Daily Archives: December 13, 2019

By now, almost everyone knows a little bit about artificial intelligence, but most people arent tech experts, and many may not be aware of just how big an impact AI has. The truth is most consumers interact with technology incorporating AI every day. From the searches we perform in Google to the advertisements we see on social media, AI is an ever-present feature of our lives.

To help nonspecialists grasp the degree to which AI has been woven into the fabric of modern society, 12 experts from Forbes Technology Council detail some applications of AI that many may not be aware of.

1. Offering Better Customer Service

Calling customer service used to be as exciting as seeing a dentist. AI has changed that: You no longer have to repeat the same information countless times to different call center agents. Brands are able to tap into insights on all their previous interactions with you. Data analytics and AI help brands anticipate what their customers want and deliver more intelligent customer experiences. - Song Bac Toh, Tata Communications

2. Personalizing The Shopping Experience

Every time you shop online at an e-commerce site, as soon as you start clicking on a product the site starts to provide personalized recommendations of relevant products. Nowadays most of these applications use some form of AI algorithms (reinforced learning and others) to come up with such results. The experience is so transparent most shoppers dont even realize its AI. - Brian Sathianathan, Iterate.ai

3. Making Recruiting More Efficient

Next time you go to look for a new job, write your rsum for a computer, not a recruiter. AI is aggregating the talent pool, slimming the selection to a shortlist and ranking matches based on skills and qualifications. AI has thoroughly reviewed your rsum and application through machine learning before a human ever gets to look at them. - Tammy Cohen, InfoMart Inc.

4. Keeping Internet Services Running Smoothly

Consumers have come to expect their favorite apps and services to run smoothly, and AI makes that possible. AI does what humans cannot: It monitors apps, identifies problems and helps humans resolve them in a fraction of the time it would take manually. AI has the ability to spot patterns at scale in monitored data with the goal of having service interruptions solved before customers even notice. - Phil Tee, Moogsoft

5. Protecting Your Finances

For credit card companies and banks, AIs incredible ability to analyze massive amounts of data has become indispensable behind the scenes. These financial institutions leverage machine learning algorithms to identify potential fraudulent activity in your accounts and get ahead of any resulting detrimental effects. Every day, this saves people from tons of agony and headaches. - Marc Fischer, Dogtown Media LLC

6. Enhancing Vehicle Safety

Even if you dont have a self-driving vehicle, your car uses artificial intelligence. Lane-departure warnings notify a driver if the car has drifted out of its lane. Adaptive cruise control ensures that the car maintains a safe distance while cruising. Automated emergency braking senses when a collision is about to happen and applies the brakes faster than the driver can. - Amy Czuchlewski, Bottle Rocket

7. Converting Handwritten Text To Machine-Readable Code

The post office has tech called optical character recognition that converts handwritten text to machine-readable code. Reading handwriting requires human intelligence, but there are machines that can do it, too! Fun fact: This technology was invented in 1914 (yes, you read that right!). So, we experience forms of AI all the time. Its just a lot trendier now to call it AI. - Parry Malm, Phrasee

8. Improving Agriculture Worldwide

Most people dont think of AI when they eat a meal, but AI is improving agriculture worldwide. Some examples: satellites scanning farm fields to monitor crop and soil health; machine learning models that track and predict environmental impacts, like droughts; and big data to differentiate between plants and weeds for pesticide control. Thank AI for the higher crop yields. - John McDonald, ClearObject

9. Helping Humanitarian Efforts

While we often hear about AI going wrong, its doing good things, like guiding humanitarian aid, supporting conservation efforts and helping local government agencies fight droughts. AI always seems to get painted as some sci-fi type of endeavor when really its already the framework of many things going on around us all the time. - Alyssa Simpson Rochwerger, Figure Eight

10. Keeping Security Companies Safe From Cyberattacks

AI has become the main way that security companies keep us safe from cyber attacks. Deep learning models run against billions of events each day, identifying threats in ways that were simply unimaginable five years ago. Unfortunately, the bad actors also have access to AI tools, so the cat-and-mouse game continues. - Paul Lipman, BullGuard

11. Improving Video Surveillance Capabilities

In cities, along highways and in neighborhoods, video cameras are proliferating. Federal, state and/or local authorities deploy these devices to monitor traffic and security. In the background, AI-related technologies that include object and facial recognition technologies underpinned by machine and deep learning capabilities speed problem identification, reducing crime and mitigating traffic. - Michael Gurau, Kaiser Associates, Inc.

12. Altering Our Trust In Information

AI will change how we learn and the level of trust we place in information. Deepfakes and the ability to create realistic videos, pictures, text, speech and other forms of communication on which we have long relied to convey information will give rise to concerns about the foundational facts used to inform decision-making in every aspect of life. - Mike Fong, Privoro

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12 Everyday Applications Of Artificial Intelligence Many People Aren't Aware Of - Forbes

Since MIT Professor Bernhardt Trout's engineering ethics course shifted to focus on the ethics of artificial intelligence, the class has ballooned from a handful of students per semester in 2009 to more than 150 this year.

As deep learning and neural networks take center stage, "the students have much more of a concern about AI...particularly over the last year or so," Trout says.

A key challenge, according to Trout, is that "these algorithms push us toward us thinking less and acting based on impressions that may or may not be correct, as opposed to [making] our own decisions in a fully informed way. In general, we want to have the answer and move on. And these algorithms tend to play off on that psychology."

As AI evolves, "we need to be actively engaged in questioning what the algorithms do, what the results mean, and how inherent bias in the training set can affect the results," Trout says.

There are many ways this blind faith in algorithms can have adverse effects. For instance, when you start to believe (and "like") everything you see in your Facebook News Feed, which is powered by AI algorithms, you'll end up seeing only articles that confirm your viewpoints and biases, and you could become less tolerant of opposing views.

