If you accept that business is always evolving, learning and changing then you won't be surprised by this forecast. Think ultimate velocity. Think the next wave of digital disruption. This makes mobile, big data and the cloud seem like old news. The competitive landscape of companies, markets and individuals just got very complex and interesting. Artificial intelligence, AI is the new competitive advantage. Our civilization is heading for a reality check.

We will need to make a call very soon. That is about how the AI Wars will play out. Do we want a Human-Centric Future, enabled by AI but not replaced by AI? This will be a central question in the debate over AI in work, society and business. We need to consider the future trends in AI that would challenge the Human-Centric Future.

AI maybe both our greatest competition and our greatest creation.

We have entered a new era--The AI Wars. Artificial intelligence, and the current computer programs that deliver various forms of machine learning, natural language processing, neural networks and cognitive computing is emerging fast as a competitive force in every industry, nation and market. The only question that matters is Are You Future Ready? How will you adapt, integrate into your business or career as you prepare for the AI Wars?

Amazon is using Alexa to compete against all of the other retailers on the planet and Google Home. Tesla's AI downloads updated geo-intelligence to compete against all the other car brands that don't update via the cloud. IBM's Watson is automating decision analysis that competes with clinics and hospitals not enabled by its cognitive computer. This is just the beginning of the AI Wars. Companies that are using AI to compete will shape the future of AI.

There are companies using AI for diagnosing disease, deciphering law, designing fashion, writing films, drafting music, reading taxes or figuring out if your a terrorist, fraudster or threat. AI is everywhere. If you are within sight of a videocamera, cell phone, city, driving in a car or traveling by transit, online or off, unless you are on Mars you are likely exposed to AI in real-time. You may not know this.

Here's a forecast--every job a human can do will be augmented by (increased intelligence assets) and possibly replaced by AI. Companies will use AI to outcompete other companies. Nations will use AI to compete against other nations. AI augmented humans will outcompete the Naturals--humans not augmented by AI.

We must prepare now for this extreme future possibility. AI is the ultimate competitor and collaborator of humans. AI is the game changer of the future that is coming sooner then we think. So smart AI is an investment every organization and nation needs to make now so we can shape the future of AI to become Human-Centric.

Now the challenge is how will we will redesign organizations, alliances, markets, work and careers in a world where AI is a partner, enabler, producer and yes, a competitor? We need to redesign our civilization to keep pace with the advancement of AI. Now I am not a dystopian. I believe we need to prepare smarter to meet these challenges but they are coming. No denial needed. Most of what AI will bring will be productive and positive. Some of the developments will pose difficulty, challenge and threat.

Artificial intelligence will be the most powerful future competitive force influencing every business, markets, security, creativity and every profession--from law, medicine, engineering to gaming and entertainment. Having AI that can deliver solutions, faster then, even more cost-effective then, with greater quality then humans is coming. This is the inevitable end game of digital transformation.

Geopolitical power will be shaped not just by economics, wealth and might but by AI. Thinking machines that can out think the competition mean a new world of geopolitical intelligence that may evolve beyond states, law, human knowledge and understanding. How do we figure out what we cannot understand? When AI writes its own rules, operating system and behaviors and we don't understand how will we realize that we have created a potential competitor not just a collaborator. The AI Wars are coming.

The ultimate digital disruption is coming. I am not advocating that AI will replace human jobs but rather that it could happen if we don't plan ahead--become Future Ready, redesign our world to anticipate this future. Companies will and are even today competing using AI. Predictive analytics and big data driven by AI is a competitive differentiator. Make sure you are in this game--shape this future.

Even if AI surpasses humans in a autonomous world of smart technology that is faster then humans, we should hold to a Human-Centric Future. We should be ready for this future as we are creating it now. I remain positive and suggest that the future is best served by humanity using AI to fix the grand challenges that face our world--hunger, security, water, disease, poverty and sustainability. We could use some help and I advocate for AI to be directed to help enable humans to fix the planet. Makes sense to this futurist.

This Blogger's Books and Other Items from...

Future Smart: Managing the Game-Changing Trends that Will Transform Your World

by James Canton

Future Smart: Managing the Game-Changing Trends that Will Transform Your World

by James Canton

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The Coming AI Wars - Huffington Post

Artificial intelligence, with the human touch, is building a new customer experience

Artificial Intelligence is the definitive technology of the 21st century. All businesses of every size, in every industry will be impacted by AI. In the age of the connected customer, every 1 in 5 U.S. adults are almost never offline, customer experience is the battleground for true differentiation. Today, every successful consumer application is powered by AI. Tomorrow, every successful business will be powered by AI. The line-of-business that is most likely to embrace AI first will be the customer service typically the most process oriented and technology savvy organization within most companies. But before we dig into AIs tremendous potential in transforming customer service, lets scope AIs market size and growth projections.

The Artificial Intelligence (AI) Market Size and Future Projections

Today, 38% of enterprises are already using artificial intelligence (AI), growing to 62% by 2018. Forrester is predicting a 300% increase in AI investments in 2017 compared to 2016 and IDC believes AI will be a $47 billion market by 2020. Forrester lists the top 10 AI technologies here:

Forrester

Gartner named Intelligence Applied AI and Advance Machine Learning, Intelligent Apps, and Intelligent Things as 3 of its top 10 strategic technology trends for 2017.

The disruptive power of AI will impact every business, in every industry. According to Gartner, by 2020, 20% of companies will dedicate workers to monitor and guide neural networks. Gartner advises CIOs to look at areas of the company that have large data sets but lack analytics. AI can provide augmented intelligence with respect to discovery, predictions, recommendations and automation at scale.

PWC named Artificial Intelligence (AI) as one of the eight essential technologies in business. Today, there are 1,652 artificial intelligence (AI) startups and private companies that have captured over $12.24 billion of funding.

Venture Scanner

The power of artificial intelligence is mass personalization and contextual intelligence at scale. According to Accentures 2017 Technology Vision Report, AI could double annual economic growth rates by 2035. Accenture also notes that AI is the new UI. AI is becoming the new user interface (UI), underpinning the way we transact and interact with systems. Seventy-nine percent of business leaders agree that AI will revolutionize the way they gain information from and interact with customers. As AI takes over more of the user experience, it grows beyond just an intelligent interface. With each customer interaction becoming more personalized, powerful, and natural, AI moves into an even more prominent position: your digital spokesperson, Accenture Technology Vision 2017

AI Implementation Realities in the Enterprise

Although Amazons Alexa, has evolved from 1,000 voice command interpretations (or skills) to now more than 10,000 skills in one year period, there is still a lot of AI progress to be made before machines can truly understand and guide next best actions.

Robots, AI will replace 7% of US jobs by 2025 Forrester. Here are the highlights of the report:

The future of work projections and AIs impact on jobs may appear aggressive and somewhat unrealistic. In order to better understand the realities of AI in business, it is important to define phases and prerequisite of AI deployments in large businesses.

The three key phases of enterprise AI roll out: data, algorithms, and workflows. The power of AI usefulness is a function of the quality and quantity of data. Algorithms will help deliver insights discovery, prediction, recommendation and automation of existing manual processes require strong, self-learning and adaptable algorithms. The final phase and the most challenging is the workflows. The constant iteration of analyzing data, researching and developing algorithms, and creating timely actions based on gleaned insights through robust workflows is the job of data scientists and line-of-business people experts. Workflows that guide customer engagements must not be automated to a point where businesses lose sight on the importance of the human touch, empathy and relationship building aimed at earning the right to be a trusted advisor and strategic business partner.

For most companies, the algorithms and workflow complexities will the use of augmented intelligence. This is especially true for complex customer relationship management workflows in B2B customer service functions. That said, there is exponential growth in AI innovation and advancements and companies cannot afford to tag AI as hype, only to find themselves significantly behind their competitors in 1-2 years. AI knowledge, planning and adoption must happen now.

The Role of AI in Customer Service

Today machines have the ability to interact with humans at a level that used to only seem possible in sci-fi movies. Amazon serves up personalized product recommendations, Facebook automatically tags photos and Google maps proactively reroutes you around traffic. AI is powering nearly every experience we have-- making it smarter, seamless and personalized-- and as a result our expectations as consumers are at an all-time high. The most indispensable consumer apps are powered by AI technologies, delivering real personalized value, in real-time. This seamless personalized, immediate and intelligent user experience will make its way to every business, across all industries. AI in business will create motion and flow-based solutions and services. In order for customer service leaders to stay relevant, they must think differently and educate their stakeholder about AI. AI allows companies to deliver these smarter, more personalized and predictive experiences that customers have come to expect, but the human touch is still table stakes for customer success. The most suitable line-of-business to start with AI? Customer Service.

According to Salesforce research, 92% percent of senior executives believe that customer experience is a key competitive differentiator and they view customer service as the primary vehicle for improving the customer experience. In order for customer service organizations to lead customer experience transformation, they must fully embrace, deploy and utilize AI technologies.

What does excellent customer service look like? According to research, excellent customer service is personalized, always on and real-time, consistent and omni-channel. To achieve customer service excellence, service organization must leverage AI to bolster their discovery, prediction, recommendation and automation engines.

Salesforce Research

In the age of the customer, contact channels are expanding rapidly and the amount of data created - both structured and unstructured means that service organizations are drowning in data, but starving for actionable insights.

