LITTLETON, Colo. -- A group of MIT researchers visited Lockheed Martin this month for a chance to talk about the future of artificial intelligence and automation.

Liz Reynolds is the executive director of the MIT Task Force on the Work of the Future and says her job is to focus on the relationship between new technologies and how they will affect jobs.

Colorado is at the forefront of thinking about these things, Reynolds said. All jobs will be affected by this technology.

Earlier this year, U.S. Sen. Michael Bennet, D-Colo., created an artificial intelligence strategy group to take a closer look at how AI is being used in the state and how that will change in the future.

We need a national strategy on AI that galvanizes innovation, plans for the changes to our workforce, and is clear-eyed about the challenges ahead. And while were seeing progress, workers and employers cant wait on Washington, said Sen. Bennet in a statement. Colorado is well-positioned to shape those efforts, which is why weve made it a priority to bring together Colorado leaders in education, business, nonprofits, labor, and government to think through how we can best support and train workers across Colorado so they are better prepared for a changing economy."

MIT recently released a 60-page report detailing some of the possibilities and challenges with AI and automation.

One of the major challenges the group is considering is how the technology will affect vulnerable workers, particularly people who do not have a four-year degree.

The MIT team is looking for ways to train those workers to better prepare them for the changes.

Were not trying to replace a human, thats not something youre ever going to do with eldercare. For example, youre going be looking for ways to use this technology to help, Reynolds said.

Despite recent advances in AI, Reynolds believes the changes to the workforce will happen over a matter of decades, not years.

We think its going to be a slower process and its going to give us time to make the changes that we need institutionally, she said.

Beyond that, projections suggest that, with an aging workforce, there will be a scarcity of people to employ in future and technology can help fill some of those gaps.

The bigger question is how to ensure that workers can get a quality job that results in economic security for their families.

I think theres really an opportunity for us to see technology not as a threat but really, as a tool, Reynold said. If we can use the right policies and institutions to support workers in this transition then we could really be working toward something that works for everyone.

Lockheed Martin has been using artificial intelligence and automation in its space program for years. The companys scientists rely on automation to manage and operate spacecrafts that are on missions.

However, the technology is also being applied closer to home. The AI Lockheed Martin has created is already being applied to peoples day-to-day lives, from GPS navigation to banking. Now, the company is looking for more ways to make use of it.

Even though its been around for some time, we want to think about how we can use it in different, emerging ways and apply it to other parts of our business as well, said Whitley Poyser, the business transformation acting director for Lockheed Martin Space.

One of the areas in particular Lockheed Martin is looking to apply the technology is in its manufacturing, not only to streamline processes but to use data the machines are already collecting to predict potential issues and better prepare for them.

Poyser understands that there are some fears about this technology taking over jobs, but she doesnt believe thats the case.

Its not taking the job away, its just allowing our employees to think differently and think about elevating their skills and their current jobs, Poyser said. Its actually less of a fear to us and more of an opportunity.

The true potential of artificial intelligence is only beginning to be unleashed for companies like Lockheed Martin. Reynolds is hoping that predicting for the possibilities and challenges now will help the country better prepare for the changes in the decades to come.

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Colorado at the forefront of AI and what it means for jobs of the future - The Denver Channel

2020 is going to be a big year for artificial intelligence, that is.

At least, that was the message LivePerson CEO Robert LoCascio delivered to CNBC's Jim Cramer on Friday.

"When we think about 2020, I really think it's the start of everyone having [AI]," LoCascio said on "Mad Money." "AI is now becoming something that's not just out there. It's something that we use to drive everyday life."

LivePerson, based in New York City, provides the mobile and online messaging technology that companies use to interact with customers.

Shares of LivePerson closed up just more than 5% on Friday, at $38.32. While it sits below its 52-week high of $42.85, it is up more than 100% for the year.

It reported earnings last week, with total revenue at $75.2 million for the third quarter, which is up 17% compared with the same quarter in 2018.

More than 18,000 companies use LivePerson, including four of the five largest airlines, LoCascio said. Around 60 million digital conversations happen through LivePerson each month, he said.

"You can buy shoes with AI on our platform. You can do airlines. You can do T-Mobile, change your subscription with T-Mobile," he said. "That's the stuff in everyday life."

The world has entered a point where technology has transformed all aspects of communication, LoCascio said.

"Message your brand like you message your friends and family," he said, predicting a day where few people want to pick up the phone and call a company to ask questions. "We're powering all that ... for some of the biggest brands in the world."

LoCascio said LivePerson, which he founded in 1995, now uses AI to power about 50% of the conversations on its platform.

"We're one of the few companies where it's not a piece of the puzzle. It's the entire puzzle," he said.

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Artificial intelligence has become a driving force in everyday life, says LivePerson CEO - CNBC

Have you ever commiserated with your colleagues that your boss acts like an automaton?

This soon might be more than just a figure of speech and some employees don't necessarily think that would be a bad thing.

By 2030, up to 800 million workers around the world could be replaced by machines. The fear of rampaging robots isnt just restricted to jobs. Leaders in emerging technology, such as Elon Musk, have suggested artificial intelligence (AI) is a fundamental risk to the existence of civilization.

But a new survey shows some workers have much friendlier views toward AI. Oracle and Future Workplace found 82% of workers believe robot managers are better at certain tasks such as maintaining work schedules and providing unbiased information than their human counterparts.

And almost two-thirds (64%) of workers worldwide say they would trust a robot more than their human manager. In China and India, that figure rises to almost 90%.

Almost two-thirds of workers worldwide trust a robot manager more than a human manager.

Image: Oracle/Future Workplace

The use of robotics in Asia is growing rapidly. Sales of industrial robots in India jumped by 39% in a year, while China is aiming to become one of the worlds most automated nations by 2020.