On other online platforms, content-recommendation algorithms can shape your preferences and nudge you in specific directions without your conscious knowledge. And in fields such as banking and criminal justice, blind trust in algorithms can be more damaging, such as the unwarranted decline of a loan application or an unfair verdict passed against a defendant.

"We have to remember that these are all mathematical algorithms. And there's a good argument against thinking that everything in human life is reducible to mathematics," Trout warns.

One of the major challenges of contemporary AI is lack of explainability. Deep-learning algorithms develop their logic from data and work in very complicated ways that are often opaque even to their creators. And this can cause serious trouble, especially where ethical issues are involved.

"It has become harder to trace decisions and analysis with methods like deep learning and neural nets," says Element AI's Marc-Etienne Ouimett. "The ability to know when a decision has been made or informed by an AI system, or to explain or interpret the logic behind that decision, becomes increasingly important in this context. You cannot effectively seek redress for harm caused by the misuse of an AI system unless you know that one has been used, or how it influenced the outcome."

This lack of transparency also makes it difficult to spot and fix ethical issues in algorithms. For instance, in one case, an AI algorithm designed to predict recidivism had silently used ZIP codes as a determining factor for the likelihood that a defendant would re-offend and wound up with a bias against black defendants, even though the programmers had removed racial information from their datasets.

In another case, a hiring algorithm penalized applicants whose resumes included the term "women," as in women's sports. More recently, Apple's new credit card was found to be biased against women, offering them up to 20 times less credit than menbecause of the AI algorithms it uses.

In these cases, the developers had gone to great lengths to remove any characteristics from the data that would cause bias in the algorithms. But AI often finds intricate correlations that indirectly allude to things like gender and race. And without any way to investigate those algorithms, finding these problematic correlations becomes a challenge.

Thankfully, efforts to create explainable AI models are taking place, including an ambitious project by DARPA, the research arm of the Department of Defense.

Another factor in the increased interest in the ethics of AI is the active engagement of the commercial sector.

"While the growth of deep learning and neural networks is a part of the growing attention toward ethical AI, another major contributor is...leaders in tech raising the issue and trying to actively make their points of view known to the broader public," Professor Trout says.

Execs like Bill Gates and Elon Musk, as well as scientists such as Stuart Russell and the late Stephen Hawking, have issued warnings about the potentially scary unintended consequences of AI. And tech giants like Microsoft and Google have been forced to explain their approach to AI and develop ethnical guidelines, particularly as it relates to selling their technology to government agencies.

"Ethical principles are a good start, but operationalizing these across the company is what counts. Each team, from fundamental/applied research to product design, development, and deployment, must understand how these principles apply to their functions," Element AI's Ouimett says.

Ouimett also underlines the need for companies to work with lawmakers actively. "It's important for businesses that have the technical expertise to engage in good faith with regulators to help them understand the nature of the risks posed by the technology," he says.

Element AI recently partnered with The Rockefeller and Mozilla Foundations to produce a list of recommendations for governments and companies on the role of the human-rights framework in AI governance.

"The collaboration will focus on advancing research on the legal, governance, and technical components of data trusts, which both Element AI and the Mozilla Foundation believe have tremendous potentialas safe and ethical data-sharing mechanisms, as many governments have thus far conceived of them, but also as tools that could be used to empower the public to participate in decisions regarding the use of their personal data, and to collectively seek redress in cases of harm," Ouimett says.

But Professor Trout has a slightly different view on the involvement of tech companies in AI ethics. "At the end of the day, they're doing this to a large extent for commercial reasons. They want to make their employees happy. That was the reason Google decided not to work with the Department of Defense. And they want to make their customers and the government happyand they want to enhance their bottom line," he says.

"I have not seen these companies really promote a thoughtful, deep approach to ethics, and that's where I would find them fall short. They have resources, they would be able to, but I don't see that happening. And I think that's a pity."

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Why AI Leads Us to Think Less, Act Impulsively - PCMag.com

The Decade, Reviewed looks back at the 2010s and how it changed human society forever. From 2010 to 2019, our species experienced seismic shifts in science, technology, entertainment, transportation, and even the very planet we call home. This is how the past ten years have changed us.

Bots are a lot like humans: Some are cute. Some are ugly. Some are harmless. Some are menacing. Some are friendly. Some are annoying ... and a little racist. Bots serve their creators and society as helpers, spies, educators, servants, lab technicians, and artists. Sometimes, they save lives. Occasionally, they destroy them.

In the 2010s, automation got better, cheaper, and way less avoidable. Its still mysterious, but no longer foreign; the most Extremely Online among us interact with dozens of AIs throughout the day. That means driving directions are more reliable, instant translations are almost good enough, and everyone gets to be an adequate portrait photographer, all powered by artificial intelligence. On the other hand, each of us now sees a personalized version of the world that is curated by an AI to maximize engagement with the platform. And by now, everyone from fruit pickers to hedge fund managers has suffered through headlines about being replaced.

Humans and tech have always coexisted and coevolved, but this decade brought us closer togetherand closer to the futurethan ever. These days, you dont have to be an engineer to participate in AI projects; in fact, you have no choice but to help, as youre constantly offering your digital behavior to train AIs.

So heres how we changed our bots this decade, how they changed us, and where our strange relationship is going as we enter the 2020s.

All those little operational tweaks in our day come courtesy of a specific scientific approach to AI called machine learning, one of the most popular techniques for AI projects this decade. Thats when AI is tasked not only with finding the answers to questions about data sets, but with finding the questions themselves; successful deep learning applications require vast amounts of data and the time and computational power to self-test over and over again.

Deep learning, a subset of machine learning, uses neural networks to extract its own rules and adjust them until it can return the right results; other machine learning techniques might use Bayesian networks, vector maps, or evolutionary algorithms to achieve the same goal.