Salesforce Research

Forrester identifies extended and enhanced self-service, powered by AI technologies as one of its top trends for customer service in 2017. Customer service will continue to invest in structured knowledge management and leverage communities to extend the reach of curated content. Service will become more ubiquitous, via speech interfaces, devices with embedded knowledge, and wearables for service technicians, said Kate Leggett. The second top trend is sustained customer conversations using natural language processing technology. Companies will continue to explore the power of intelligent agents to add conversational interfaces to static self-service content. They will anticipate needs by context, preferences, and prior queries and will deliver proactive alerts, relevant offers, or content, said Leggett.

Top service teams are 3.9X more likely than underperforming service organizations to say predictive intelligence will have transformational impact on their customer service by 2020. The common theme that I hear as I collaborate with business leaders is that AI biggest potential is to augment our ability to connect with customers and giving way to a smarter customer experience.

Salesforce

With projections of 6 billion smartphone users and over 50 billion connected devices by 2020, the next generation customer experience will be powered by artificial intelligence. A CRM platform powered by AI will analyze customer engagements and automatically predict sentiment and adjust customer journeys to ensure optimal user experience. The same logic applied to prediction marketing lead scores and sales opportunity conversions will be applied to customer services cases, optimizing time-to-resolution cycles and improved customers satisfaction and net promoter scores (NPS).

Service organizations can significantly improve the customer experience and bolster service delivery capacities using AI technologies. Service managers using AI can gain real-time insights across all customer contact channels with AI-powered analytics to increase team productivity and CSAT. By using smart data discovery, service managers can optimize agent availability, wait times and opportunities for proactive service delivery. Using machine learning, cases are automatically escalated and classified using sensitivity and domain expertise predictive analytics. AI powered chat bots can deliver knowledge using automated workflows. Field service professionals can use mobile apps powered by AI, delivering precision service based on access to CRM data that can deliver personalized services anywhere. AI powered field apps use algorithms to optimize scheduling and routing using complete CRM data.

Salesforce Research

The top trends for CRM in 2017 includes intelligence powering prescriptive advice, according to Forrester.

The advantage of a CRM platform powered by artificial intelligence goes far beyond just the services organization. At Salesforce, the artificial intelligence technology, called Einstein, is infused across all the Salesforce clouds, giving over 150,000 companies that use Salesforce to seamlessly access AI capabilities across their sales, services, marketing, IT and community organizations. Marketers using AI have seen an average 25% lift in click through and opens. Sales professionals using AI predictive lead scoring have a 300% increase in lead to opportunity conversions. Commerce teams using AI have 7-15% increase in revenue per site visitor.

Artificial Intelligence is the definitive technology for the 21st century, and companies that use AI as augmented intelligence to make more informed and faster decisions will win the age of the customer where personalization, immediacy and intelligence are the new currencies of growing businesses. But to sustain growth and earn customers trust, businesses have to use common sense, care more and be cautious of over-automating. Businesses must practice empathy, inside and outside of the company, and deliver on their promises.

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AI-Powered Customer Service Needs The Human Touch - Huffington Post

Driverless AI is the latest product from H2O.ai aimed at lowering the barrier to making data science work in a corporate context. The tool assists non-technical employees with preparing data, calibrating parameters and determining the optimal algorithms for tackling specific business problems with machine learning.

At the research level, machine learning problems are complex and unpredictable combining GANs and reinforcement learning in a never before seen use case takes finesse. But the reality is that a lot of corporates today use machine learning for relatively predictable problems evaluating default rates with a support vector machine, for example.

But even these relatively straightforward problems are tough for non-technical employees to wrap their heads around. Companies are increasingly working data science into non-traditional sales and HR processes, attempting to train their way to costly innovation.

All of H2O.ais products help to make AI more accessible, but Driverless AI takes things a step further by physically automating many of the tough decisions that need to be made when preparing a model. Driverless AI automates feature engineering, the process by which key variables are selected to build a model.

H2O built Driverless AI with popular use cases built-in, but it cant solve every machine learning problem. Ideally it can find and tune enough standard models to automate at least part of the long tail.

The company alluded to todays release back in January when it launched Deep Water, a platform allowing its customers to take advantage of deep learning and GPUs.

Were still in the very early days of machine learning automation. Google CEOSundar Pichai generated a lot of buzz at this years I/O conference when he provided details on the companys efforts to create an AI tool that could automatically select the best model and characteristics to solve a machine learning problem with trial, error and a ton of compute.

Driverless AI is an early step in the journey of democratizing and abstracting AI for non-technical users. You can download the tool and start experimenting here.

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H2O.ai's Driverless AI automates machine learning for businesses - TechCrunch

Since 2019, government-sponsored initiatives around AI have proliferated across Asia Pacific. Such initiatives include the setting up of cross-domain AI ethics councils, guidelines and frameworks for the responsible use of AI, and other initiatives such as financial and technology support. The majority of these initiatives builds on the country's respective data privacy and protection acts. This is a clear sign that governments see the need to expand existing regulations when it comes to leveraging AI as a key driver for digital economies. All initiatives to date are voluntary in nature, but there are indications already that existing data privacy and protection laws will be updated and expanded to include AI. To anticipate this, data and technology governance initiatives must evolve now.

Traditionally, data governance and the governance of tech associated with data has focused on topics such as master data management, data quality, and data retention -- all primarily operational. With the rise of privacy laws and data protection acts such as the General Data Protection Regulation (GDPR) in the EU and the Personal Data Protection Act (PDPA) in Singapore, the scope of data governance has been expanded to include data privacy, personal data protection, and data sovereignty. This has shifted data governance out of the operational corner and into the spotlight of regulatory compliance and enforceable laws.

With AI being ready for prime time -- that means large-scale production deployments -- data and technology governance must step up again and include data and AI ethics and AI risk management.

Like cybersecurity risk before it, regulatory initiatives and consumer demand join forces to drive AI risk management to the top of the corporate agenda. Evaluate your data and technology governance initiatives now to identify gaps and maturity challenges when it comes to the responsible use of data and AI. Prepare for AI risk management to follow cybersecurity risk to the boardroom and kick off corporate collaborations and cross-functional initiatives, including governance, risk, corporate social responsibility, and ethics. Ultimately, understand how you can build trust with your customers, partners, and employees into your responsible use of data and AI -- and turn this trust into your competitive advantage!

For more business and technology trends critical for the year head, download Forrester's 2021Asia PacificPredictions Guidehere.

This post was written by Principal Analyst Achim Granzen, and it originally appearedhere.

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AI drives the evolution of technology and data governance - ZDNet

FRANCE DEPLOYS AI-BASED CANCER DETECTION

AI-based cancer diagnostics company, Ibex Medical Analytics, and network of private pathology labs in France, Medipath, have announced the deployment of an AI-powered platform for cancer detection in pathology in France.

This coincides with a global decline in the number of pathologists and increased workloads.

The process of lab cancer diagnosis through a microscope is manual and thus prone to human error. Ibex offers a clinical grade, field proven AI-based solution that helps pathologists meet these challenges.

Medipath has completed deployment of Ibexs Galen Prostrate as part of its routine clinical practice.

With Ibexs CE-marked solution, an AI algorithm analyses prostate biopsies and raises alerts when discrepancies with the pathologists initial diagnosis are identified.

IRELAND DONATES CONTACT TRACING APP TO LINUX FOUNDATION

Irelands Health Service Executive (HSE) announced that is donating the code for COVID Tracker app as open source to the not-for-profit Linux Foundation.

This will enable jurisdictions worldwide to quickly build and deploy their own contact tracing apps.

The donated app has been named COVID Green.

NearForm will play a role as part of the Technical Steering Committee in managing the development and direction of COVID Green in the Linux Foundation.

The code is also being used in the app for Gibraltar and the upcoming apps for Northern Ireland, other jurisdictions in EMEA and some US states.

UKS TEST AND TRACE SYSTEM FAILING TO CONTACT THOUSANDS

The governments test and trace system is failing to contact thousands of people in areas with the highest infection rates in England, according to data obtained by the Guardian.

The data shows that in areas with the highest infection rates in England, the proportion of close contacts of infected being reached is far below 80%.

The governments Scientific Advisory Group for Emergencies (SAGE), agreed that at least 80% of contacts would need to isolate for an effective test and trace system.

Forty-seven per cent of at-risk people were contacted by test and trace in Luton, which has the sixth highest infection rate in England.

A Department ofHealth and Social Care spokesperson said: The service is working closely with local authorities across England to help manage local outbreaks. High quality data is critical to providing good public services and weve been providing increasingly detailed data to local directors of public health, helping them tackle local outbreaks and control this virus.

SLOVENIA BEGINS WORK ON CONTACT TRACING APP

This week, the Slovenian Ministry of Public Administration launched a tender for the creation of a contact tracingapp with a budget of 40,000.

The winner of the bid wasRSTEAM, one of six applicants of which only four met the budgetary preconditions set out by authorities.

The app will be based on Germanys Corona-Warn-App that has been in use for over a month.

The app that RSTEAM is set to develop is expected to be ready for launch on 1 August.

THE SHURI NETWORK'S FIRST ANNIVERSARY

The first network for black and ethnic minority women in digital health roles, the Shuri Network, celebrated its first anniversary this week.

One year ago, at the time of its launch, the network had 60 members but it has now grown to around 650 members.