The implementation of AI technology, including robots, is expected to add as much as $15.7 trillion to the global economy by 2030. Automating routine tasks and administration will free employees up to focus on more complex work, while product development will become more agile as machines learn rapidly about what customers want.

The research recognizes that robots can bring complementary skills to the workplace. More than half of those surveyed by Oracle/Future Workplace say they're excited about having robot co-workers. Millennials are particularly enthusiastic.

There is growing enthusiasm for human-robot work partnerships.

Image: International Federation of Robotics

Our workplaces are changing and not necessarily for the worse. A World Economic Forum report on the future of work suggests that while 75 million jobs may be lost to automation by 2022, another 133 million additional new roles will be created.

The World Economic Forum was the first to draw the worlds attention to the Fourth Industrial Revolution, the current period of unprecedented change driven by rapid technological advances. Policies, norms and regulations have not been able to keep up with the pace of innovation, creating a growing need to fill this gap.

The Forum established the Centre for the Fourth Industrial Revolution Network in 2017 to ensure that new and emerging technologies will helpnot harmhumanity in the future. Headquartered in San Francisco, the network launched centres in China, India and Japan in 2018 and is rapidly establishing locally-run Affiliate Centres in many countries around the world.

The global network is working closely with partners from government, business, academia and civil society to co-design and pilot agile frameworks for governing new and emerging technologies, including artificial intelligence (AI), autonomous vehicles, blockchain, data policy, digital trade, drones, internet of things (IoT), precision medicine and environmental innovations.

Learn more about the groundbreaking work that the Centre for the Fourth Industrial Revolution Network is doing to prepare us for the future.

Want to help us shape the Fourth Industrial Revolution? Contact us to find out how you can become a member or partner.

Those new roles as well as stable occupations such as human resources specialists and university lecturers are likely to play on our creativity and ability to empathize with colleagues.

AI is redefining not only the relationship between worker and manager, but also the role of a manager in an AI-driven workplace, says Dan Schawbel, Research Director at Future Workplace.

Managers will remain relevant in the future if they focus on being human and using their soft skills, while leaving the technical skills and routine tasks to robots.

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World Economic Forum articles may be republished in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Two-thirds of employees would trust a robot boss more than a real one - World Economic Forum

Technology has found a way to seep into every nook and cranny of the human experience. Throughout this digital shift, there has been one universal language that moves society toward an upward trajectory and lets humans live in an easier manner: coding.

Computer science is an increasingly important area of study, as the digital and physical world blends seamlessly together, like a beautifully choreographed dance. But like all things that humans touch, the mathematical sequences that create our digital existence are flawed by the systems of inequality that thrive within the physical world.

Each person, regardless of their background, has inherently adopted a set of subconscious biases. The prejudice that each person holds can come across in both superficial and significant ways, and when an engineer is creating an algorithm for a new system of artificial intelligence, those biases will undoubtedly affect the outcome.

This is not to say that humans are intentionally or deliberately ingraining their biases into these systems, but traces of their preferences break through from the data that is fed into these new creations.

Take, for instance, the AI software ImageNet.

In September, thousands of people uploaded their photos to a website called ImageNet Roulette, which used the AI software to analyze a persons face and describe what it saw.

This seemingly amusing game churned out a plethora of responses from nerd to nonsmoker. However, when Tabong Kima, a 24-year-old African American man, uploaded his smiling photo, the software analyzed him as an offender and wrongdoer, according to the New York Times.

To add insult to injury, the software also analyzed the man with him, another person of color, a spree killer.

At first glance, some may write this flawed social media trend as unimportant in the grand scheme of things, but that is far from the truth.

ImageNet is one of many data sets that have been extensively used by tech giants, start-ups and academic labs when they train new forms of artificial intelligence. This means that any flaws in this one data set, such as the racist labelling in Kimas case, have already spread far and wide throughout the digital realm of existence.

As engineers increasingly drift toward the production of AI software, with the goal of lifting tedious responsibilities from the shoulders of busy individuals, it is important to ensure the footprint of systemic inequality that has historically permeated throughout the world does not find a way into the powerful realm of software.

Software has progressively impacted each persons ability to thrive in the world. Whether thats through applying for credit cards or jobs, there continues to be less of a hands-on approach when these applications are reviewed.

This past week, Apple came under fire for their credit cards alleged sexist algorithms.

The conversation first surfaced when David Hansson, a prominent software engineer, tweeted about the issues he and his wife were having with Apples credit card.

The @AppleCard is such a f sexist program. My wife and I filed joint tax returns, live in a community-property state, and have been married for a long time. Yet Apples black box algorithm thinks I deserve 20x the credit limit she does. No appeals work, Hansson tweeted.

Not long after, Apples co-founder Steve Wozniak weighed in on the issue, stating that he and his wife experienced a similar issue with the Apple credit card.

To explain simply, a black box algorithm is a system where the inputs and outputs can be viewed by an observer, but without any knowledge of the internal system works. Meaning that although theres an output of data, no one knows how the system created that information.

In response to the deeply unsettling prospect of Apple limiting users in a way which hints at sexist black box practices, Sen. Ron Wyden (D-Oregon) tweeted, The risks of unaccountable, black box algorithms shouldnt be underestimated. As companies increasingly rely on algorithms to handle life-changing decisions and outcomes, federal regulators must do their part to stamp out discrimination before its written into code.

Goldman Sachs, the financial company that handles the credit limit for Apple Cards, has denied the use of black box algorithms, an even more important issue has surfaced from their response.

Even if black box algorithms are not in place, and a credit card application is not explicitly asking for the applicants gender, these refined machine-learning algorithms can still analyze the information its being fed and describe what gender they are analyzing. This is then applied to determine credit limits.

