackbox around AI decision-making. To gain transparency into how an AI model reaches end results, it is necessary to build measures that document the AI's decision-making process. In AI's early stage, transparency is crucial to establishing trust and adoption.

While AI's promise is exciting, its adoption is slowed by historical fear of new technologies. As a result, organizations become overwhelmed and don't know where to start. When pressured by senior leadership, and driven by guesswork rather than priorities, organizations rush to enterprise AI implementation that creates more problems.

Which leads us to ...

AI often relies on large volumes of historical data and sophisticated mathematics. Before an AI project can be implemented, organizations must achieve a certain level of data and infrastructure readiness. Common barriers include data shortcomings and disparate data sources, lack of technological infrastructure, testing inefficiencies and collaboration issues.

AI needs a strong infrastructure as its foundation, including high-performing and scalable computing systems, high volume storage systems, and GPU architecture. The process of effectively developing, deploying, and monitoring models in production environments is time-consuming and many organizations simply do not know how to operationalize their data platforms at enterprise scale. Furthermore, the data that AI utilizes must be significantly scrubbed, but organizations have not invested properly in doing so, which limits the insights AI and predictive analytics can provide. Failure to invest in and establish a strong infrastructure is responsible for much of the estimated 90 percent of AI models that are never put into production.

How to do it right?

Organizations that understand their organizational mission, data and infrastructure, and ethical needs and articulate that in a robust AI strategy can hit the ground running. During design and development, organizations must leverage strategies like human-centered design to ensure end users' needs inform system design. Strong data strategies include standardized methods for labeling, validating, cleaning, and organizing data across an enterprise. Choosing an open source platform solution will yield crucial insights into the health and lineage of data and can remove organizational data siloes and allow for a better, enterprise-wide approach to data management. Finally, investment in the infrastructure (e.g., cloud, GPUs) needed to support AI solutions is a critical foundational step as computing power is essential to enabling AI.

Ultimately, spending the time upfront to organize, prioritize, and execute against mission, data and infrastructure, and ethical needs is the best way to position organizations for long-term success.

We have seen positive signs that the private sector is ready to embrace AI and Advanced Analyticsand in many cases already has. As both the public and private sector navigate expected challenge in this journey, we're hopeful as history shows us that technology transformation is more a question or when than if. And, AI has attracted many leaders both in technology and adjacent fields, creating a robust and necessary discussion about how we build and deploy AI.Additionally, it's encouraging to see that there are many in industry have a perspective of 'Don't Go It Alone,' developing important partnerships that bring all the pieces together. Booz Allen, for example, has been working to demystify AI for the public sector, working together to bring NVIDIA'sdeep learning trainingto the Federal sector. Together, we've trained people from more than 15 government organizations within just the last year.

Ultimately, we are excited about the AI-powered possibilities that lay ahead. AI has already plays an important role in combating cybercrime and it helped speed our global response to the COVID-19 pandemic. It is important that we remember, however, that AI is ultimately an enabler that will help humans tackle seemingly complex challenges.

See the rest here:

5 reasons AI isn't being adopted at your organization (and how to fix it) - ZDNet

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G7E5Ib@as<42is)/_Xni,[EP95liR2pBXn~wZRH}oEUi4XdF-EL'MDuxA b6fc,Pw(5L4'XXzZ!R"t+i)R2~($I,BK(0|_0FYC:"""LD%|^wF^rJd "d`0K1PvY`qz&aSXPqwCEFPVw/YN3

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China may match or beat America in AI - The Economist

Intelligent applications and devices can change the way welive and work.

Here are just a few that promise to change the way we live and work.

Google Now was Androids answer to Apples Siri, and Google Assistant is the search giants latest generation digital assistant thats available for an increasing number of recent phone models including Apples iPhones.

As well as being smarter than Now and able to have two-way conversations with users, Google Assistant is compatible with Google Lens, a new augmented reality (AR) app that will use the phones camera to provide information about whatever its focusing on anything from identifying flowers to displaying a restaurants menu.