The network was launched at Digital Health Summers Schools in 2019 to help increase visibility of black and minority ethnic women (BME) in NHS technology roles.

It has since worked to create spaces that give women of colour a platform to share experiences and propel forward the advancement and contributions of BME women in digital innovation.

The network has recently launched the Shuri Fellowship which will provide future leaders training opportunities to enhance their career. They have also partneredup with the Faculty of Informatics (FCI) to supplyShuri FCI bursaries for 15 members to cover the cost of their FCI membership.

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Roundup: UK's test and trace failing to contact thousands, France deploys AI-based cancer detection and more briefs - Healthcare IT News

Facebook has started deploying its artificial intelligence capabilities to help combat terrorists' use of its service.

Company officials said in a blog post Thursday that Facebook will use AI in conjunction with human reviewers to find and remove "terrorist content" immediately, before other users see it. Such technology is already used to block child pornography from Facebook and other services such as YouTube, but Facebook had been reluctant about applying it to other potentially less clear-cut uses.

In most cases, Facebook only removes objectionable material if users first report it.

Facebook and other internet companies face growing government pressure to identify and prevent the spread of terrorist propaganda and recruiting messages on their services. Earlier this month, British Prime Minister Theresa May called on governments to form international agreements to prevent the spread of extremism online. Some proposed measures would hold companies legally accountable for the material posted on their sites.

The Facebook post by Monika Bickert, director of global policy management, and Brian Fishman, counterterrorism policy manager did not specifically mention May's calls. But it acknowledged that "in the wake of recent terror attacks, people have questioned the role of tech companies in fighting terrorism online."

"We want to answer those questions head on. We agree with those who say that social media should not be a place where terrorists have a voice," they wrote.

Among the AI techniques used in this effort are image matching, which compares photos and videos people upload to Facebook to "known" terrorism images or video. Matches generally mean that either that Facebook had previously removed that material, or that it had ended up in a database of such images that Facebook shares with Microsoft, Twitter and YouTube.

Facebook is also developing "text-based signals" from previously removed posts that praised or supported terrorist organizations. It will feed those signals into a machine-learning system, over time, will learn how to detect similar posts.

Bickert and Fishman said that when Facebook receives reports of potential "terrorism posts," it reviews those reports urgently. In addition, it says that in the rare cases when it uncovers evidence of imminent harm, it promptly informs authorities.

But AI is just part of the process. The technology is not yet at the point where it can understand nuances of language and context, so humans are still in the loop.

Facebook says it employs more than 150 people who are "exclusively or primarily focused on countering terrorism as their core responsibility." This includes academic experts on counterterrorism, former prosecutors, former law enforcement agents and analysts and engineers, according to the blog post.

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Facebook deploys AI to fight terrorism on its network - ABC News - ABC News

Five Trends Business-Oriented AI Will Inspire Forbes It might seem as though enterprise AI players are the most qualified to provide enterprise solutions, but AI's move into the day-to-day will likely be fueled by startups solving unaddressed legacy problems. According to Brad Power at Harvard Business ...

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Five Trends Business-Oriented AI Will Inspire - Forbes

If ever there were an industry that could reap the benefits of AI, it is healthcare. The adoption of this technology to actually make medicine better is obvious. However, with this adoption comes a slew of ethical issues.

Lets start with some numbers: In 2018, the US spent $3.65 trillion on healthcare. That works out to $11,121 per capita, a 4.4% increase over 2017. In addition:

The per capita spend in western economies, other than Switzerland, which was about 80%, was 50% or less. The worse news is that the US has slipped to 36th in the world in quality of healthcare. (The above data is from Centers for Medicare & Medicaid Services and CIA World FactBook.)

Another lesser-known statistic is the magnitude of iatrogenic disease. From Wikipedia: an iatrogenic disorder occurs when the deleterious effects of the therapeutic or diagnostic regimen causes pathology independent of the condition for which the regimen is advised.

In other words, they are harmed by medical practice. According to a Johns Hopkins study, 251,454 deaths stemmed from a medical error - making it the third leading cause of death in the US, just behind cancer and heart disease.

All industries are facing the problem of which areas to apply AI. In an article in Healthcare IT News, some advice for the healthcare industry was: while AI may have the potential to discover new treatment methods, the report finds strongly entrenched ways of working in the healthcare industry that are resistant to change. The authors warn that simply adding AI applications to a fragmented system will not create sustainable change. Good advice for any industry.

Writing for Nature partner journal Digital Medicine, Trishan Panch, Heather Mattie and Leo Anthony Celi outline the obstacles healthcare faces in implementing AI solutions:

Data is balkanized along organizational boundaries, severely constraining the ability to provide services to patients across a care continuum within one organization or across organizations However, the inconvenient truth is that at present the algorithms that feature prominently in research literature are in fact not, for the most part, executable at the frontlines of clinical practiceAI innovations by themselves do not re-engineer the incentives that support existing ways of workingmost healthcare organizations lack the data infrastructure required to collect the data needed to optimally train algorithms to (a) fit the local population and/or the local practice patterns.

What industry isnt facing the same obstacles? Eric Topol is a cardiologist and geneticist, Executive Vice-President of Scripps Research, founder of a new medical school. His current book, just out is, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. His previous books are The Creative Destruction of Medicine (a take-off on Schumpeter's 'Creative Destruction in Economics,'" and "The Patient Will See You Now." Dr. Topol sees a more hopeful future for AI, but also cautions about its drawbacks and impediments.

The common thread running through Topols books is that medicine is a mess and technology will save it. I admire Topol for taking a stand on the state of medicine, but his breathless enthusiasm for technology overlooks how difficult it is to effect change in the $3+ trillion industry.

As examples of where AI shows promise for medicine, Topol explains that: Machine learning of mammography images from more than 1,000 patients, coupled with biopsy results indicating risk of cancer, showed that more than 30 percent of breast surgeries could be avoided. However, will doctors comply? Eye disease and radiology are two medical areas that are getting priority with lots of research and deep learning algorithmic development. The problem is not many physicians are practicing deep medicine, but rather, those doctors can be overconfident, condescending, arrogant, or simply not caring.

The industry is moving ahead with AI drug discovery, AI mental health (Instagram filters apparently say much about ones mental state), but Topol points out that there are the deep liabilities AI brings. Too commonly we ascribe the capability of machines to 'read' scans or slides," Topol writes, when they really cant read. Machines lack of understanding cannot be emphasized enough. Recognition is not understanding; there is zero context.

A rather horrifying example of the deep liabilities is the Dying Algorithm, a digital neural network 18 layers thick based on the electronic health records of nearly 160,000 people. It was, he writes, able to predict the time until death on a test population of 40,000 patient records with remarkable accuracy. Google and a trio of medical centers are now working with 47 billion data points to predict whether a patient would die, length of hospital stay, unexpected hospital admission, and final discharge diagnoses.

This is a perfect example of something that could be extremely useful, but is entirely, thoroughly unethical.

With all of the AI-driven scans and surgical assistants et al. that are mildly interesting, one he points out that is actually available now is speech recognition and transcription, so doctors can actually talk to their patients instead of typing in their EHR and not even making eye contact.This isn't super-science like robots doing brain surgery or models predicting the moment of my death. The technology behind NLP is already viable and organizations might want to find applications for it before looking for the "game-changer."

Topol confines himself mostly to doctors and hospitals, but there is a multitude of opportunities in healthcare for AI. In just about every customer-facing business, augmented intelligence is a current and promising approach. For Topol, There are about 10,000 human diseases, and there's not a doctor who could recall any significant fraction of them. If doctors can't remember a possible diagnosis when making up a differential, then they will diagnose according to the possibilities that are mentally 'available' to them, and an error can result." He is critical about IBMs Watson (as am I). He says, Watson does ingest abstracts, but it doesnt transform all that data into a structured database that would be useful to a working doctor.

Lesson there: ask an expert before you believe the hype.

I like Topol. I met him a few years ago at a medical device conference. He was talking about mobility, not yet AI. He took out a card, put it in his jacket pocket, held up an iPhone and showed on the big screen for the audience, live, his EKG and other vitals (which were impressive). Then he explained that a doctor in Europe was looking at this at the same time we were.

There were some ooh's and ah's, but in retrospect, I wonder what happened to that data, and the data from perhaps hundreds of thousands of other people who would use the device. Who owns that data? Also, what it hundreds of thousands of data streaming EKGs, similar to these, got sold and resold to another company who married it to the mountain of unregulated personal data to ultimately deny you credit, housing, education using models you will never see?

This is my point about AI for healthcare (or anything). There are two storylines to consider: the usefulness of the application - and the ultimate effect, often unintended, on people.

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To understand AI advancements in health care, there are two storylines we must follow - Diginomica

FreeAgent, the cloud-accounting software specialist, has released the results of a new survey that underlines just how much automation can do for your work/life balance and business profits.

Key findings reveal that nearly 1 in 2 accountants (49%) believe an automated workflow will lead to a reduction in stress and/or boredom from dealing with data-entry tasks. A similar number (48%) believe it will help provide a better work/life balance.

The new research also highlights that 81% of accountants think they can save up to 2 hours a day (10 hours a week) by using Artificial Intelligence (AI) to automate simple tasks, unlocking up to 68,163 of additional revenue annually.

Nearly 1 in 4 (24%) Scottish accountants believe they could save a sizeable 3-4 hours a day through automation, potentially unlocking up to 119,285.25 of additional revenue annually.