For instance, the machines could learn that applicants who have credit cards open at a particular womens clothing store are a bad financial risk. It could then provide lower credit limits for those who carry these cards, which results in women receiving lower credit limits than men, according to Forbes Magazine.

Another instance of gender-biased algorithms occurred in 2018, after Amazon engineers created an AI engine with the sole purpose of vetting through over 100 resumes to help choose the top candidates to be hired.

The tech giant realized that the engine was not rating women software engineer applicants in a fair way, because the resume patterns that the engine had been taught to replicate illustrated the stark gender gap within the tech industry.

In a male-dominated industry, Amazons system taught itself that male applicants were preferred over women.

Even after the engineers reprogrammed the system to ignore explicitly gendered words, like womens, the system still picked up on implicitly gendered words and used that to rate its applicants.

As these new systems are trained to learn from historical decisions made by humans, it must come as no surprise that the race and gender-based inequality that has plagued society for so long has now found a new home within the digital realm.

Discrimination is entangled in our private lives, thats just the truth. The tango of privilege continues to strut across all facets of human existence, and as this experience dives deeper within the world of artificial intelligence, engineers must ensure that they are not dipping further into the discrimination that minority communities have historically been shown.

Were all beginning to understand better that algorithms are only as good as the data that gets packed into them, said Sen. Elizabeth Warren (D-MA) in an interview with Bloomberg News. And if a lot of discriminatory data gets packed in, in other words, if thats how the world works, and the algorithm is doing nothing but sucking out information about how the world works, then the discrimination is perpetuated.

Theres no easy fix to this problem. The way in which bias affects the livelihood of individuals, and how to combat that in a fair manner, has long been a question for social scientists and philosophers. Expanding that issue into technology, where concepts have to be defined in mathematical terms, illustrates the hard work that must be done to create a truly fair digital environment.

While fixing these computing errors will rely on an extreme amount of trial and error, its the responsibility of software engineers to ensure that these new technologies will not cause more harm and discrimination toward people.

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Artificial intelligence engines have implicit biases - The Daily Titan

Project Cortex will let partners build knowledge networks for customers.

Microsoft intends to significantly simplify how people find organizational information through internal knowledge networks. Its via new technology set to appear next year, revealed as Project Cortex.

If Project Cortex works as Microsoft envisions, it will streamline, or in many cases eliminate, the need for individuals to interrupt their work to search for internal information. Project Cortex, introduced at this months Microsoft Ignite conference in Orlando, Florida, was a stealth effort in the works for more than two years.

Project Cortex also promises to give Microsofts vast partner ecosystem much to digest going into 2020, particularly as customers look to utilize it to enhance their collaboration and employee productivity efforts and Microsoft begins offering training and enablement for partners.

Company officials described Project Cortex as the first major new productivity and collaboration service planned for Microsoft 365 since the launch of Microsoft Teams three years ago. Microsoft CEO Satya Nadella underscored the notion of bringing more seamless access to knowledge in context of whatever application a user is working in is a critical next step forward for the Microsoft 365 platform.

Project Cortex takes what is data today inside of your organization and converts it into knowledge, Nadella said during his Ignite keynote session.

Under development by the Microsoft SharePoint team alongside adjacent Office groups including Teams, Yammer and Outlook, Project Cortex uses the companys AI, advances in Microsoft Search and its Graph APIs to create organizational knowledge networks that will enable customers to automatically surface collective information.

Project Cortex is scheduled to appear in Office, Dynamics and other connected applications in the first half of 2020. Its not a product itself; rather, Project Cortex will apply its advances in artificial intelligence (AI) and machine learning, drawing on the Microsoft Graph APIs.

A topic card with resources appears when a user hovers over an acronym.

Using the Microsoft Graph, individuals will be able to find organizational information contextually, without switching applications. For example, Microsoft demonstrated at Ignite how a user can scroll over an acronym in any Office app to get a definition of what that acronym means through a topic card that appears without interrupting the readers flow, which inevitably occurs when searching for it in some other application.

You now have AI-driven topics, so essentially you have AI creating topic wikis inside the enterprise, Nadella said. You have that ability now, in the context of any document or any email. More interestingly, you can find the experts who know something about that acronym, and you can really integrate and interact with them.

Project Cortex will automatically use metadata that resides in distributed repositories in what Microsofts SharePoint team described as the next generation of core enterprise content management (ECM) services.

Chris McNulty, Microsofts senior product manager for Project Cortex, who works on the SharePoint team, underscored that Project Cortex is not a new platform, but rather building on the existing Microsoft 365 and SharePoint platforms.

Were able to reach left to right across the suite to be able to invoke the best on top of our platform [by] doing things with security, doing things with compliance [and] doing things with search, McNulty said during an Ignite session that provided an overview of Project Cortex.

McNulty noted that all of these capabilities will bring together and invoke the metadata within those specific services. For instance, with Microsoft Information Protection, Project Cortex will extract key metadata that can attach and invoke policies based on the value of a contract, he said.

Microsofts Seth Patton earlier this month at Ignite 2019.

Seth Patton, general manager of Microsofts Office 365 marketing group, said during a briefing at Ignite that the company spent time with 25 customers in the development of Project Cortex, and 13 organizations are testing it a private technical preview. Patton said Project Cortex is centered around a common problem facing organizations knowledge drain the consequence of those with expertise leaving an organization.

That knowledge is walking out the door, Patton said.

While acknowledging thats a problem that has long faced all organizations, Patton said the need for making internal knowledge more readily available with the appropriate controls to protect it is becoming more critical. Whats driving that acceleration, he noted, is

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Microsoft Aims to Bring AI to Mainstream Collaboration with Project Cortex - Channel Futures

Artificial intelligence has helped archaeologists uncover an ancient lost work of art.