Apples voice-activated digital assistant for iPhones, iPads and the Apple Watch was one of the first commonly used intelligent applications, but in some ways it has fallen behind competitors such as Google Assistant. Apple isnt taking that lying down, of course.

In iOS 11 the next version of the mobile operating system due out later in 2017 Siri will have a number of improvements, including on-device learning to deliver more personal experiences and offer suggestions based on personal usage of Safari, News, Mail, Messages and more, according to Apple.

Professional Development: CPA Q&A. Access a handpicked selection of resources each month and complete a short monthly assessment to earn CPD hours. Exclusively available to CPA Australia members.

Samsungs take on the digital assistant, Bixby, comes with the companys latest flagship phone, the Galaxy S8, and will eventually be rolled out to Samsung TVs and other products. Bixbys main app is like Google Now: it automatically provides information you need when you need it, such as a reminder to call someone back when you get to the office. It also powers Vision, an AR app similar to Google Lens. Bixby was released in South Korea in May; English voice commands are due to be added over the next few months.

Echo is a smart speaker that brings Amazons digital assistant, Alexa, into homes. Using voice commands, you can ask it to play music, read the news, place an order and much more. Like all these intelligent apps, Alexa learns and gets better the more you use it.

Google Home is a very similar device. The difference is Home lets you use your voice to command Googles Chromecast streaming device, while Amazon Echo only lets you connect with other Echo devices, such as the Echo Dot smart speakers. Unfortunately, neither the Echo nor Home are officially available in Australia yet.

Apple has announced that its smart speaker, HomePod, will be available in December 2017, including in Australia. While it has an intelligent assistant in this case Siri, as youd expect HomePod seems to be more focused on music than Amazon Echo and Google Home. HomePod will feature a seven-speaker array of tweeters and a 4-inch subwoofer, and Apple says it will automatically analyse the acoustics of a room and adjust the audio accordingly.

Machine learning is obviously a high priority for Google because the technology has even made its way into Google G Suite, the latest name for the search giants online application suite. For example, Explore in Docs, Slides, and Sheets are features that use machine learning to quickly find documents or information on the web, reformat presentations, perform calculations in spreadsheets and more.

Most recently, Google has added the ability to use natural language, which is technology that helps computers understand human speech, to quickly create charts in Sheets. This means you can ask Sheets to create a bar chart for total sales of this year and it will automatically build one for you.

Machine learning is also coming to accounting. Xero is trialling its first machine-learning tool that promises to speed up invoice preparation by suggesting account codes for invoices. Like other forms of machine learning, it improves over time, but Xero claims it can achieve 80 per cent accuracy after only four invoices. Meanwhile, Sage is working on a speech-based interface for its accounting system and is also beta-testing a chat bot called Pegg that will let users manage their accounts using natural language via Skype, Facebook Messenger or team messaging platform Slack.

Read next: 5 ways accountants have mastered AI

Read more from the original source:

AI: the new normal - INTHEBLACK

Healthcare costs are rapidly increasing. For companies that provide health insurance for their employees, theyve been getting hit with higher and higher premiums every year with no end in sight.

One Chicago-based startup experiencing explosive growth has been tackling this very problem. This company leverages artificial intelligence and chatbot technology to help employees navigate their health insurance and use less costly services. As a result, both the employee and employer end up saving money.

Justin Holland, CEO and co-founder of HealthJoy, has a strong grasp on how chatbots are going to change healthcare and save companies money in the process. I spoke with Holland to get his take on what CEOs need to know about their health benefits and how to contain costs.

Related:CanArtificial IntelligenceIdentify Pictures Better than Humans?

Whats the biggest problem with employer-sponsored health insurance? Why have costs gone up year after year faster than the rate of inflation?

One of the biggest issues for companies is that health insurance is kind of like giving your employees a credit card to go to a restaurant that doesnt have any prices. They are going to order whatever the waiter suggests to them that sounds good. Theyll order the steak and lobster, a bottle of wine and dessert. Employees have no connection to the actual cost of any of the medical services they are ordering. Several studies show that the majority of employees dont understand basic insurance terms needed to navigate insurance correctly. And its not their fault. The system is unnecessarily complex. Companies have finally started to realize that if they want to start lowering their healthcare costs, they need to start lowering their claims. The only way they are going to start doing that is by educating their employees and helping them to navigate the healthcare system. They need to provide advocates and other services that are always available to help.