When asked to predict how quickly the industry will embrace technology to automate some or all accounting tasks, 59% of the 200 respondents think only some tasks will be automated within one year, 72% think some tasks will be automated within five years and 59% think the majority of tasks will be automated within 10 years.

Younger people have more faith in technology, with 77% of 18-35 year olds stating that some accounting tasks will be automated within five years, compared to 57% of over 55s. In contrast, nearly 1 in 10 (9%) of those aged 55+ believe no automation is coming to the accounting industry within 10 years, compared to one in 50 (2%) of 18-35 year olds.

The research also suggests that the Welsh are the most skeptical of any imminent change technology will bring to the sector, with just 33% believing some accounting tasks will be automated in one years time, compared to 72% of those based in the South East.

Accountants see artificial intelligence as most useful for accurate auto-reconciling of data in client accounts (50%), preventing clients entering incorrect information (45%), and dealing with HMRC (44%) with 90% of those in larger firms (of over 300 people) being interested in using AI, compared to 76% of those in of firms up to 50 people

Again, age seems to have an impact on willingness to embrace technology, with 40% of over 55s say they are not interested in using AI in their practice, compared to just 14% of younger colleagues.

Unsurprisingly, the majority of accountants across the board (61%) believe that technologys biggest impact in a firm will be around increasing the automation of data. However, many accountants believe other aspects of the industry will be impacted by technology:

Over one in three (37%) think technology will lead to less face-to-face time spent with clients. Meanwhile, male accountants are twice as likely to believe technology will provide wider accessibility to challenger banks and new finance providers (42% vs 21%). Nearly 1 in 2 accountants (49%) believe automation will lead to a reduction in stress and/or boredom from dealing with data-entry tasks, and a similar number (48%) believe it will help provide a better work/life balance.

However, those working in the capital are clearly hoping technology will provide some relief from the day to day grind, with 68% of London-based accountants believing automation will help with work/life balance, compared to just 9% of those based in the East of England. Twice as many women (43%) as men (20%) believe automation will provide opportunities for growth.

Ed Molyneux, co-founder and CEO at FreeAgent said, When small simple tasks require paperwork and endless data-entry, they are the things that often get left to the end of the day or week. Not only does this take up a significant amount of time when you eventually get around to them, but it can lead to unnecessary levels of stress and boredom.

I am therefore not surprised that almost half the accountants we surveyed believe that automation will help provide a better work/life balance and reduce stress. Essentially automation gives people back time, which is not only beneficial to them but can save a company a huge amount in hours for just one employee over a year.

By simply eliminating mundane, complicated processes around simple tasks, automation can bring explosive change that truly has the potential to revolutionize accountancy as a profession. In our survey, we see that accountants - and women, in particular - believe automation can open the door to opportunities for growth in business and create the chance for them to excel.

With less time spent on admin and logging data, accountants then have time to focus on other aspects of the job including more consultative work, which will also bring significant benefits to their clients.

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AI will transform our workflow and boost profits according to FreeAgent survey - TechRadar

Researchers from theUSC ViterbiSchool of EngineeringsInformation Sciences Institute(ISI) have partnered withPurdue Universityto take part in theDefense Advanced Research Projects Agency(DARPA)-funded program that seeks to develop the science that will allow AI systems to adapt to novelty, or new conditions that havent been seen before.

Take an AI that has been trained to play a standard game of Monopoly. What if you change the rules so that you can buy houses and hotels without first getting a monopoly? What if the game is set to end after 100 turns instead of waiting for bankruptcies? These are both novelties which would affect the optimal strategy to win.

And yet, as Mayank Kejriwal, the primary investigator on the project and a USC Viterbi research assistant professor, added, even today the most advanced AIs are ill-equipped to deal with this sort of novelty.

Even though there have been lots of advancements in AI, they are very task specific, Kejriwal said. The moment you introduce changes that the AI is not specifically equipped to handle, you have to go back and retrain the program. There is no general AI, something that can adapt to novel situations. We are really in uncharted waters because there is no science of novelty.

Thats the significance of this project, he added. Its not just about improving some specific AI module. By developing a science of novelty, we are laying the foundation for future generations of AI.

TheScience of Artificial Intelligence and Learning for Open-world Novelty(SAIL-ON) program, or SAIL-ON program began in November of 2019 and will continue until 2023. At the programs end, theDepartment of Defensehopes to use the research in a range of applications, from autonomous disaster-relief robots to self-driving military vehicles. The USC and Purdue collaborative team has been allocated $1.2 million from DARPA, and will likely receive more as the program goes on.

In some respects, AI has already surpassed human capabilities. Kejriwal citedAlphaZeroas an example a computer program that uses machine learning to play board games such as chess and Go, can now beat even the most advanced human players.

Unfortunately, because of an inability to handle novelty, most successful applications of AI such as AlphaZero are limited to tasks with fixed rules and objectives.

If we want AI systems to operate successfully in real-world environments, we need them to handle things they havent seen before, Kejriwal added; the real world is full of new situations.

COVID-19 is a perfect example of a novelty, Kejriwal said. Its not like we are trained to deal with this, but we figured it out and adapted. An AI would not have known what to do.

As an example, he spoke about an AI security system whose purpose was to protect an online retailer from different types of cyber-attacks. When the pandemic caused people to panic-buy toilet paper from the retailer, the AI saw more such requests than ever before. Not understanding the influence of the pandemic, the system assumed it was under attack and blocked all of the valid requests. Faced with this novel situation, the AI was unable to adapt.

There are infinitely many possibilities in a real-world environment, Kejriwal said, which means theres no way an AI can anticipate everything that might happen. Short of anticipating every single possibility, how do you actually learn to deal with novelty in the same way that a human does? he asked. In this project, we want to establish an entire paradigm for doing this, which doesnt exist currently.

While the program aims to develop general solutions for handling novelty across many fields, each group chose specific domains for testing. Researchers at ISI are working in the domain of board games, specifically Monopoly, while their counterparts at Purdue focus on ride-sharing.

In the context of Monopoly, like the real world, there are infinitely many ways to introduce novelty.

In addition to the possible rule changes mentioned previously, Kejriwal explained that you could add more dice, have different paths to choose from, alter the objective of the game, or even introduce incentives for teamwork.

The AI has to adapt to all of this, and it doesnt know beforehand what types of novelties can happen, he said.

Similarly, for an AI system that governs a ride-sharing app, there are so many possible real-time changes that theres no way to account for them all individually. Vaneet Aggarwal, an associate professor at Purdue and one of the project leaders, talked about the importance of adaptability for AI in this field.

We want the algorithms to be scalable to different things that happen around us, he said. It should adapt to different countries, different cities, different rules, as well as any unexpected events like road blockages.

Aggarwal added that the underlying science of novelty developed in the project would be useful for far more than just ride-sharing or game-playing. It would be applicable in any place where decision-making has to happen under uncertain conditions, he said.

Link:

Want to Teach An AI Novelty? First, Teach It Monopoly. Then Throw Out the Rules. - ScienceBlog.com

Watch all the Transform 2020 sessions on-demand right here.

When considering transformational ways to use computer vision on the edge in devices like robots, drones, cameras, and other devices, Booz Allen Hamilton VP Josh Sullivan advises caution, urging people to take security seriously on whats become a whole new attack vector.

For me, deploying an AI model in your IT environment is an entirely new attack vector. Ive seen a model working correctly that can identify tanks and other military equipment be fooled into seeing a school bus because someone sent poisoned data into the model, he said.

Failure to keep models secure can lead to adversarial machine learning attacks to make malicious code appear as benign or a range of other bad outcomes. Sullivan was joined in conversation at VentureBeats Transform 2020 conference by Nvidia VP of federal initiatives Anthony Robbins, Intel IoT VP Stacey Shulman, and Joint AI Center acting director Nand Mulchandani. Advances in encrypted models or use of federated learning may be required so that models are not as valuable if left in a war zone or discovered by a combatant. Booz Allen Hamilton signed a $800 million contract with the Pentagons Joint AI Center last month.

I think for the conversations were having like about facial recognition and law enforcement and privacy are crucial ones for us to have as a nation, but I also dont want to ignore the equally profound conversation around adversarial machine learning and the ability to hack AI models with poisoned data in attempts to influence their predictions, Sullivan said. Were going to keep relying more and more on these predictions and youre going to create an outsized impact that becomes possible by hijacking just a few bits of training data to change the entire outcome. I dont want to bolt the front door and leave the entire back door popped open.

The Joint AI Center was created by the Department of Defense in 2018 to lead Pentagon AI efforts. The initiative was led by Air Force Lt. Gen. Jack Shanahan until he retired and was replaced by Mulchandani, a civilian who said he sold four startups and spent 20 years in Silicon Valley before coming to the Pentagon one year ago. Mulchandani told VentureBeat in an interview last month how JAIC is evolving to adopt a culture and workflow more in line with how Silicon Valley treats software sprints and investment.

In a press conference at the Pentagon last week, Mulchandani talked about a global war for AI talent and said JAICs first lethal application of AI, what he describes as JAICs flagship product for joint warfighting operations, will be a tactical edge device.