In January, Technology Reviews Karen Hao released an exhaustive analysis of recent papers in AI that concluded that machine learning was one of the defining features of AI research this decade. Machine learning has enabled near-human and even superhuman abilities in transcribing speech from voice, recognizing emotions from audio or video recordings, as well as forging handwriting or video, Hao wrote. Domestic spying is now a lucrative application for AI technologies, thanks to this powerful new development.

Haos report suggests that the age of deep learning is finally drawing to a close, but the next big thing may have already arrived. Reinforcement learning, like generative adversarial networks (GANs), pits neural nets against one another by having one evaluate the work of the other and distribute rewards and punishments accordinglynot unlike the way dogs and babies learn about the world.

The future of AI could be in structured learning. Just as young humans are thought to learn their first languages by processing data input from fluent caretakers with their internal language grammar, computers can also be taught how to teach themselves a taskespecially if the task is to imitate a human in some capacity.

This decade, artificial intelligence went from being employed chiefly as an academic subject or science fiction trope to an unobtrusive (though occasionally malicious) everyday companion. AIs have been around in some form since the 1500s or the 1980s, depending on your definition. The first search indexing algorithm was AltaVista in 1995, but it wasnt until 2010 that Google quietly introduced personalized search results for all customers and all searches. What was once background chatter from eager engineers has now become an inescapable part of daily life.

One function after another has been turned over to AI jurisdiction, with huge variations in efficacy and consumer response. The prevailing profit model for most of these consumer-facing applications, like social media platforms and map functions, is for users to trade their personal data for minor convenience upgrades, which are achieved through a combination of technical power, data access, and rapid worker disenfranchisement as increasingly complex service jobs are doubled up, automated away, or taken over by AI workers.

The Harvard social scientist Shoshana Zuboff explained the impact of these technologies on the economy with the term surveillance capitalism. This new economic system, she wrote, unilaterally claims human experience as free raw material for translation into behavioural data, in a bid to make profit from informed gambling based on predicted human behavior.

Were already using machine learning to make subjective decisionseven ones that have life-altering consequences. Medical applications are only some of the least controversial uses of artificial intelligence; by the end of the decade, AIs were locating stranded victims of Hurricane Maria, controlling the German power grid, and killing civilians in Pakistan.

The sheer scope of these AI-controlled decision systems is why automation has the potential to transform society on a structural level. In 2012, techno-socialist Zeynep Tufekci pointed out the presence on the Obama reelection campaign of an unprecedented number of data analysts and social scientists, bringing the traditional confluence of marketing and politics into a new age.

Intelligence that relies on data from an unjust world suffers from the principle of garbage in, garbage out, futurist Cory Doctorow observed in a recent blog post. Diverse perspectives on the design team would help, Doctorow wrote, but when it comes to certain technology, there might be no safe way to deploy:

It doesnt help that data collection for image-based AI has so far taken advantage of the most vulnerable populations first. The Facial Recognition Verification Testing Program is the industry standard for testing the accuracy of facial recognition tech; passing the program is imperative for new FR startups seeking funding.

But the datasets of human faces that the program uses are sourced, according to a report from March, from images of U.S. visa applicants, arrested people who have since died, and children exploited by child pornography. The report found that the majority of data subjects were people who had been arrested on suspicion of criminal activity. None of the millions of faces in the programs data sets belonged to people who had consented to this use of their data.

State-level efforts to regulate AI finally emerged this decade, with some success. The European Unions General Data Protection Regulation (GDPR), enforceable from 2018, limits the legal uses of valuable AI training datasets by defining the rights of the data subject (read: us); the GDPR also prohibits the black box model for machine learning applications, requiring both transparency and accountability on how data are stored and used. At the end of the decade, Google showed the class how not to regulate when they built, and then scrapped, an external AI ethics panel a week later, feigning shock at all the negative reception.

Even attempted regulation is a good sign. It means were looking at AI for what it is: not a new life form that competes for resources, but as a formidable weapon. Technological tools are most dangerous in the hands of malicious actors who already hold significant power; you can always hire more programmers. During the long campaign for the 2016 U.S. presidential election, the Putin-backed IRA Twitter botnet campaignsessentially, teams of semi-supervised bot accounts that spread disinformation on purpose and learn from real propagandainfiltrated the very mechanics of American democracy.

Keeping up with AI capacities as they grow will be a massive undertaking. Things could still get much, much worse before they get better; authoritarian governments around the world have a tendency to use technology to further consolidate power and resist regulation.

Tech capabilities have long since proved too fast for traditional human lawmakers, but one hint of what the next decade might hold comes from AIs themselves, who are beginning to be deployed as weapons against the exact type of disinformation other AIs help to create and spread. There now exists, for example, a neural net devoted explicitly to the task of identifying neural net disinformation campaigns on Twitter. The neural nets name is Grover, and its really good at this.

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The Bot Decade: How AI Took Over Our Lives in the 2010s - Popular Mechanics

In many industries, artificial intelligence (AI) is seen more as a buzzword than a tangible solution to accelerate outcomes. In fact, resources are commonly used to establish what AI can and cant do eCommerce is an exception to this.

In eCommerce, brands have invested in the power of AI. The trend is only set to grow, with a compound annual growth rate (CAGR) of 42.8 per cent in retail and eCommerce between 2019 and 2025. Whether its informing pricing strategies and product promotions, or satisfying the demand for more nuanced customer journeys, theres no shortage of applications in eCommerce.

While use-cases may be too specialised to drive widespread adoption in other industries, in eCommerce, AI enables merchants to add a personal touch to the way consumers buy their goods convenient given consumer demands for flexibility and consistency across multiple platforms.

So, how are eCommerce merchants turning this technology from a buzzword to a panacea and what can other sectors learn from it?

To begin with the most well-known solution - chatbots automate community management, customer engagement and even sales leads. According to Gartner, the average person will have more conversations with bots than with their spouse by 2020. Meanwhile, 70 per cent of white-collar workers will interact with conversational platforms on a daily basis by 2022. AI-enabled bots provide eCommerce merchants with a scalable solution which works around the clock, using natural language processing (NLP) to help people find the right product or make complaints. Equally, they are integrated with organisations internal APIs to provide visibility over product availability or assist employees with customer engagement.