Related:The Growth ofArtificial Intelligencein Ecommerce (Infographic)

Ive had an advocacy service previously that was just a phone number and I never used it. I actually forgot to use it all year and only remembered I had it when they changed my insurance plan and I saw the paperwork again. How is HealthJoy different?Is this where chatbots come in?

Phone-based advocacy services are great but youve identified their biggest problem: no one uses them. They are cheap to provide, so a lot of companies will bundle them in with their employee benefits packages, but they have zero ROI or utilization. Our chatbot JOY is the hub for a lot of different employee benefits including advocacy. JOYs main job is to route people to higher quality, less expensive care. She is fully supported by our concierge staff here in Chicago. They do things like call doctors offices to book appointments, verify network participation and much more. Our app is extremely easy to use and has been refined over the last three years to get the maximum engagement and utilization for our members.

Related:Why Tech Companies Are Pumping Money IntoArtificial Intelligence

Ive played around with your app. You offer a lot more than just an advocacy service. I see that you can also speak with a doctor in the app.

Yes, advocacy through JOY and our concierge team really is just the glue that binds our cost saving strategies. We also integrate telemedicine within the app so an employee can speak with a doctor 24/7 for free. This is another way we save companies money. We avoid those cases where someone needs to speak with a doctor in the middle of the night for a non-emergency and ends up at the emergency room or urgent care. Avoiding one trip to the emergency room can save thousands of dollars. Telemedicine has been around for a few years but, like advocacy, getting employees to use it has always been the big issue. Since we are the first stop for employee's healthcare needs, we can redirect them to telemedicine when it fits. We actually get over 50% of our telemedicine consults from when a member is trying to do something else. For example, they might be trying to verify if a dermatologist is within their insurance plan. Well ask them if they want to take a photo of an issue and have an instant consultation with one of our doctors. This is one of the reasons that employers are now seeing utilization rates that are sometimes 18X the industry standard. Redirecting all these consultations online is a huge savings to companies.

Related:4 WaysArtificial IntelligenceBoosts Workforce Productivity

What other services do you provide within the app?

We actually offer a lot of services and its constantly growing. Employers can even integrate their existing offerings as well. Healthcare is best delivered as a conversation, and thats why our AI-powered chatbot is perfect to service such a wide variety of offerings. The great thing is that its all delivered within an app that looks no more complex than Facebook Messenger or iMessage.

Right now we do medical bill reviews and prescription drug optimization. Well find the lowest prices for a procedure, help people with their health savings account and push wellness information. Our platform is like an operating system for healthcare engagement. The more we can engage with a company's employees for their healthcare needs, the more we can save both the employer and employees money.

Related:Artificial Intelligence- A Friend or Foe for Humans

It sounds like you're trying to build the Siri of healthcare, no?

In a way, yes. Basically, we are trying to help employers reduce their healthcare costs by providing their employees with an all-in-one mobile app that promotes smart healthcare decisions. JOY will proactively engage employees, connect them with our benefits concierge team and redirect to lower-cost care options like telemedicine. We integrate each client's benefits package and wellness programs to deliver a highly personalized experience that drives real ROI and improves workplace health.

So if a company wants to launch HealthJoy to their employees, do they need to just tell them to download your app?

We distribute HealthJoy to companies exclusively through benefits advisors, who are experts in developing plan designs and benefits strategies that work, both for employees and the bottom line. We always want HealthJoy to be integrated within a thoughtful strategy that leverages the expertise the benefits advisor provides, and we rely on them to upload current benefits and plan information.

Marsha is a Growth Marketing Expertbusiness advisor and speaker with specialism in international marketing.

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This Startup Is Lowering Companies Healthcare Costs With AI - Entrepreneur

A Parliamentary Standing Committee has sought details from the government on its strategic disinvestment plans for national carrier Air India.