Returning to the subject of tactical edge AI today, Mulchandani said advances in batteries, chip sets, algorithms, and other areas will be necessary to make progress in deploying AI at the edge. We are really relying on the private industry and breakthroughs and the technology platforms that private industry will enable that we can consume but also customize and deploy for our own use case, so its been a fascinating move, he said. That entire end to end process is an incredibly complicated one and is going to be. I think if somebody is thinking about starting a company or are partners on this webcast if theyre investing, which they are this is really where I think the game is going to get played out from an exciting standpoint in the next gen of technology here.

Each participant in the panel conversation has active contracts with the Department of Defense. Interest in government contracts may increase for some businesses as COVID-19 has introduced more volatility to the economy.

In addition to Mulchandanis appointment to acting director last month, other recent high-profile moves between the Pentagon and Silicon Valley include current White House CTO Michael Kratsios. He was chief of staff to Palantir founder and investor Peter Thiel at one time, but on Monday news emerged that he has been appointed Acting Under Secretary of Defense for Research and Engineering, according to his LinkedIn page.And going the other way, Josh Marcuse was executive director of the Defense Innovation Board in the office of the Secretary of Defense, but in March became head of strategy and innovation in Googles public sector division vying for government contracts.

Connections between Silicon Valley and the Pentagon appear to be increasing. Interaction has grown in part through the activity of groups like the Joint AI Center and the Defense Innovation Unit, a group created in 2015 with offices in Palo Alto.

For example, Google employees spoke out against the companys involvement in Project Maven, an initial JAIC project. A few months after the Maven contract came to light, Google adopted AI principles, but in a conversation with former JAIC director Air Force Lt. Gen. Jack Shanahan last fall, Google VP of global affairs Kent Walker reaffirmed the companys commitment to competing for government contracts.

The Defense Innovation Board is led by former Google CEO Eric Schmidt, while the National Security Council on AI, a group advising Congress on AI policy and investments, includes leaders from companies like AWS, Google Cloud, and Microsoft. Each of the groups advocates for more partnership between industry, academia, and government. Analysis by Tech Inquiry earlier this month found extensive government contracts between tech companies and the military.

Nvidias Anthony Robbins called public-private partnerships a giant team sport and identified opportunities for startups in cybersecurity, robotics, and other services. Running and retraining models at the edge is going to define the next decade, he said.

The rest is here:

Silicon Valley execs and Pentagon AI chief talk AI at the edge - VentureBeat

Emulators speed up simulations, such as this NASA aerosol model that shows soot from fires in Australia.

By Matthew HutsonFeb. 12, 2020 , 2:35 PM

Modeling immensely complex natural phenomena such as how subatomic particles interact or how atmospheric haze affects climate can take many hours on even the fastest supercomputers. Emulators, algorithms that quickly approximate these detailed simulations, offer a shortcut. Now, work posted online shows how artificial intelligence (AI) can easily produce accurate emulators that can accelerate simulations across all of science by billions of times.

This is a big deal, says Donald Lucas, who runs climate simulations at Lawrence Livermore National Laboratory and was not involved in the work. He says the new system automatically creates emulators that work better and faster than those his team designs and trains, usually by hand. The new emulators could be used to improve the models they mimic and help scientists make the best of their time at experimental facilities. If the work stands up to peer review, Lucas says, It would change things in a big way.

A typical computer simulation might calculate, at each time step, how physical forces affect atoms, clouds, galaxieswhatever is being modeled. Emulators, based on a form of AI called machine learning, skip the laborious reproduction of nature. Fed with the inputs and outputs of the full simulation, emulators look for patterns and learn to guess what the simulation would do with new inputs. But creating training data for them requires running the full simulation many timesthe very thing the emulator is meant to avoid.

The new emulators are based on neural networksmachine learning systems inspired by the brains wiringand need far less training. Neural networks consist of simple computing elements that link into circuitries particular for different tasks. Normally the connection strengths evolve through training. But with a technique called neural architecture search, the most data-efficient wiring pattern for a given task can be identified.

The technique, called Deep Emulator Network Search (DENSE), relies on a general neural architecture search co-developed by Melody Guan, a computer scientist at Stanford University. It randomly inserts layers of computation between the networks input and output, and tests and trains the resulting wiring with the limited data. If an added layer enhances performance, its more likely to be included in future variations. Repeating the process improves the emulator. Guan says its exciting to see her work used toward scientific discovery. Muhammad Kasim, a physicist at the University of Oxford who led the study, which was posted on the preprint server arXiv in January, says his team built on Guans work because it balanced accuracy and efficiency.

The researchers used DENSE to develop emulators for 10 simulationsin physics, astronomy, geology, and climate science. One simulation, for example, models the way soot and other atmospheric aerosols reflect and absorb sunlight, affecting the global climate. It can take a thousand of computer-hours to run, so Duncan Watson-Parris, an atmospheric physicist at Oxford and study co-author, sometimes uses a machine learning emulator. But, he says, its tricky to set up, and it cant produce high-resolution outputs, no matter how many data you give it.

The emulators that DENSE created, in contrast, excelled despite the lack of data. When they were turbocharged with specialized graphical processing chips, they were between about 100,000 and 2 billion times faster than their simulations. That speedup isnt unusual for an emulator, but these were highly accurate: In one comparison, an astronomy emulators results were more than 99.9% identical to the results of the full simulation, and across the 10 simulations the neural network emulators were far better than conventional ones. Kasim says he thought DENSE would need tens of thousands of training examples per simulation to achieve these levels of accuracy. In most cases, it used a few thousand, and in the aerosol case only a few dozen.

Its a really cool result, said Laurence Perreault-Levasseur, an astrophysicist at the University of Montreal who simulates galaxies whose light has been lensed by the gravity of other galaxies. Its very impressive that this same methodology can be applied for these different problems, and that they can manage to train it with so few examples.

Lucas says the DENSE emulators, on top of being fast and accurate, have another powerful application. They can solve inverse problemsusing the emulator to identify the best model parameters for correctly predicting outputs. These parameters could then be used to improve full simulations.

Kasim says DENSE could even enable researchers to interpret data on the fly. His team studies the behavior of plasma pushed to extreme conditions by a giant x-ray laser at Stanford, where time is precious. Analyzing their data in real timemodeling, for instance, a plasmas temperature and densityis impossible, because the needed simulations can take days to run, longer than the time the researchers have on the laser. But a DENSE emulator could interpret the data fast enough to modify the experiment, he says. Hopefully in the future we can do on-the-spot analysis.

Read the rest here:

From models of galaxies to atoms, simple AI shortcuts speed up simulations by billions of times - Science Magazine

Executive Summary

For all we hear about the wonders of AI, one critical factor is often left out: How do you actually integrate artificial intelligence into your business? The answer, the authors argue, is AI Operations (or AIOps for short). At its most basic, AIOps boils down to having not just the right hardware and software but also the right team: developers and engineers with the skills and knowledge to integrate AI into existing company processes and systems. A good AIOps team will help a business build a robust production environment that is dependable, flexible, and scalable. This team can either be in-house, which will cost more up front, but give businesses more control, or contracted out, which will lower overheard, as well as administrative costs, but will likely meaning giving up the option of running a proprietary system, as well as some control. But, if youre considering integrating AI into your business, AIOps can help you avoid costly failures and keep the system running smoothly.

Here is a common story of how companies trying to adopt AI fail. They work closely with a promising technology vendor. They invest the time, money, and effort necessary to achieve resounding success with their proof of concept and demonstrate how the use of artificial intelligence will improve their business. Then everything comes to a screeching halt the company finds themselves stuck, at a dead end, with their outstanding proof of concept mothballed and their teams frustrated.

What explains the disappointing end? Well, its hard in fact, very hard to integrate AI models into a companys overall technology architecture. Doing so requires properly embedding the new technology into the larger IT systems and infrastructure a top-notch AI wont do you any good if you cant connect it to your existing systems. But while companies pour time and resources into thinking about the AI models themselves, they often do so while failing to consider how to make it actually work with the systems they have.

The missing component here is AI Operations or AIOps for short. It is a practice involving building, integrating, testing, releasing, deploying, and managing the system to turn the results from AI models into desired insights of the end-users. At its most basic, AIOps boils down to having not just the right hardware and software but also the right team: developers and engineers with the skills and knowledge to integrate AI into existing company processes and systems. Evolved from a software engineering and practice that aims to integrate software development and software operations, it is the key to converting the work of AI engines into real business offerings and achieving AI at a large, reliable scale.

Only a fraction of the code in many AI-powered businesses is devoted to AI functionality actual AI models are, in reality, a small part of a much larger system, and how users can interface with them matter as much as the model itself. To unlock the value of AI, you need to start with a well-designed production environment (the developers name for the real-world setting where the code meets the user). Thinking about this design from the beginning will help you manage your project, from probing whether the AI solution can be developed and integrated into the clients IT environment to the integration and deployment of the algorithm in the clients operating system. You want a setting in which software and hardware work seamlessly together, so a business can rely on it to run its real-time daily commercial operations.

A good product environment must successfully meet three criteria:

Dependability. Right now, AI technologies are fraught with technical issues. For example, AI-driven systems and models will stop functioning when being fed wrong and malformed data. Furthermore, the speed they can run at is bound to diminish when they have to ingest a large amount of data. These problems will, at best, slow the entire system down and, at worst, bring it to its knees.