The Nazca Lines in Peru are ancient geoglyphs, images carved into the landscape. First formally studied in 1926, they depict people, animals, plants, and geometric shapes. The formations vary in size, with some of the biggest running up to 30 miles long. Their exact purpose is unknown, although some archaeologists think they may have had religious or spiritual significance. Local guides believe the lines relate to sources of water.

Some Nazca lines span miles of Peruvian countryside. Flickr/Christian Haugen

New geoglyphs are still being discovered and can be hard to spot due to changes in the landscape, with natural erosion and urbanization breaking them up.

A research team from Yamagata University recently announced it had discovered 142 new Nazca formations, including images of birds, monkeys, fish, snakes, and foxes.

The team partnered with IBM to try and train its deep-learning platform Watson to look for hard-to-find geoglyphs.

They fed the AI with aerial images to see if it could spot any more Nazca outlines. Watson threw up a few candidates, from which the researchers picked the most promising. Sure enough, their field work confirmed the AI had found an ancient Nazca artwork.

The find was a relatively small depiction of a humanoid figure spanning just 16 feet. The researchers estimate the figure dates from roughly 100 BC to AD 100, making it at roughly 2,000 years old.

The project's success has prompted Yamagata University to announce a more prolonged partnership with IBM, and will create a full location map of the geoglyphs to help future archaeologists.

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Scientists used IBM Watson to discover an ancient humanoid stick figure - Business Insider

Although AIs are entering new areas every day, a handful of AI laboratories that still focus on artificial intelligence are still consuming large amounts of cash and have made not much progress on AI.

According to the documents submitted to the UK Companies Registry in August, only the Alphabet-owned AGI Lab DeepMind lost $570 million in 2018 alone. Another AI Lab, OpenAI, which aims to create AGI, had to abandon its non-profit organisation to find investors in its expensive research. Both labs have achieved extraordinary success, including the creation of robots that can play complex board games and video games. But they are still far from creating artificial intelligence.

A survey by MIT Sloan Management Review and BCG showed that 90 per cent of companies are currently investing in artificial intelligence technology. But less than 40 per cent of companies were able to sense and calculate the economic benefits of implementing AI, while the rest noted that new technologies did not affect the business, or the impact was minimal. According to experts, not all enterprises have learned how to correctly use AI in their business areas, it is also incorrect to consider AI as a means of reducing costs. Successful companies integrate AI into the overall business development strategy; for them, this is one of the tools to increase revenue, rather than save.

Georgy Lagoda, the Deputy CEO of Software Product Group, In his report at CNews Forum 2019 answered the question of why companies continue to invest and develop artificial intelligence technologies, and also spoke about the application of the latest AI trends in various Russian government and business fields.

According to George, the use of AI is necessary for the field of Big Data. Over the past 2 years, about 95 per cent of all world data was generated, while 90 per cent of the data are not structured and fragmented. In this regard, in the era of the digital economy, there is a need to implement AI-based solutions, without which the processing of a huge amount of data becomes impossible.

In 2019, based on business needs, the Hype Gartner cycle included 8 new and previously known technologies in the artificial intelligence class: cloud AI services, AutoML, augmented intelligence, Explainable AI, smart devices, reinforcement learning, quantum computers, marketplaces with AI. One of the most sought-after trends of this year is speech recognition and synthesis. George cites Amazon Alexa and Yandex Alice as an example, as well as Vera Voice technology, which allows synthesising celebrity speech.

Furthermore, George mentioned the agreement between Gazprom Neft and Saudi Aramco. The companies will now work together to create and use AI and neural networks to refine the hydrodynamic models of the fields.

In Russian banking sector, there is also a widespread introduction of AI: the appearance of chatbots, robot operators in call centres. The largest bank in Russia - Sberbank, plans to introduce smart machines to help operators with coin counting, loading operations, etc.

In the field of Russian education at the moment there are several problems. For example, in each class, there are laggards and those to whom the material seems fairly easy. It is assumed that the use of adaptive technologies will help in solving this issue. AI will monitor the progress of each student and inform the teacher about the levels of assimilation of various materials. The speaker of the Software Product also spoke about another application of the latest technologies in the field of education - the introduction of AI in testing USE tests. AI worked over a million-student work and, based on handwriting analysis, found tests written by different people.

George also spoke about the use of AI in the field of the Internet of things and announced the economic effect of the introduction of the SOVA PAK for writing fines and controlling parking lots. For the first year of work in one of the regions of Russia, the complex allowed to significantly reduce labour costs, saving about 500 million rubles. from the budget.

According to experts, technological capabilities are widely used in the Russian industrial market, especially in the field of instrumentation, engineering and aircraft manufacturing. Georgy cites an example of a system for automatic photo and video recording of violations and neglect of industrial safety. The solution allows you to increase the safety of production processes by instantly responding to dangerous situations and informing workers.

Then he drew the attention of listeners to the use of AI in the field of Russian government information security. Recently, cybercriminals have begun to use new technologies in the development of malware, which makes it increasingly difficult for anti-virus systems to detect threats. To combat cybercrime, IT developers began to implement the machine learning function in software products for the most in-depth analysis of malware. George cited Wallarm as an example, which develops solutions with a machine learning feature for firewalling. Classical security systems use a common set of static signatures, as a result of which, during the intrusion, a huge number of IP addresses are blocked. Unlike them, the Wallarm platform analyses traffic using AI and then blocks individual packets, i.e. specific attacker.

As for AI in the public sector, the speaker cited the Moscow Information Technology Department as an example, in which a huge number of information security events are generated every minute. When parsing such a data stream by a person, errors and omissions of dangerous incidents are not ruled out. Therefore, for the efficiency of processing such data, software solutions are introduced that, with the help of machine learning, can analyse and segment events, as well as respond to threats with high speed.