Elsewhere, AI helps brands to build meaningful relationships with their customers by making sense of increasingly large volumes of data. When a consumer visits a website, they leave behind a trail of digital breadcrumbs, much of which has been left untapped. However, AI allows retailers to rapidly sift through transactional data to help employees generate insights from trends, purchasing patterns and marketing leads, and turn them into improved decisions.

In the digital era, retailers must be able to contextualise, optimise and narrow down search results for their buyers. AI enables merchants to leverage cookie data and provide consumers with highly tailored offerings. By utilising natural language processing capabilities, image, video and audio recognition, retailers can home in on what it is their customers really want.

Clearly, there is no shortage of use cases for AI in eCommerce. While some are more obvious than others, what is certain is that it enables merchants to provide customers with seamless experiences while enabling employees to do their work more effectively. So how can AI be leveraged successfully?

AI is nothing without data. It derives intelligence from the vast quantities of information possessed by organisations, meaning data science and data engineering become crucial. However, deriving insights from this data is by no means easy, and organisations need to ensure that they have the necessary foundations in place to apply analytics.

The problem is that this data is often extracted from fragmented and siloed sources, meaning there is a need to make data more accessible this requires coherent integration structures. Whats more, screening and aligning this data is a manual process and preparing data can take up a significant amount of time and resources.

Additionally, much of the data needed for AI to perform requires perishable insights. By this, we mean insights where the value degrades over time and which need to be detected and actioned as quickly as possible. Therefore, if companies struggle to collect sufficient amounts of the necessary data, it can quickly be rendered useless.

Preparing data is a complex process, particularly as large organisations tend to have their information spread across multiple sources. This all needs to be aligned if AI is to yield the hoped-for results. This means that data quality becomes a key challenge for eCommerce merchants to overcome, as poor data could prove detrimental so, when it comes to implementing AI, do the rewards outweigh the challenges?

eCommerce stands to benefit tremendously from AI. Already, we see companies shape the buying and selling experience for both shoppers and sellers AI is forecasted to be worth $27 billion in retail alone by 2025.

Customer experience will be the most significant beneficiary of developments in AI. With consumer adoption of technology and increasing demands for personalisation driving adoption, merchants cant afford to sit tight. While the technology is costly and difficult to implement, those early adopters will reap the rewards.

Whether or not eCommerce merchants can truly benefit will depend on how prepared they are. Before investing in AI, retailers need to think about the business case, whether there are opportunities to exploit, and whether they have the right data, people and technology.

Ultimately, there is a lot of preparation to do before AI can begin producing results. Organisations need to ensure they have clean, accessible and high-quality data from which they can derive meaningful insights only then can they ride the hype.

Richard Mathias, Senior Technology Architect, LiveArea

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The applications of AI in eCommerce - ITProPortal

At the last Democratic presidential debate, the technologist candidate Andrew Yang emphatically declared that were in the process of potentially losing the AI arms race to China right now. As evidence, he cited Beijings access to vast amounts of data and its substantial investment in research and development for artificial intelligence. Yang and othersmost notably the National Security Commission on Artificial Intelligence, whichreleased its interim report to Congress last monthare right about Chinas current strengths in developing AI and the serious concerns this should raise in the United States. But framing advances in the field as an arms race is both wrong and counterproductive. Instead, while being clear-eyed about Chinas aggressive pursuit of AI for military use and human rights-abusing technological surveillance, the United States and China must find their way to dialogue and cooperation on AI. A practical, nuanced mix of competition and cooperation would better serve U.S. interests than an arms race approach.

AI is one of the great collective Rorschach tests of our times. Like any topic that captures the popular imagination but is poorly understood, it soaks up the zeitgeist like a sponge.

Its no surprise, then, that as the idea of great-power competition has reengulfed the halls of power, AI has gotten caught up in therace narrative.ChinaAmericans are toldis barreling ahead on AI, so much so that the United States willsoon be lagging far behind. Like the fears that surrounded Japans economic rise in the 1980s or the Soviet Union in the 1950s and 1960s, anxiety around technological dominance are really proxies for U.S. insecurity about its own economic, military, and political prowess.

Yet as technology, AI does not naturally lend itself to this framework and is not a strategic weapon.Despite claims that AI will change nearly everything about warfare, and notwithstanding its ultimate potential, for the foreseeable future AI will likely only incrementally improve existing platforms, unmanned systems such as drones, and battlefield awareness. Ensuring that the United States outpaces its rivals and adversaries in the military and intelligence applications of AI is important and worth the investment. But such applications are just one element of AI development and should not dominate the United States entire approach.

The arms race framework raises the question of what one is racing toward. Machine learning, the AI subfield of greatest recent promise, is a vast toolbox of capabilities and statistical methodsa bundle of technologies that do everything from recognizing objects in images to generating symphonies. It is far from clear what exactly would constitute winning in AI or even being better at a national level.

The National Security Commission is absolutely right that developments in AI cannot be separated from the emerging strategic competition with China and developments in the broader geopolitical landscape. U.S. leadership in AI is imperative. Leading, however, does not mean winning. Maintaining superiority in the field of AI is necessary but not sufficient. True global leadership requires proactively shaping the rules and norms for AI applications, ensuring that the benefits of AI are distributed worldwidebroadly and equitablyand stabilizing great-power competition that could lead to catastrophic conflict.

That requires U.S. cooperation with friends and even rivals such as China. Here, we believe that important aspects of the National Security Commission on AIs recent report have gotten too little attention.

First, as the commission notes, official U.S. dialogue with China and Russia on the use of AI in nuclear command and control, AIs military applications, and AI safety could enhance strategic stability, like arms control talks during the Cold War. Second, collaboration on AI applications by Chinese and American researchers, engineers, and companies, as well as bilateral dialogue on rules and standards for AI development, could help buffer the competitive elements of anincreasingly tense U.S.-Chinese relationship.