Avoiding data bottlenecks is important to creating a dependable environment. Putting well-considered processing and storage architectures in place can overcome throughput and latency issues. Furthermore, anticipation is key. A good AIOps team will consider ways to prevent the environment from crashing and prepare contingency plans for when things do go wrong.

Flexibility. Business objectives and the supporting flows and processes within the overall system change on an ongoing basis. At the same time, everything needs to run like clockwork at a system level to enable the AI models to deliver their promised benefits: data imports must happen at regular intervals according to some fixed rules, reporting mechanisms must be continuously updated, and stale data must be avoided by frequent refreshing.

To meet the ever-evolving business requirements, a production environment needs to be flexible enough for quick and smooth system reconfiguration and data synchronization without compromising running efficiency. Think through how to best build a flexible architecture by breaking down it into manageable chunks, like LEGO blocks that can subsequently be added, replaced, or taken off.

Scalability and extendibility. When businesses expand, the plumbing within the infrastructure inevitably has to adapt. This can involve scaling up existing capabilities and extending into new competencies. Yet, an inescapable fact is that different IT systems often carry different performance, scalability, and extendibility characteristics. The result: Many problems will likely arise when they try to cross system boundaries.

Being able to simultaneously stay business as usual while embedding upgraded AI models is critical to business expansion. The success depends greatly on the ability of the team to constantly adjust, tinker, and test the existing system with the new proposed solution, reaching equilibrium through functionality of old with new systems.

The question, therefore, isnt whether you need an AIOps team, its what kind of AIOps team makes the most sense for your business. For most businesses, the most important decision theyll make with their AIOps team is whether they want to build it in house or contract it out. There are advantages to both, but heres what the tradeoffs look like:

Do it yourself. On the plus side, creating your own team to build and maintain a production environment gives you full control over the entire setup. It can also save a lot of potential management and contractual hassles resulting from having to work with external suppliers. This applies to both large companies, which may want to verticalize the AIOps team, as well as for small- to medium-sized enterprises that may want to expand the competencies of their IT team to be able to deal with the production environment directly.

That said, DIY is no small undertaking it involves significant administrative and organizational burdens, not to mention overhead. Additionally, companies need to develop expertise and knowledge of AIOps in house. The upfront economic impact is also likely to huge: High initial cash outlay are needed and tied up to buy depreciating assets like storage hardware and servers. Even with cloud infrastructure, the trial and error setup activities will likely push installation costs up.

Plug and play. An alternative is to partner with an AIOps vendor. A good vendor will be able to work closely with its client, offering the required expertise to construct and run a production environment that sits well within the clients IT infrastructure and can support AI models, be they self-developed or supplied by third parties. (This is what our company, Nexus FrontierTech, does.) With such a service, enterprises can now access a robust production environment and a trustworthy AIOps team yet freeing up the enormous resources otherwise necessary to run their own AIOps.

However, for many businesses, this may mean losing the right to own a proprietary system and a full say in the running of AIOps. It may come across as a compromise between financial constraints and access to a solid and robust AI architecture, which may not be as bespoke as in the case of a native AIOps project but good enough to help the firm digitize its production.

***

All too often, we are bombarded by news on the wonders created by AI what its going to do for us and how its going to change our lives. But that coverage misses a critical point: For any business wanting to leverage on the benefits of AI, what truly matters is not the AI models themselves; rather, its the well-oiled machine, powered by AI, that takes the company from where its today to where it wants to be in the future. Ideals and one-time projects dont. AIOps is therefore not an afterthought; its a competitive necessity.

More here:

The Dumb Reason Your AI Project Will Fail - Harvard Business Review

Gamers could soon be guiding military robots afterresearchers won a grant to investigate what they can teach an AI about controlling swarms of drones.

Scientists from the University of Buffalowillstudy the decisions, brainwaves and eye movements of people playing video games. They will then use this data to build an AI that can controlautonomous air and ground robots

The participants will play a real-time strategy game developed by the research team thats comparable to the likes of Starcraft and Company of Heroes,UBNow revealed.

[Read: How DeepMinds AI defeated top players at StarCraft II]

While they play, the researchers will record their decisions, track their eye movements with high-speed cameras, and monitor their brain wave patterns through electroencephalography (EEG)headsets.

The data they extract will be used to create algorithms that guideswarms of up to 250 military drones.

We dont want the AI system just to mimic human behavior; we want it to form a deeper understanding of what motivates human actions. Thats what will lead to more advanced AI, principal investigatorChowdhury told UBNow.

The study will be funded by the Defense Advanced Research Projects Agency (DARPA), which recentlypublished a video showing how itsdrone swarms could conduct anurban raid.

StarCraft experts left unimpressed by DARPAs efforts now have a chance to demonstrate that their own tactics are superior.

Youre here because you want to learn more about artificial intelligence. So do we. So this summer, were bringing Neural to TNW Conference 2020, where we will host a vibrant program dedicated exclusively to AI. With keynotes by experts from companies like Spotify, RSA, and Medium, our Neural track will take a deep dive into new innovations, ethical problems, and how AI can transform businesses. Get your early bird ticket and check out the full Neural track.

Published February 12, 2020 18:24 UTC

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Gamers will teach AI how to control military drone swarms - The Next Web

In 1950, Alan Turing, already famous for helping to crack the German Enigma code during World War II, devised the Turing test to define intelligence in machines. Could a computer, Turing asked, fool a human into thinking he was interacting with another person, or imitate human responses so well that it would be impossible for a person to tell the difference? If the machine could, Turing proposed, it could be considered intelligent. Turings thought experiment spawned scores of science-fiction tales, such as the 2015 hit movie Ex Machina. Now, artificial intelligence (AI) and autonomous algorithms are not only passing the Turing test every day but, more importantly, are making and saving money for the businesses that deploy them.

CenturyLink is one of the largest telecommunications providers in the United States, serving both small and large businesses nationwide. The collects thousands of sales leads from the businesses it serves, and it wishes to interact with them in the intimate, personal manner consumers have come to expect. Pursuing those leads more effectively would accelerate the companys growth, and converting and upselling a larger percentage of hot leads (people who have expressed interest in the companys services by filling out a form, clicking on an ad, or emailing the company) would boost the companys bottom line.

Accordingly, in the latter half of 2016, CenturyLink made a small investment in an AI-powered sales assistant made by Conversica to see if it could help the company identify hot leads without hiring an expensive army of sales reps to comb through the leads. The Conversica AI, a virtual assistant named Angie, sends about 30,000 emails a month and interprets the responses to determine who is a hot lead. She sets the appointment for the appropriate salesperson and seamlessly hands off the conversation to the human.

The potential customer gets a prompt and helpful outreach from Angie, and the reps who may each have 300 accounts save time because Angie vets the inquiries to identify the ones with the most potential. The reps also become more efficient because Angie routes the right leads to the right reps. In the small pilot CenturyLink ran, Angie could understand 99% of the emails she received; the 1% that she couldnt understand were sent to her manager.

According to Scott Berns, CenturyLinks Director of Marketing Operations, the company has approximately 1,600 sales people, and the Angie pilot started with four of them. That number soon rose to 20, and continues to grow today. Initially, Angie was identifying about 25 hot leads per week. That has now increased to 40, and the results have certainly validated the companys investment. It has earned $20 in new contracts for every dollar it spent on the system.

Tom Wentworth, Chief Marketing Officer at RapidMiner, a company that provides an analytical tool for data scientists, had a problem that was similar to CenturyLinks. Like many software companies, RapidMiner offers free trials, and Wentworth was struggling to serve the approximately 60,000 users who come to the companys site each month for the free trial. Many of the visitors using RapidMiners software, and needing help, are not paying anything for the service. So, how could Wentworth help them in a cost-effective way?

The company had a popular chat feature on its site, but its salesforce was overwhelmed and spending a great deal of time sorting through the chat sessions to find potential customers. It was like looking for the proverbial needle in a haystack.

Wentworth approached a friend who suggested he try a chat tool called Drift, which would ask a visitor initiating a chat, What brought you to RapidMiner today? The visitor would respond, and the Drift bot would provide one of seven potential follow-up answers. For example, a visitor might say, I need help, and Drift would send him or her to the support section of the website.

Drift was relatively easy to set up. Wentworth, like CenturyLink, started small, running the tool on a few of RapidMiners smaller web pages to test how helpful it was.

In less than two weeks, he had deployed it on every page.

The Drift bot now conducts about a thousand chats per month. It resolves about two-thirds of customer inquiries; those that it cannot, it routes to humans. In addition to Wentworth, who is monitoring the tools interactions, two co-op college students support the inquiries part-time. Wentworth told me that Drift is generating qualified leads for the sales team by making customers. Its the most productive thing Im doing in marketing, he said.

Every day, Wentworth reviews conversations people have had with Drift. Ive learned things about my visitors that no other analytics system would show, said Wentworth. Weve learned about new use cases, and weve learned about product problems.

This is the strength of an AI agent that can elicit information like a person, rather than an analytics tool that simply finds patterns in the data it collects, like a machine.

In 2016, Epson America, the printer and imaging giant, piloted the same Conversica AI assistant as CenturyLink. Chris Nickel, Epsons senior manager of commercial marketing, was drowning in all the leads he was getting for the companys diverse line of products: big printers, projectors, scanners, point of sale solutions, and industrial robots. Epson America was getting 40,000 to 60,000 leads per year from trade shows, direct mail, email marketing, social media, print and online advertising, and a successful brand awareness campaign. The leads would pour in, and whether they were good, bad, qualified or not, they would all be turned over to salespeople whose availability to follow up was inconsistent.