There is a risk in everything but the fact that AI is progressing continuously, and that AI is the future of the Internet, computing, etc. AI will strengthen the Internet of Things and IoT is expected to increase exponentially over the next 2 years. Simply put, there are currently about 23 billion connected devices, and this number will increase to over 75 billion, which is more than 200 per cent by 2025. All adds up to a YES, it is worth investing in AI.

However, there remains an important question of what part of AI to invest in? Then wait for our upcoming articles.

Amin Haqshanas, Sophomore in Mechatronics Engineering, Web Designer/Developer, Zinniagroup

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Is it worth investing in artificial intelligence? - ITProPortal

Artificial intelligence (AI) is more than just cyborgs in movies trying to destroy humanity. It has actual real-world applications that can make our lives better and our businesses stronger and more profitable. In retail, artificial intelligence is being adopted rapidly - between 2016 and 2018 there was a 600% increase in adoption. Unfortunately the adoption rate is still relatively low, ranging from 26% for home improvement stores to 33% for apparel and footwear. If AI can make such a big difference, why isnt everyone adopting it?

Currently, only 15% of companies are saying they are spearheading AI adoption. Only 25% of large retailers are investing up to 10% of their capital in artificial intelligence systems, while all other size retailers are spending just 7%. Implementation of AI is costly, but those who use it are finding the benefits far outweigh the costs.

Customer service is one of the most successful use-cases for artificial intelligence yet. Customer-facing AI can improve customer satisfaction by 9%, reduce customer complaints by 8%, and can lower customer churn by 5%, all in the early stages. As this technology improves, it can help even more with customer satisfaction. Currently, voice technology is being programmed that can emulate and mirror human emotions better, mimicking empathy and deescalating tense customer service situations. But even just getting people to the right customer service representative when they call a call center can help get customers the right remedy to their situation.

Chatbots and virtual assistants are an extension of this technology that are used to assist customers who arent having a problem. They can help assist customers in finding the right item to buy, finding new items to try, and learning more about what they are buying. In fashion, AI and chatbots are being used to help customers find new clothing looks based on what they like. In the spirits business, Brown-Forman has a Whiskey Whisperer that can help customers learn more about whiskey, find new products, and find cocktail recipes to try.

AI is also helping to streamline the shopping experience. In Amazon Go stores, cameras and AI mean that customers can take items off the shelves, bag them and walk out of the store, saving time in the checkout line, as the system rings things up as you go and automatically charges you as you walk out the door.

Throughout the supply chain, AI can be used to streamline operations. In production, AI can be used to forecast orders, schedule workers optimally, and even create a schedule that will utilize electricity more efficiently. In shipping, AI can help ensure trucks are completely full so empty space isnt being shipped. It can also optimize shipping routes to save on time and fuel. In retail, AI can be used to optimize ordering so that valuable inventory isnt sitting around collecting dust.

Artificial intelligence is often portrayed as something that is going to destroy mankind in the movies, but in reality most of the real-life applications are pretty mundane and most are actually beneficial. As AI is adopted in more aspects of the retail landscape, there will certainly be bumps in the road. But the end product will be a stronger, more agile sector of the economy that serves both customers and businesses better. Learn more about the future of retail with AI from the infographic below!

Read next: From Science Fiction To Reality With Artificial intelligence (infographic)

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The Future Of Artificial Intelligence In Retail - Digital Information World

Speaking at BlockShow Asia 2019, Todalarity CEO Toufi Saliba posed a hypothetical question to the audience: How many people would take a pill that made you smarter, knowing they can be controlled by a social entity?

No one raised their hand, and he was unsurprised.

Thats the response that I get, zero percent of you, he continued. Now imagine at the same time the pill has autonomous decentralized governance so that no one can control or repurpose that pill but the host yourself.

This time hands were raised in abundance. Decentralized governance represents a necessary step for the tech community to build up a trust in digital developments related to securely managing big data.

Economics and ethics can go together thanks to decentralization, commented SingularityNET CEO Ben Goertzel.

But does the decentralized governance represent a step forward from centralization, or it is just an illusion of evolution? Cole Sirucek, co-founder of DocDoc, shared his vision:

It is when we are at a point of centralizing data that you can begin to think about decentralization. For example, electronic medical records: in five years the data will be centralized. After that, you can decentralize it.

Goertzal didnt fully agree: I dont think it is intrinsic. The reason centralized systems are simpler to come by is how institutions are built right now. There is nothing natural about centralization of data. He elaborated on the mutual dependence of two important technologies:

Blockchain is not as complex as AI, but it is a necessary component of the future. Without BTC, you dont have means of decentralized governance. AI enables the future, blockchain secures it.

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Artificial Intelligence Will Enable the Future, Blockchain Will Secure It - Cointelegraph

Recently, Hisham Daoud and Magdy Bayoumi of the University of Louisiana at Lafayette have introduced a completely newArtificial Intelligence (AI) system that predicts epilepsy seizures. According to the World Health Organizations reports, around 50 million people around the world are suffering from epilepsy and 70% of those patients can control the seizures through medications.

The new AI technology shows 99.6% accurate results, and the best thing about it is that it predicts the attacks an hour before it happens. In this way, the patient can gear up for it and take medications that can prevent its occurrence. Having enough time to control the attack is what a patient needs.

Some might be thinking that its the perfect cure and prediction strategy for the epilepsy patients, but its not; however, it is definitely something big in the market. Right now, there are many other options as well that does the work, for instance, electroencephalogram (EEG) tests to evaluate the brain activity to predict the condition.

This new AI technology performs both EEG tests and applies predictive models simultaneously to predict seizures. The deep learning algorithms in the technology allows predicting the electrical channels lightning that happens only during a seizure.