Finally, there is a much higher bar to sharing core AI inputs such as data and software and building AI for shared global challenges if the United States sees AI as an arms race. Although commercial and military applications for AI are increasing, applications for societal good (addressing climate change,improving disaster response,boosting resilience, preventing the emergence of pandemics, managing armed conflict, andassisting in human development)are lagging. These would benefit from multilateral collaboration and investment, led by the United States and China.

The AI arms race narrative makes for great headlines, buttheunbridled U.S.-Chinese competition it implies risks pushing the United States and the world down a dangerous path. Washington and Beijing should recognize the fallacy of a generalized AI arms race in which there are no winners. Instead, both should lead by leveraging the technology to spur dialogue between them and foster practical collaboration to counter the many forces driving them apartbenefiting the whole world in the process.

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Artificial Intelligence Isn't an Arms Race With China, and the United States Shouldn't Treat It Like One - Foreign Policy

At a startup's birth, an idea or a vision can be so well wrapped in a blanket of buzzwords that not even the smartest investors can stop themselves from throwing money at it. Lets be honest: Most groundbreaking ideas deserve a sober share of criticism, but once a company gets its initial funding and the team goes into execution, continuing to feed the same party line has to be called out.

Some of the buzz floating around the insurance industry is artificial intelligence (AI). When I say "buzz," I mean product messaging like this:

Our [insert product name] uses artificial intelligence to help find you the best insurance coverage.

Or, better yet:

"Let artificial intelligence handle your insurance claims from the initial call to the fund's disbursement."

One of the insurance industry's sore spots is claims from the first notice of loss (FNOL) to the claims payout. It is ridden with fraud, inefficiencies and lack of transparency. Many of us have experiences spending hours on the phone with customer service describing what happened, going from one shop to another to get several repair estimates and waiting for the reimbursement check to arrive.

It gets worse when dealing with home insurance claims. There is always a caveat to what is covered and what is not covered.

Thus, filing a claim turns into a never-ending argument with the insurer and a big out-of-pocket expense. This surely sounds like a perfect candidate to be saved by an AI knight in shining armor riding on a machine learning horse.

Let's take a look at auto claims, which are relatively standardized and straightforward compared to home damage claims.

I cringe when I read that certain insurance companies are "using the latest AI technology" to help resolve claims in seconds or minutes rather than weeks or months. Without digging deep under wraps of each insurer's statement, I believe that, at best, the usage of AI is limited to identifying some of the damaged parts.

Here is how a typical AI computer vision system works: It analyzes (using neural networks) thousands of images (let's say, a rear bumper) and uses proprietary mathematical models to come up with a certain confidence level upon looking at a new picture of what is supposed to be a bumper.

Once the system has analyzed the image and determined with some confidence level that it is indeed a Ford Mustang rear bumper and the damage is medium, the platform can match all the previous Ford Mustang rear bumper repair jobs that were classified as medium and suggest how much it would cost to fix the damage (whether to repair or replace). Then it can suggest a course of action based on the deductible or coverage details.

I recall my own "minor damage" claim experience. I happened to hit a turkey while driving 65 mph on an interstate. Besides losing one side marker lamp and acquiring a few bumper dents, I couldnt see anything serious. I called my insurance company and filed a claim. When my insurance company adjuster visually inspected the damage, his estimate was about $1,650. I took my vehicle to a body shop, and the mechanic lifted the car to inspect it properly. Guess what? He found more damage and had to invite the adjuster back to look. The final bill ended up being around $4,500.

Would this number have been know without a human looking more closely? Could AI have found all that hidden damage that an adjuster with 30 years of experience didnt see?

While some insurance companies claim AI technology provides them with immediate total loss and repair estimates based on photos and, thus, saves everyone time and expenses, I am personally skeptical. Yes, this would have made my FNOL experience superb. I wouldnt have spent time on the phone, driven to an adjusters shop or waited for his initial inspection. I would have just received an ACH transfer to my bank account for $1,650. The fun part would have begun once I brought the car to the mechanic, who then would have had to schedule an appointment to get the adjuster over to approve more funding, and Id be waiting again, this time cursing my insurance for not being able to get it right the first time.

Insurance companies watch their claim payouts like hawks. It's no wonder why. In my experience, overall claim expenses are 35% to 45% of their total expenses. So if AI will begin to overpay, an insurance company will be underwater in no time. So naturally, they would "tune" AI to underpay, resulting in additional adjuster visits, thus defeating the purpose of AI to begin with.

Do I believe AI will be capable of replacing human adjusters or making entire insurance claim processes touchless"?

Maybe in the near future, perhaps in 5-10 years.

For now, adjusters, customer service agents and underwriters are safe from the incoming AI doom-and-gloom predictions.

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Will AI Be Capable Of Replacing Insurance Claims Adjusters? - Forbes

An expert on artificial intelligence has called for all algorithms that make life-changing decisions in areas from job applications to immigration into the UK to be halted immediately.

Prof Noel Sharkey, who is also a leading figure in a global campaign against killer robots, said algorithms were so infected with biases that their decision-making processes could not be fair or trusted.

A moratorium must be imposed on all life-changing decision-making algorithms in Britain, he said.

Sharkey has suggested testing AI decision-making machines in the same way as new pharmaceutical drugs are vigorously checked before they are allowed on to the market.

In an interview with the Guardian, the Sheffield University robotics/AI pioneer said he was deeply concerned over a series of examples of machine-learning systems being loaded with bias.

On inbuilt bias in algorithms, Sharkey said: There are so many biases happening now, from job interviews to welfare to determining who should get bail and who should go to jail. It is quite clear that we really have to stop using decision algorithms, and I am someone who has always been very light on regulation and always believed that it stifles innovation.