After implementing the AI assistant, Epsons leads are now followed up promptly and persistently until their AI assistant gets a response. Because the outreach to leads takes 6-8 times, Conversica is a true force multiplier for our sales team, say Nickel. After a lead is passed to one of Epsons partners, the AI assistant follows up to make sure the customer was satisfied. Sometimes, the response to that follow-up identifies a new sales opportunity, such as everything went great, and actually we are looking to buy another 60 projectors, giving Epson the opportunity to quickly capitalize on a new sales opportunity before the competition. Or it can uncover an unresolved customer support issue, such as Im having a problem with my projector.

As Nickel told me, Before, if we gave 100 leads to the reps, we might get a couple of responses from customers. Now, if we give 100 leads to the AI assistant, we get 50 responses. Epson reports that the official response rate with the AI assistant is 51%, representing a 240% increase from the baseline established at the beginning of the pilot, and a 75% increase in qualified leads. According to Nickel, that has produced $2 million in incremental revenue in just 90 days.

Because the AI tools that Epson America, RapidMiner, and CenturyLink deployed are offered as-a-service, it was easy for these companies to conduct pilots, and then scale up. Clearly, its worthwhile for companies to test AI-powered chat or email tools to see if they can convert more leads, and improve their understanding of what customers want and need.

When it comes to AI in business, a machine doesnt have to fool people; it doesnt have to pass the Turing test; it just needs to help them and thereby help the businesses that deploy them. And that test has already been passed. As one CMO told me, AI tools are the only way I can scale helpfulness to a global community of 200,000-plus users with a team of two.

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How AI Is Streamlining Marketing and Sales - Harvard Business Review

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Line Corp.,owner of Japans most popular messaging service, is getting into the artificial intelligence market in a big way by outlining an ambitious plan that pits it against the likes of Google, Facebook and Amazon.

According to a report inBloomberg News, Line Corp. is gearing up to launch a suite of AI softwaretools that will enable a digital assistant thatspeaks in Japanese and Korean. The assistant will be able to converse with users and provide weather and news via a dedicated smartphone app or a speaker that sits on the table and is called Wave, similar to Amazons Echo.

Line Corp., which unveiled the strategy during the Mobile World Congress in Barcelona, Spain, this week, said both the app and the speaker will come to the market between April and June. While Line faces a lot of competition, the companythinks it can stand out from the pack because of its local knowledge about the markets in which it is operating, including South Korea, Taiwan, Thailand and Indonesia.

There is a shift toward toward post-smartphone, post-touch technologies, Chief Executive Officer Takeshi Idezawa said in an interview with Bloomberg. These connected devices will permeate even deeper into our daily lives and therefore must even closer match the local needs, languages and cultures.

According to the report,Lines AI software platform was developed with its parent company Naver Corp., which operates a search engine. While Line is mainly a messaging app, customers use it to read the news, get a taxi ride and find part-time work. All of that content and interaction in local languages provides Line with an edge over larger rivals, noted the report, with Idezawa arguing that the AI experience is only as good as the data its trained on.

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Japan's Line Corp. To Launch AI App, Speaker - PYMNTS.com

Oto, a startup spun off from research at SRI International to help customer service operations understand voice intonation, announced a $5.3 million seed round today.

Participants in the round included Firstminute Capital, Fusion Fund, Interlace Ventures, SAP.iO and SRI International . The total includes a previous $1 million seed round, according to the company.

Teo Borschberg, co-founder and CEO at Oto, says the company launched out of SRI International, the same company where Apples Siri technology was originally developed. It has been developing intonation data, based originally on SRI research, to help customer service operations respond better to callers emotions. The goal is to use this area of artificial intelligence to improve interactions between customer service reps (CSRs) and customers in real time.

As part of the research phase, the company compiled a database of 100,000 utterances from 3,000 speakers, culled from two million sales conversations. From this data, it has built a couple of tools to help customer service operations automate intonation understanding.

The first is a live coaching tool. Its difficult to have management monitor every call, so only a small percentage gets monitored. With Oto, CSRs can get real-time coaching on every call to raise their energy or to calm a frustrated customer before a problem escalates. In real time, were able to guide the agents on how they sound, how energetic they are, and we can nudge and push them to be more energetic, Borschberg explained.

He says this has three main advantages: more engaged agents, higher sales conversion rates and better satisfaction scores and cost reduction.

The other product measures the quality of a customer experience and gives a score at the end of each call to help the CSR (and their managers) understand how well they did, simply based on intonation. It displays the score in a dashboard. Were building a universal understanding of satisfaction from intonation, where we can learn acoustic signatures that are positive, neutral, negative, Borschberg said.

He sees a huge market opportunity here, pointing to Qualtrics, which sold to SAP last year for $8 billion. He believes that surveying people is just a part of the story. You can build a better customer experience when you understand intonation of just how well that experience is going, and you put it on a scale so that it makes it easy to understand just how well or how poorly you are doing.

The company has 20 employees today, with offices in New York, Zurich and Lisbon. It has seven customers working with the product so far, but it is still early days.

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Oto snags $5.3M seed to use AI to understand voice intonation - TechCrunch

In BriefGoogle Brain and data science platform Kaggle have announcedan "AI Fight Club" to train machine learning systems on how tocombat malicious AI. As computer systems become smarter,cyberattacks also become tougher to defend against, and thiscontest could help illuminate unforeseen vulnerabilities. Reinforcing AI Systems

When artificial intelligence (AI) is discussed today, most people are referring to machine learning algorithmsor deep learning systems. While AI hasadvanced significantly over the years, the principle behind these technologies remains the same. Someone trains a system to receivecertain data and asks it to produce a specified outcome its up to the machine to develop its own algorithm to reach this outcome.

Alas, while weve been able to create some very smartsystems, they are not foolproof. Yet.

Data science competition platform Kaggle wants to prepare AI systems for super-smart cyberattacks, and theyre doing so by pitting AI against AIin acontest dubbed the Competition on Adversarial Attacks and Defenses. The battle is organized by Google Brain and will be part of the Neural Information Processing Systems (NIPS) Foundations 2017 competition track later this year.

This AI fight club will feature three adversarial challenges. The first (non-targeted adversarial attack) involves getting algorithms to confuse a machine learning system so it wont work properly. Another battle (targeted adversarial attack) requires training one AI to force another to classify data incorrectly. The third challenge (defense against adversarial attacks) focuses on beefing up a smart systems defenses.

Its a brilliant idea to catalyze research into both fooling deep neural networks and designing deep neural networks that cannot be fooled,Jeff Clune, a University of Wyoming assistant professor whose own work involves studying the limits of machine learning systems, told the MIT Technology Review.

AI is actually more pervasive now than most people think, and as computer systems have become more advanced, the use of machine learning algorithms has become more common. The problem is that the same smart technology can be used to undermine these systems.

Computer security is definitely moving toward machine learning, Google Brain researcher Ian Goodfellow told theMIT Technology Review. The bad guys will be using machine learning to automate their attacks, and we will be using machine learning to defend.

Training AI to fight malicious AI is the best way to prepare for these attacks, but thats easier said than done.Adversarial machine learning is more difficult to study than conventional machine learning, explained Goodfellow. Its hard to tell if your attack is strong or if your defense is actually weak.

The unpredictability of AI is one of the reasons some,including serial entrepreneur Elon Musk,are concerned that the tech may prove malicious in the future. They suggest that AI development be carefully monitored and regulated, but ultimately, itsthe people behind these systemsand not the systems themselves that present the true threat.

In an effort to get ahead of the problem, the Institute of Electrical and Electronics Engineers has createdguidelines for ethical AI, and groups like the Partnership on AI have also set up standards. Kaggles contest could illuminate new AI vulnerabilities that must be accounted for in future regulations, and by continuing to approach AI development cautiously, we can do more to ensure that the tech isnt used for nefarious means in the future.

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Google's AI Fight Club Will Train Systems to Defend Against Future Cyberattacks - Futurism

Earlier this week, at the campus of MIT, TechCrunch had the chance to sit down with famed roboticist Rodney Brooks, the founding director of MITs Computer Science and Artificial Intelligence Lab, and the cofounder of both iRobot and Rethink Robotics.

Brooks had a lot to say about AI, including his overarching concern that many people including renowned AI alarmist Elon Musk get it very wrong, in his view.

Brooks also warned that despite investors fascination with robotics right now, many VCs may underestimate how long these companies will take to build a potential problem for founders down the road.

Our chat, edited for length, follows.

TC: You started iRobot when there was no venture funding, back in 1990. You started Rethink in 2008, when there was funding but not a lot of interest in robotics. Now, there are both, which seemingly makes it a better time to start a robotics company. Is it?

RB: A lot of Silicon Valley and Boston VCs sort of fall over themselves about how theyre funding robotics [now], so you [as a founder] can get heard.

Despite [investors who say there is plenty of later-stage funding for robotics] , I think its hard for VCs to understand how long these far-out robotics systems will really take to get to where they can get a return on their investment, and I think thatll be crunch time for some founders.

TC: Theres also more competition and more patents that have been awarded, and a handful of companies have most of the worlds data. Does that make them insurmountable?