The department-related Parliamentary Standing Committee on Transport, Tourism and Culture, chaired by Rajya Sabha Member of Parliament Mukul Roy, is set to meet the Central government officials on Friday.

To hear the views of the Ministry of Civil Aviation, Department of Investment and Public Asset Management (Ministry of Finance) and Air India on Disinvestment of Air India, the agenda of the meeting said.

The Cabinet Committee on Economic Affairs (CCEA), chaired by Prime Minister Narendra Modi, on June 28 gave its in-principle approval for the strategic disinvestment of Air India and its subsidiaries.

The CCEA also set up a group of ministers under Finance Minister Arun Jaitley to examine the modalities of the national carriers stake sale. The Ministerial group will decide upon the treatment of unsustainable debt of Air India, hiving off of certain assets to shell company, de-merger and strategic disinvestment of three profit-making subsidiaries, quantum of disinvestment and the universe of bidders.

Minister of State Civil Aviation Jayant Sinha told the Rajya Sabha on Tuesday that the decision to divest a stake in Air India was based on government think-tank NITI Aayogs recommendations in May this year.

In its recommendations, the Aayog had given the rationale for the disinvestment of Air India and has attributed the main reason as fragile finances of the company. AI has been incurring continuous losses and has huge accumulated losses, Mr. Sinha said in a written reply.

Further, NITI Aayog in its report on Air India says that further support to an unviable non-priority company in a matured and competitive aviation sector would not be the best use of scarce financial resources of the Government, Mr. Sinha added.

Mr. Sinha said in the Lok Sabha on Thursday that Air Indias market share on domestic routes has reduced from 17.9% in 2014-15 to 14.2% in 2016-17.

Air India has accumulated total debt of 48,876 crore till March 2017. The national carrier has been reporting continuous losses due to its high debt with its net loss at 3,728 crore in 2016-17 compared with 3,836 crore in 2015-16.

Hours after the Union Cabinet gave its nod to Air India's strategic disinvestment, Indias largest low-cost carrier IndiGo expressed interest in acquiring a stake in its airline business, mainly related to its international operations. Tata Sons were also reportedly in talks with the government seeking details on the national carriers strategic disinvestment.

Continued here:

AI sale plan details sought from Centre - The Hindu

Reinforcement learning algorithms may be the next best thing since sliced bread for engineers looking to improve chip placement.

Researchers from Google have created a new algorithm that has learned how to optimize the placement of the components in a computer chip, so as to make it more efficient and less power-hungry.

SEE ALSO: WILL AI AND GENERATIVE DESIGN STEAL OUR ENGINEERING JOBS?

Typically, engineers can spend up to 30 hours configuring a single floor plan of chip placement, or chip floor planning. This complicated 3D design problem requires the configuration of hundreds, or even thousands, of components across a number of layers in a constrained area. Engineers will manually design configurations to minimize the number of wires used between components as a proxy for efficiency.

Because this is time-consuming, these chips are designed to only last between two and five years. However, as machine-learning algorithms keep improving year upon year, a need for new chip architectures has also arisen.

Facing these challenges, Google researchers Anna Goldie and Azalia Mirhoseini, have looked into reinforcement learning. These types of algorithms use positive and negative feedback in order to learn new and complicated tasks. Thus, the algorithm is either "rewarded" or "punished" depending on how well it learns a task. Following this, it then creates tens to hundreds of thousands of new designs. Ultimately, it creates an optimal strategy on how to place these chip components.

After their tests, the researchers checked their designs with the electronic design automation software and discovered that their method's floor planning was much more effective than the ones human engineers designed. Moreover, the system was able to teach its human workers a new trick or two.

Progress in AI has been largely interlinked with progress is computer chip design. The researchers' hope is that their new algorithm will assist in speeding up the chip design process and pave the way for new and improved architectures, which would ultimately accelerate AI.

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Google Researchers Create AI-ception with an AI Chip That Speeds Up AI - Interesting Engineering