Currently, this technology is not available worldwide and there is some time before it does so. Right now, the team of this technology is busy working on a custom chip that can make the process further easier.

It is definitely good news for epilepsy patients and more improvement in the technology will only bring good outcomes for them.

Source: IEEE spectrum / IEEE.

Read next: Effects of Social Media on the Mental Health of Young People!

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Epileptic Seizure Prediction becomes much easier with the new Artificial Intelligence technology - Digital Information World

Rong Luo, CFO of TAL Education Group, Doranda Doo, SVP of iFLYTEK Co. Ltd. and Song Zhang, Managing Director of Thoughtworks China on Day 2 of CNBC East Tech West at LN Garden Hotel Nansha Guangzhou on November 19, 2019 in Nansha, Guangzhou, China.

Zhong Zhi | Getty Images News | Getty Images

The technology industry and policymakers need to address public concerns about artificial intelligence (AI) which are "not the fault of AI" itself, a tech executive said Tuesday.

"It is the fault of developers, so we need to solve this problem," said Song Zhang, managing director for China at global software consultancy, ThoughtWorks.

Consumer worries relating to AI include concerns about personal privacy and how the systems may get out of control, said Zhang during a panel discussion discussing the "Future of AI" at CNBC's East Tech West conference in the Nansha district of Guangzhou, China.

It is the duty of the tech industry and policymakers to focus on, discuss and solve such problems, said Zhang in Mandarin, according to a CNBC translation. Indeed, while consumers are curious about AI when they first come into contact with the technology, their mindset changes over time, said Rong Luo, chief financial officer of TAL Education Group.

"The first phase is everyone finds it refreshing, they like something new, they want to give it a try," said Luo.

But "in phase two, people start to care a lot about their privacy, their security," Luo added.

And finally, after "one to two years of adjustments, we (have) now entered phase three, we have a more objective view of the technology. We do not put (it) on the pedestal nor do we demonize it," said Luo.

Panelists at the session acknowledged the potential of AI in various fields such as language translation and education.

"Technology is here to assist them, empower them. We want to free them from those repetitive and meaningless work (tasks) so they have more energy and time for other more creative jobs," said Doranda Doo, senior vice president of Chinese artificial intelligence firm iFlytek.

"So I think what's the most powerful is not AI itself, but people who are empowered by AI," Doo said.

See the rest here:

Public fears about artificial intelligence are 'not the fault of A.I.' itself, tech exec says - CNBC

Updated: May 25, 2018:

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Three Legal Areas to Think About When Using Artificial Intelligence in the Workplace - JD Supra

Intel Corporation(NASDAQ: INTC) revealed its plans for expanding its role in high-performance computing (HPC) artificial intelligence (AI) with new additions to its data-centric silicon portfolio and new software initiative that represents a paradigm shift from the current single-architecture, single-vendor programming models.

HPC and AI workloads demand diverse architectures, ranging from CPUs, general-purpose GPUs and FPGAs, to more specialized deep-learning NNPs, which Intel demonstrated earlier this month, said Raja Koduri, senior vice president, chief architect, and general manager of architecture, graphics and software at Intel. Simplifying our customers ability to harness the power of diverse computing environments is paramount, and Intel is committed to taking a software-first approach that delivers a unified and scalable abstraction for heterogeneous architectures.

oneAPI: A Developer-Centric Approach to Heterogeneous Computing

Intels silicon portfolio has a diverse mix of architectures deployed across various silicon platforms. The foundation of Intels data centric strategy is the Intel Xeon Scalable processor, which today powers over 90 percent of the worlds Top500 supercomputers. At Supercomputing 2019 conference, Intel unveiled a new category of general-purpose GPUs based on Intels Xearchitecture. Code-named Ponte Vecchio, this new high-performance, highly flexible discrete general-purpose GPU is architected for HPC modeling and simulation workloads and AI training.

Intels Data-Centric Strategy Delivers the Foundation for AI/HPC Convergence

Intels data-centric silicon portfolio and oneAPI initiative lays the foundation for the convergence of HPC and AI workloads at exascale within the Aurora system at Argonne National Laboratory.

Aurora will be the first U.S. exascale system to leverage the full breadth of Intels data-centric technology portfolio, building upon the Intel Xeon Scalable platform and using Xearchitecture-based GPUs, as well as Intel Optane DC persistent memory and connectivity technologies.

Original post:

Intel to Expand Role in High-Performance Computing and Artificial Intelligence - Financialbuzz.com

By now, most of us are aware of artificial intelligence (AI) being an increasingly present part of our everyday lives. But, many of us would be quite surprised to learn of some of the skills AI already knows how to do. Here are 13 mind-blowing skills artificial intelligence can already do today.

13 Mind-Blowing Things Artificial Intelligence Can Already Do Today

1. Read

Is one of your wishes to save time by only having to pay attention to the salient points of communication? Your wish has come true with artificial intelligence-powered SummarizeBot. Whether it's news articles, weblinks, books, emails, legal documents, audio and image files, and more, automatic text summarization by artificial intelligence and machine learning reads communication and reports back the essential information. Currently, SummarizeBot can be used in Facebook Messenger or Slack and relies on natural language processing, machine learning, artificial intelligence, and blockchain technologies.

2. Write

Would you believe that along with professional journalists, news organizations such as The New York Times, Washington Post, Reuters, and more rely on artificial intelligence to write? They do, and although this does include many "who, what, where, when, and how" formulaic pieces, AI is able to expand beyond this to more creative writing as well. Many marketers are turning to artificial intelligence to craft their social media posts. Even a novel has even been generated by artificial intelligence that was short-listed for an award.