But then I realised eventually that some innovations are well worth stifling, or at least holding back a bit. So I have come down on the side of strict regulation of all decision algorithms, which should stop immediately.

There should be a moratorium on all algorithms that impact on peoples lives. Why? Because they are not working and have been shown to be biased across the board.

Sharkey said he had spoken to the biggest global social media and computing corporations, such as Google and Microsoft, about the innate bias problem. They know its a problem and theyve been working, in fairness, to find a solution over the last few years but none so far has been found.

Until they find that solution, what I would like to see is large-scale pharmaceutical-style testing. Which in reality means that you test these systems on millions of people, or at least hundreds of thousands of people, in order to reach a point that shows no major inbuilt bias. These algorithms have to be subjected to the same rigorous testing as any new drug produced that ultimately will be for human consumption.

As well as numerous examples of racial bias in machine-led decisions on, for example, who gets bail in the US or on healthcare allocation, Sharkey said his work on autonomous weapons, or killer robots, also illuminated how bias infects algorithms.

There is this fantasy among people in the military that these weapons can select individual targets on their own. These move beyond the drone strikes, which humans arent great at already, with operatives moving the drone by remote control and targeting individual faces via screens from bases thousands of miles away, he said.

Now the new idea that you could send autonomous weapons out on their own, with no direct human control, and find an individual target via facial recognition is more dangerous. Because what we have found out from a lot of research is that the darker the skin, the harder it is to properly recognise the face.

In the laboratory you get a 98% recognition rate for white males without beards. Its not very good with women and its even worse with darker-skinned people. In the latter case, the laboratory results have shown it comes to the point where the machine cannot even recognise that you have a face.

So, this exposes the fantasy of facial recognition being used to directly target enemies like al-Qaida, for instance. They are not middle-class men without beards, of whom there is a 98% recognition rate in the lab. They are darker-skinned people and AI-driven weapons are really rubbish at that kind of recognition under the current technology. The capacity for innocent people being killed by autonomous weapons using a flawed facial recognition algorithm is enormous.

Sharkey said weapons like these should not be in the planning stage, let alone ever deployed. In relation to decision-making algorithms generally, these flaws in facial recognition are yet another argument along with all the other biases that they too should be shut down, albeit temporarily, until they are tested just like any new drug should be.

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AI expert calls for end to UK use of racially biased algorithms - The Guardian

Dublin, Dec. 13, 2019 (GLOBE NEWSWIRE) -- The "Global Artificial Intelligence (AI) in Agriculture Market: Focus on Product Type (Software, Hardware, AI-as-a-Service), Farming Type (Field Farming, Livestock, Indoor), Application (Crop Protection, Weather Forecasting, Automation), Funding - Analysis and Forecast, 2019-2024" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence (AI) in Agriculture Market Analysis projects the market to grow at a significant CAGR of 28.38% during the forecast period from 2019 to 2024.

The reported growth in the market is expected to be driven by the increasing need to optimize farm operation planning, growing demand to derive insights from emerging complexities of data-driven farming and rising development of autonomous equipment in agriculture.

Artificial intelligence has emerged to be a strong driving force behind the growth of data-driven farming. Regions and countries where agriculture is the major source of livelihood and sustenance, artificial intelligence technology has led to greater profitability in the farms of those economies.

The reduction in expenditure and resultant positive RoI with AI's integration in farm equipment and operations has even reached above 30% in a few countries. Such favorable advantages associated with the technology have led to extensive investments by all types of stakeholders including government, private investors, corporations, and academic institutions, from across the world.

Expert Quote

Artificial intelligence has become the leader of deep technologies in the era of precision agriculture. It has created the widest impact across agricultural sectors including crop and livestock over recent years. Governments of the majority of the leading countries in the agriculture market are working on their respective national AI strategies. This technology has fastened the digital transformation process, even in sluggish agricultural economies. Its capability to enable precision and autonomy in farm operations has especially caught the attention of growers across the world.

Scope of the Report

The global artificial intelligence in agriculture market research provides a detailed perspective regarding the adoption of AI technology in the agriculture industry, its market size in value, its estimation, and forecast, among others. The purpose of this market analysis is to examine the outlook of artificial intelligence technology in the agriculture industry in terms of factors driving the market, trends, developments, and regulatory landscape, among others.

The report further takes into consideration the funding and investment landscape, government initiatives landscape, market dynamics, and the competitive landscape, along with the detailed financial and product contributions of the key players operating in the market. The artificial intelligence in the agriculture market report is a compilation of different segments including market breakdown by product offering, farming type, application, and region.

Market Segmentation

The global artificial intelligence in the agriculture market (on the basis of product offering) is segmented into software, hardware, AI-as-a-Service, and support services. The software segment dominated the global artificial intelligence in the agriculture market in 2018 and is anticipated to maintain its dominance in market size throughout the forecast period (2019-2024) with hardware and AI-as-a-Service experiencing higher growth rates.

The global artificial intelligence in the agriculture market (on the basis of farming type) is segmented into field farming, livestock farming, indoor farming, and other farming types such as aquaculture. The field farming segment dominated the global artificial intelligence in the agriculture market in 2018 and is anticipated to maintain its dominance throughout the forecast period (2019-2024).

The global artificial intelligence in the agriculture market (on the basis of application) is segmented into crop protection, weather forecasting, precision farming, farm machinery automation, crop growth assessment, and other applications under the category crop, fruit, and vegetable farming. The market is also segmented into animal growth monitoring, animal health monitoring, and other applications under the category livestock and aquaculture farming. The crop protection segment dominated the global artificial intelligence in agriculture market in 2018. Applications such as farm machinery automation and precision farming (across crop and livestock) are anticipated to experience higher growth rates over the forecast period (2019-2024).

The global artificial intelligence in the agriculture market by region is segregated under four major regions, namely North America, Europe, APAC, and Rest-of-the-World. Data for each of these regions has been provided by country. Interesting regional market dynamics have also been provided in the report.