RB: Someone starting a robotics company today should be thinking that maybe at some point, in order to grow, theyre going to have to get bought by a large company that has the deep pockets to push it further. The ecosystem would still use the VC funding to prune out the good ideas from the bad ideas, but going all the way to an IPO may be hard.

Second thing: On this data, yes, machine learning is fantastic, it can do a lot, but there are a lot of things that need to be solved that are not just purely software; some of the big innovations [right now] have been new sorts of electric motors and controls systems and gear boxes.

TC: Youre writing a book on AI, so I have to ask you: Elon Musk expressed again this past weekend that AI is an existential threat. Agree? Disagree?

RB: There are quite a few people out there whove said that AI is an existential threat: Stephen Hawking, astronomer Royal Martin Rees, who has written a book about it, and they share a common thread, in that: they dont work in AI themselves. For those who do work in AI, we know how hard it is to get anything to actually work through product level.

Heres the reason that people including Elon make this mistake. When we see a person performing a task very well, we understand the competence [involved]. And I think they apply the same model to machine learning. [But they shouldnt.] When people saw DeepMinds AlphaGo beat the Korean champion and then beat the Chinese Go champion, they thought, Oh my god, this machine is so smart, it can do just about anything! But I was at DeepMind in London about three weeks ago and [they admitted that things could easily have gone very wrong].

TC: But Musks point isnt that its smart but that its going to be smart, and we need to regulate it now.

RB: So youre going to regulate now. If youre going to have a regulation now, either it applies to something and changes something in the world, or it doesnt apply to anything. If it doesnt apply to anything, what the hell do you have the regulation for? Tell me, what behavior do you want to change, Elon? By the way, lets talk about regulation on self-driving Teslas, because thats a real issue.

TC:Youve raised interesting points about this in your writings, noting that the biggest worry about autonomous cars whether theyll have to choose between driving into a gaggle of baby strollers versus a group of elderly women is absurd, considering how often that particular scenario happens today.

RB:There are some ethical questions that I think will slow down the adoption of cars. I live just a few blocks [from MIT]. And three times in the last three weeks, I have followed every sign and found myself at a point where I can either stop and wait for six hours, or drive the wrong way down a one-way street. Should autonomous cars be able to decide to drive the wrong way down a one-way street if theyre stuck? What if a 14-year-old riding in an Uber tries to override it, telling it to go down that one-way street? Should a 14-year-old be allowed to drive the car by voice? There will be a whole set of regulations that were going to have to have, that people havent even begun to think about, to address very practical issues.

TC: You obviously think robots are very complementary to humans, though there will be job displacement.

RB:Yes, theres no doubt and it will be difficult for the people who are being displaced. I think the role in factories, for instance, will shift from people doing manual work to people supervising. We have a tradition in manufacturing equipment that it has horrible user interfaces and its hard and you have to take courses, whereas in consumer electronics [as with smart phones], we have made the machines we use teach the people how to use them. And I do think we need to change our attitude in industrial equipment and other sorts of equipment, to make the machines teach the people how to use them.

TC: But do we run the risk of not taking this displacement seriously enough? Isnt the reason we have our current administration because we arent thinking enough about the people who will be impacted, particularly in the middle of the country?

RB: Theres a sign that maybe I should have seen and didnt. When I started Rethink Robotics, it was called Heartland Robotics. Id just come off six years of being an adviser to the CEO of John Deere; Id visited every John Deere factory. I could see the aging population. I could see they couldnt get workers to replace the aging population. So I started Heartland Robotics to build robotics to help the heartland.

Its no longer called Heartland Robotics because I started to get comments like, Why didnt you just come out and call it Bible Belt Robotics? The people in the Midwest thought we were making fun of them. I should have now, in retrospect, thought of that a little deeper.

TC: If you hadnt started Rethink, what else would you want to be focused on right now?

RB: Im a robotics guy, so every problem I think I can solve has a robotics solution. But what are the sorts of things that are important to humankind, which the current model of either large companies investing in or VCs investing in, arent going to solve? For instance: plastics in the ocean. Its getting worse; its contaminating our food chain. But its the problem of the commons. Who is going to fund a startup company to get rid of plastics in the ocean? Whos going to fund that, because whos going to [provide a return for those investors] down the line?

So Im more interested in finding places where robotics can help the world but theres no way currently of getting the research or the applications funded.

TC: Youre thought as the father of modern robotics. Do you feel like you have to be out there, evangelizing on the part of robotics and roboticists, so people understand the benefits, rather than focus on potential dangers?

RB: Its why Im right now writing a book on AI and robotics and the future because people are getting too scared about the wrong things and not thinking enough about what the real implications will be.

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This famous roboticist doesn't think Elon Musk understands AI - TechCrunch

More investors are setting their sights on the financial technology (Fintech) arena. According to consulting firm Accenture, investment in Fintech firms rose by 10 percent worldwide to the tune of $23.2 billion in 2016.

China is leading the charge after securing $10 billion in investments in 55 deals which account for 90 percent of investments in Asia-Pacific. The US came second taking in $6.2 billion in funding. Europe, also saw an 11 percent increase in deals despite Britain seeing a decrease in funding due to the uncertainty from the Brexit vote.

TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017.

The excitement stems from the disruption of traditional financial institutions (FIs) such as banks, insurance, and credit companies by technology. The next unicorn might be among the hundreds of tech startups that are giving Fintech a go.

What exactly is going to be the next big thing has yet to be determined, but other developments in computing like artificial intelligence (AI) may play a huge part.

The growing reality is that, while opportunities are abound, competition is also heating up.

Take, for example, the number of Fintech startups that aim to digitize routine financial tasks like payments. In the US, the digital wallet and payments segment is fiercely competitive. Pioneers like PayPal see themselves being taken on by other tech giants like Google and Apple, by niche-oriented ventures like Venmo, and even by traditional FIs.

Some ventures are seeing bluer oceans by focusing on local and regional markets where conditions are somewhat favorable.

The growth of Chinas Fintech was largely made possible by the relative age of its current banking system. It was easier for people to use mobile and web-based financial services such as Alibabas Ant Financial and Tencent since phones were more pervasive and more convenient to access than traditional financial instruments.

In Europe, the new Payment Services Directive (PSD2) set to take effect in 2018 has busted the game wide open. Banks are obligated to open up their application program interfaces (APIs) enabling Fintech apps and services to tap into users bank accounts. The line between banks and fintech companies are set to blur so just about everyone in finance is set to compete with old and new players alike.

Convenience has become a fundamental selling point to many users that a number of Fintech ventures have zeroed in on delivering better user experiences for an assortment of financial tasks such as payments, budgeting, banking, and even loan applications.

There is a mad scramble among companies to leverage cutting-edge technologies for competitive advantage. Even established tech companies like e-commerce giant Amazon had to give due attention to mobile as users shift their computing habits towards phones and tablets. Enterprises are also working on transitioning to cloud computing for infrastructure.

But where do more advanced technologies such as AI come in?

The drive to eliminate human fallibility has also made artificial intelligence (AI) driven to the forefront of research and development. Its applications range from sorting what gets shown on your social media newsfeed to self-driving cars. Its also expected to have a major impact in Fintech due to potential of game changing insights that can be derived from the sheer volume of data that humanity is generating. Enterprising ventures are banking on it to expose the gap in the market that has become increasingly small due to competition.

AI and finance are no strangers to each other. Traditional banking and finance have relied heavily on algorithms for automation and analysis. However, these were exclusive only to large and established institutions. Fintech is being aimed at empowering smaller organizations and consumers, and AI is expected to make its benefits accessible to a wider audience.

AI has a wide variety of consumer-level applications for smarter and more error-free user experiences. Personal finance applications are now using AI to balance peoples budgets based specifically to a users behavior. AI now also serves as robo-advisors to casual traders to guide them in managing their stock portfolios.

For enterprises, AI is expected to continue serving functions such as business intelligence and predictive analytics. Merchant services such as payments and fraud detection are also relying on AI to seek out patterns in customer behavior in order to weed out bad transactions.

People may soon have very little excuse of not having a handle of their money because of these services

Despite the exciting potential AI brings, there are still caveats. A big challenge for Fintech is to develop AI to be as smart as it can. There will be no shortage of people who will try to game and outwit such systems.

While AI seeks to eliminate human error, the flipside losing the human touch is a common criticism of AI. Smart money decisions are best made through numbers and logic. However, people do have an emotional connection with their money so it will be a challenge for Fintech apps to create experiences that do not alienate its users. Take the sad stories of insurance claims being denied due to strict algorithms that disregard the nuances of the human condition. AI still has a way to go factoring in what is just and moral in its decision making.

As for finance as a field and industry, there is also the issue of financial analysts, advisors, bankers, and traders being threatened to obsolescence by AI. A running joke with AI alludes to the Terminator movie franchise where AI seeks to eliminate humanity from existence. Unemployment, however, is rarely a laughing matter.

With the stiff competition in Fintech, ventures have to deliver a truly valuable products and services in order to stand out. The venture that provides the best user experience often wins but finding this X factor has become increasingly challenging.

The developments in AI may provide that something extra especially if it could promise to eliminate the guess work and human error out of finance. Its for these reasons that AI might just hold the key to what further Fintech innovations can be made.

This post is part of our contributor series. It is written and published independently of TNW.

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Why AI will determine the future of fintech - The Next Web - TNW