3. See

Machine vision is when computers can see the world, analyze visual data, and make decisions about it. There are so many amazing ways machine vision is used today, including enabling self-driving cars, facial recognition for police work, payment portals, and more. In manufacturing, the ability for machines to see helps in predictive maintenance and product quality control.

4. Hear and understand

Did you know artificial intelligence is able to detect gunshots, analyze the sound, and then alert relevant agencies? This is one of the mind-blowing things AI can do when it hears and understands sounds. And who can refute the helpfulness of digital voice assistants to respond to your queries whether you want a weather report or your day's agenda? Business professionals love the convenience, efficiency, and accuracy provided by AI through automated meeting minutes.

5.Speak

Artificial intelligence can also speak. While its helpful (and fun) to have Alexa and Google Maps respond to your queries and give you directions, Google Duplex takes it one step further and uses AI to schedule appointments and complete tasks over the phone in very conversational language. It can respond accurately to the responses given by the humans its talking to as well.

6.Smell

There are artificial intelligence researchers who are currently developing AI models that will be able to detect illnessesjust by smelling a human's breath. It can detect chemicals called aldehydes that are associated with human illnesses and stress, including cancer, diabetes, brain injuries, and detecting the "woody, musky odor" emitted from Parkinson's disease even before any other symptoms are identified. Artificially intelligent bots could identify gas leaks or other caustic chemicals, as well. IBM is even using AI to develop new perfumes.

7.Touch

Using sensors and cameras, there's a robot that can identify "supermarket ripe" raspberries and even pick them and place them into a basket! Its creator says that it will eventually be able to pick one raspberry every 10 seconds for 20 hours a day! The next step for AI tactile development is to link touch with other senses.

8.Move

Artificial intelligence propels all kinds of movement from autonomous vehicles to drones to robots. The Alter 3 production at Tokyos New National Theatre features robots that can generate motion autonomously.

9.Understand emotions

Market research is being aided by AI tools that track a persons emotions as they watch videos. Artificial emotional intelligence can gather data from a person's facial expressions, body language, and more, analyze it against an emotion database to determine what emotion is likely being expressed, and then determine an action based on that info.

10. Play games

It's not all serious business with artificial intelligenceit can learn to play games such as chess, Go, and poker (which was an incredible feat)! And, turns out that AI can learn to play these games plus compete and even beat humans at them!

11. Debate

IBMs Project Debater showed us that artificial intelligence can even be successful at debating humans in complex subjects. Not only is it able to research a topic, but it can also create an engaging point of view and craft rebuttals against a human opponent.

12. Create

Artificial intelligence can even master creative processes, including making visual art, writing poetry, composing music, and taking photographs. Google's AI was even able to create its own AI childthat outperformed human-made counterparts.

13. Read your mind

This is truly mind-bogglingAI that can read your mind! It can interpret brain signals and then create speech. Impressive and life-changing for those with speech impairment, but a little bit unnerving when you consider the mind-reading aspect of the skill. It's no surprise that some of the biggest tech giants, including Facebook and Elon Musk have their own projects underway to capitalize on AI's mind-reading potential.

Now imagine if these 13 mind-blowing skills were all combined into one super artificial intelligence! Frightening or exhilarating? Thats up to you to decide, but I think we can all agree, it would be mind-blowing!

View original post here:

13 Mind-Blowing Things Artificial Intelligence Can Already Do Today - Forbes

Read Christopher Tozzi explain the shortcomings of cloud artificial intelligence on IT Pro Portal :

Want to build artificial intelligence (AI) into your app, but dont want to have to collect your own data or train your own models? The cloud has a solution for you. Cloud-based AI services have emerged as one of the hottest service offerings from the major public clouds, which are eagerly inviting developers to make use of them. But while there are good reasons to use cloud AI in some cases, there are also some arguments to be made for avoiding it. In fact, there are so many reasons why AI in the cloud does not seem as beneficial as other types of cloud-based services that I sometimes suspect cloud AI will turn out to be a fad, or at least prove less popular than the cloud providers hope.

Read his full article here.

Related

Link:

Is Cloud AI a Fad? The Shortcomings of Cloud Artificial Intelligence - CTOvision

Albert Einsteins famous expression spooky action at a distance refers to quantum entanglement, a phenomenon seen on the most micro of scales. But machine learning seems to grow more mysterious and powerful every day, and scientists dont always understand how it works. The spookiest action yet is a new study of heart patients where a machine-learning algorithm decided who was most likely to die within a year based on echocardiogram (ECG) results, reported by New Scientist.

The algorithm performed better than the traditional measures used by cardiologists. The study was done by researchers in Pennsylvanias Geisinger regional healthcare group, a low-cost and not-for-profit provider.

Much of machine learning involves feeding complex data into computers that are better able to examine it really closely. To analogize to calculus, if human reasoning is a Riemann sum, machine learning may be the integral that results as the Riemann calculation approaches infinity. Human doctors do the best they can with what they have, but whatever the ECG algorithm is finding in the data, those studying the algorithm cant reverse engineer what it is.

The most surprising axis may be the number of people cardiologists believed were healthy based on normal ECG results: The AI accurately predicted risk of death even in people deemed by cardiologists to have a normal ECG, New Scientist reports.

To imitate the decision-making of individual cardiologists, the Geisinger team made a parallel algorithm out of the factors that cardiologists use to calculate risk in the accepted way. Its not practical to record the individual impressions of 400,000 real human doctors instead of the results of the algorithm, but that level of granularity could show that cardiologists are more able to predict poor outcomes than the algorithm indicates.

It could also show they perform worse than the algorithmwe just dont know. Head to head, having a better algorithm could add to doctors human skillset and lead to even better outcomes for at-risk patients.