Key Companies in the Global Artificial Intelligence in Agriculture Market

The key market players in the global artificial intelligence in agriculture market include Alibaba Group Holding Limited, AgEagle Aerial Systems Inc., BASF SE, The Climate Corporation (A Bayer AG Company), Deere & Company, IBM Corporation, JD.com Inc., Microsoft Corporation, Robert Bosch GmbH, SAP SE, Connecterra B.V., Descartes Labs, Gamaya SA, Granular Inc., Harvest Croo Robotics, PrecisionHawk, Prospera Technologies Ltd., Root AI Inc., SZ DJI Technology Co. Ltd., Vineview, AGCO Corporation, Capgemini SE, Cargill Inc., CNH Industrial N.V., Iteris Inc., Lindsay Corporation, Abundant Robotics Inc., aWhere Inc., Aquabyte Inc., Ceres Imaging, Delair, ecoRobotix Ltd., Farmers Edge, Taranis, and XAG Co. Ltd., among others.

Key Topics Covered

Executive Summary

1 Market Dynamics1.1 Overview1.2 Impact Analysis1.3 Market Drivers1.3.1 Growing Need for Precision and Efficiency in Agricultural Operations1.3.2 Emerging Complexities in Data-Driven Farming1.3.3 Rising Demand for Autonomous Equipment1.4 Market Restraints1.4.1 Data Privacy Concerns Among Farmers1.4.2 Lack of Technical Infrastructure in Developing Countries1.5 Market Opportunities1.5.1 Favorable Government Initiatives to Support AI in Agriculture1.5.2 Increase in Implementation of Robots and Drones in Agriculture1.5.3 Rise in Adoption of SaaS Business Model in Agriculture

2 Competitive Insights2.1 Key Strategies and Developments2.1.1 Partnerships, Collaborations, and Joint Ventures2.1.2 Product Launches and Developments2.1.3 Business Expansions and Contracts2.1.4 Mergers and Acquisitions2.1.5 Others (Awards and Recognition)2.2 Competitive Benchmarking of Agricultural AI Analytics Companies

3 Industry Analysis3.1 Artificial Intelligence in Agriculture: Technology Ecosystem3.1.1 AI Technology Stack3.1.1.1 AI-Powered Technologies3.1.1.1.1 Machine Learning3.1.1.1.2 Computer Vision3.1.1.1.3 Deep Learning3.1.1.1.4 Speech Recognition Technology3.1.1.1.5 Other Technologies3.1.1.2 Hardware3.1.1.2.1 Memory3.1.1.2.2 Storage3.1.1.2.3 Logic3.1.1.2.4 Networking3.1.1.3 Others3.1.1.4 AI Technology Classifications3.1.1.4.1 AI Technology (by Functionality)3.1.1.4.1.1 Reactive Machines3.1.1.4.1.2 Limited Memory3.1.1.4.1.3 Theory of Mind3.1.1.4.1.4 Self-Awareness3.1.1.4.2 AI Technology (by Capability)3.1.1.4.2.1 Weak AI3.1.1.4.2.2 General AI3.1.1.4.2.3 Strong AI3.1.2 Key AI Use Cases in Agriculture3.1.2.1 Predictive Analytics3.1.2.2 Drones / UAVs3.1.2.3 Robotics3.1.2.4 Autonomous Vehicles3.2 Key Consortiums and Associations3.3 Investment and Funding Landscape3.4 Government Initiatives Landscape3.4.1 North America3.4.2 Europe3.4.3 Asia-Pacific3.4.4 Rest-of-the-World

4 Global Artificial Intelligence in Agriculture Market (by Product Offering), $Million4.1 Assumptions and Limitations for Analysis and Forecast of the Global Artificial Intelligence in Agriculture Market4.2 Market Overview4.3 Software4.4 Hardware4.5 Artificial Intelligence-as-a-Service (AIaaS)4.6 Support Services

5 Global Artificial Intelligence in Agriculture Market (by Farming Type), $Million5.1 Market Overview5.2 Field Farming5.3 Indoor Farming5.4 Livestock Farming5.5 Others

6 Global Artificial Intelligence in Agriculture Market (by Application), $Million6.1 Market Overview6.2 Crops, Fruits, Vegetables, and Other Plants6.2.1 Crop Protection6.2.2 Weather Forecasting6.2.3 Precision Farming6.2.4 Farm Machinery Automation6.2.5 Crop Growth Assessment6.2.6 Others6.3 Livestock and Aquaculture6.3.1 Animal Growth Monitoring6.3.2 Animal Health Monitoring6.3.3 Others

7 Global Artificial Intelligence in Agriculture Market (by Region), $Million7.1 Market Overview7.2 North America7.3 Europe7.4 Asia-Pacific7.5 Rest-of-the-World (RoW)

8 Company Profiles8.1 OverviewPublic CompaniesExisting Market Players8.2 Alibaba Group Holding Limited8.3 AgEagle Aerial Systems Inc.8.4 BASF SE8.5 The Climate Corporation (a Bayer AG Company)8.6 Deere & Company8.7 IBM Corporation8.8 JD.com, Inc.8.9 Microsoft Corporation8.10 Robert Bosch GmbH8.11 SAP SEEmerging Market Players8.12 AGCO Corporation8.13 Capgemini SE8.14 Cargill, Inc.8.15 CNH Industrial N.V.8.16 Iteris, Inc.8.17 Lindsay CorporationPrivate PlayersExisting Market Players8.18 Connecterra B.V.8.19 Descartes Labs, Inc.8.20 Gamaya SA8.21 Granular Inc.8.22 Harvest Croo Robotics, LLC8.23 PrecisionHawk Inc.8.24 Prospera Technologies Ltd.,8.25 Root AI, Inc.8.26 SZ DJI Technology Co. Ltd8.27 VineViewEmerging Market Players8.28 Abundant Robotics Inc.8.29 Aquabyte, Inc.8.30 aWhere Inc.8.31 Ceres Imaging Inc.8.32 Delair8.33 ecoRobotix Ltd.8.34 Farmers Edge8.35 Taranis Ag8.36 XAG Co., Ltd.

For more information about this report visit https://www.researchandmarkets.com/r/tl0t4t

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Global Artificial Intelligence (AI) in Agriculture Market Study (2019-2024): Set to Exhibit a CAGR of 28.38% During the Forecast Period -...

AI2 CEO Oren Etzioni spakes at the Technology Alliances AI Policy Matters Summit. (GeekWire Photo / Monica Nickelsburg)

Artificial intelligence is here, its just the beginning, and its time to start thinking about how to regulate it.

Those were the takeaways from the Technology Alliances AI Policy Matters Summit, a Seattle event that convened experts and government officials for a conversation about artificial intelligence. Many of those experts agreed that the government should start establishing guardrails to defend against malicious or negligent uses of artificial intelligence. But determining what shape those regulations should take is no easy feat.

Its not even clear what the difference is between AI and software, said Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, on stage at the event. Where does something cease to be a software program and become an AI program? Google, is that an AI program? It uses a lot of AI in it. Or is Google software? How about Netflix recommendations? Should we regulate that? These are very tricky topics.

Regulations written now will also have to be nimble enough to keep up with the evolving technology, according to Heather Redman, co-founder of the venture capital firm, Flying Fish Ventures.

Weve got a 30-40 year technology arc here and were probably in year five, so we cant do a regulation that is going to fix it today, she said during the event. We have to make it better and go to the next level next year and the next level the year after that.

With those challenges in mind, Etzioni and Redman recommend regulations that are tied to specific use cases of artificial intelligence, rather than broad rules for the technology. Laws should be targeted to areas like AI-enabled weapons and autonomous vehicles, they said.

My suggestion was to identify particular applications and regulate those using existing regulatory regimes and agencies, Etzioni said. That both allows us to move faster and also be more targeted in our application of regulations, using a scalpel rather than a sledgehammer.

He believes the rules should include a mandatory kill switch on all AI programs and requirements that AI notify users when they are not interacting with a human. Etzioni also stressed the importance of humans taking responsibility for autonomous systems, though it isnt clear whether the manufacturer or user of the technology will be liable.

Lets say my car ran somebody over, he said. I shouldnt be able to say my dog ate my homework. Hey I didnt do it, it was my AI car. Its an autonomous vehicle. We have to take responsibility for our technology. We have to be liable for it.

Redman also sees the coming tide of A.I. regulation as a business opportunity for startups seeking to break into the industry. Her venture capital firm is inundated with startups pitching an A.I. and M.L. first approach but Redman said there are two other related fields, or stacks as she describes them, that companies should be exploring.

If you talk to somebody on Wall Street, they dont care what tech stack theyre running their trading on theyre looking at new evolutions in law and policy as big opportunities to build new businesses or things that will kill existing businesses, she said.

From a startup perspective, if youre not thinking about the law and policy stack as much as youre thinking about the tech stack, youre making a mistake, Redman added.

But progress toward a regulatory framework has been slow at the local and federal level. In the last legislative session, Washington state almost became one of the first to regulate facial recognition, the controversial technology that is pushing the artificial intelligence debate forward. But the bill died in the state House. Lawmakers plan to introduce data privacy and facial recognition bills again next session.

Redman said shes disappointed Washington state wasnt a first-mover on AI regulation because the company is home to two of the tech giants consumers trust most with their data: Amazon and Microsoft. Amazon is in the political hot seat along with many of its tech industry peers but the Seattle tech giant has not been implicated in the types of data privacy scandals plaguing Facebook.

We are the home of trusted tech, Redman said, and we need to lead on the regulatory frameworks for tech.

Excerpt from:

Tech experts agree its time to regulate artificial intelligence if only it were that simple - GeekWire

In March 2018, an Uber self-driving car killed for the first time: It did not recognize a pedestrian crossing the road. COMPAS, a machine-learning-based computer software system assisting judges in 12 courts in the US, was found by ProPublica to have a harmful bias. It was discriminating between black and white people, suggesting to judges that the former were twice as likely to commit another crime than the latter and recommending longer detention periods for them before trial. I could continue with more examples of how AI can become harmful.

Enterprises are infusing their enterprise applications with AI technology and building new AI-based digital experiences to transform business and accelerate their digital transformation programs. But there is a chance that all these positives about AI could end, especially if we continue to see examples like this of delivering poor-quality, untested AI or AI that's not adequately tested for businesses and consumers.AI-infused applications are applications made ofa mix of "automatic software" -- the software we all have been building for years that is deterministic -- and autonomous software, or software that is nondeterministic with learning capabilities. AI-infused apps see, listen, speak, sense, execute, automate, make decisions, and more.

And as AI becomes more autonomous, the risk of these systems not being tested enough increases dramatically. Until humans are in the loop, there is hope that their bugs will be mitigated by humans making the right decision or taking the right action, but once they are out of the loop, we are in the hands of this untested, potentially harmful software.

Since AI-infused applications are a mix of automatic and autonomous software, to test an AIIA involves testing more than the sum of all its parts with all its interactions. The good news is testers, developers, and data scientistsknow how to test 80% of AIIAsand can use conventional testing tools and testing services companies that are learning to do so; the bad news is there are areas of AIIAs that we don't know how to test:In a recent report, I call this "testing the unknown," and an example of "testing the unknown" happens when the AI generates the new experience. To test an AI-generated experience, we can't predefine a test case as we would do for deterministic automatic software. Intrigued?

This post was written by VP, Principal Analyst Diego Lo Giudice, and originally appeared here.

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Don't put your AI initiatives at risk: Test your AI-infused applications! - ZDNet