Machine learning experts use a metric called area under the curve (AUC) to measure how well their algorithm can sort people into different groups. In this case, researchers programmed the algorithm to decide which people would survive and which would die within the year, and its success was measured in how many people it placed in the correct groups. This is why future action is so complicated: People can be misplaced in both directions, leading to false positives and false negatives that could impact treatment. The algorithm did show an improvement, scoring 85 percent versus the 65 to 80 percent success rate of the traditional calculus.

As in other studies, one flaw in this research is that the scientists used past data where the one-year window had finished. The data set is closed and scientists can directly compare their results to a certain outcome. Theres a differenceand in medicine its an ethical onebetween studying closed data and using a mysterious, unstudied mechanism to change how we treat patients today.

Medical research faces the same ethical hurdles across the board. What if intervening based on machine learning changes outcomes and saves lives? Is it ever right to treat one group of patients better than a control group that receives less effective care? These obstacles make a big difference in how future studies will pursue the results of this study. If the phenomenon of better prediction holds up, it may be decades before patients are treated differently.

Read more:

AI Can Tell If You're Going to Die Soon. We Just Don't Know How It Knows. - Popular Mechanics

Artificial Intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. It is a behind-the-scenes technology that is increasingly being used in day-to-day life. From Siri and Alexa to autonomous vehicles, as well as more complex applications, AI isbeing used in nearly every industry, with customers often unaware of how the technology is helping to meet our ever-demanding expectations.

Many sectors within the superyacht industry are also investing in AI and looking at ways in which it can help design and build the next generation of yachts. Designer Justin Olesinski believes that the technology will play a progressively more important role in superyacht design his design studio has seen the benefits of AI first-hand within its hull development, allowing detailed concept hulls to be produced within a tenth of the time.

With projects becoming larger, more complicated, more expensive, more time consuming and with more options resulting in higher risk, the superyacht industry is a prime candidate to take advantage of the benefits that AI has to offer, explains Olesinski. We believe AI, combined with the experience of everyone involved, can not only help manage the current level of production, but actually allow the market to expand.

We believe AI, combined with the experience of everyone involved, can not only help manage the current level of production, but actually allow the market to expand.

He continues, How many customers are put off with long design development and build timescales, biased opinions and complicated route to owning a superyacht? Intelligently designing AI with the client, not ourselves, in mind, will make buying and owning a superyacht easier. The running, insuring and managing of the yacht in service may be optimised too with AI.

An interactive discussion on the application of AI and Machine Learning (ML) will take place during day two of The Superyacht Forum. Olesinski will be joined by Mike Blake, president of Palladium Technologies, Joseph Adir, founder and CEO of WinterHaven, and Bill Edwards, head of research and development at Olesinski, to share their experiences and discuss how AI can optimise the way superyachts are designed, built and operated.

We will explain how it has helped us, the customer and where we are heading, adds Olesinski. We will also show how diverse AI and ML is by explaining how it can be used in developing corals that are more resistant to warmer sea temperature, increasing salinity and other factors, which may help rejuvenate the Great Barrier Reef. Expect to be surprised, enlightened, intrigued and inspired to question how AI can work for your own business.

Remaining tickets toThe Superyacht Forum 2019are availablehere. Join us from 18-20 November in Amsterdam and enjoy three days of engaging workshops and thought-provoking keynotes,whileinteracting with leading minds from our market to learn about the subjects that you and your clients need to know.

Profile links

Olesinski Limited

If you like reading our Editors' premium quality journalism on SuperyachtNews.com, you'll love their amazing and insightful opinions and comments in The Superyacht Report. If youve never read it, click here to request a sample copy - it's 'A Report Worth Reading'. If you know how good it is, click here to subscribe - it's 'A Report Worth Paying For'.

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How can artificial intelligence improve the superyacht industry? - Superyacht News - The Superyacht Report

Americans dont have to be beholden to the tech Goliaths to get the benefits of artificial intelligence. An alternative possibility is for government to provide the infrastructure needed for a technological future through a public option for artificial intelligence.

Big tech companies have an extraordinary amount of data about how we behave, largely because they engage in widespread surveillance of much of our behavior. Because A.I. depends on data, these companies have a huge market advantage over start-ups and entrepreneurs and its a gap that will only get wider. The lax regulatory environment hasnt helped, either; instead, it has allowed the biggest tech companies to acquire their rivals, stifle competition and snatch up the best software engineers and data scientists. Together, these dynamics make it hard for start-ups, governments and nonprofits to develop and use artificial intelligence without relying on big tech companies, effectively ceding influence over this developing field to a private sphere distorted by anti-competitive practices.

The alternative is a public option for A.I. The public option, a term familiar from debates over health care, is a public program that provides universal access to goods and services, with a private opt-out. A public option for A.I. wouldnt prevent companies like Google from collecting and using data. But it would provide a pathway for start-ups and public-sector organizations to develop abilities and products that would compete with those of the tech giants. Although the Trump administration released an executive order on A.I. this year, we believe that a broader conceptual framework for the public option for A.I. coupled with significant financial resources from Congress is an opportunity for all levels of government to take control of technology for their constituents and engage them deeply in the development of the rules by which it is governed and used.

Our proposal has three components: The first is a public data pool that would make data accessible to registered users. Local, state and federal governments have sizable data resources that would seed this digital commons. Users would be verified to block foreign governments, hackers and others with ill motives from access, and users would be prevented from using the data to engage in racial or other forms of discrimination and for microtargeted advertising.

Some of the data may be very sensitive, and access to those resources would be highly regulated. We can imagine a variety of ways that regulation and technology together could protect privacy and still foster innovation: Data could be anonymized at the source; the commons could have an interface that allowed users to derive insight from the data set, while leaving the underlying information inaccessible; less sensitive data, like weather information, could be made available in a format optimized for training A.I. Whats more, methods for safely sharing A.I. models without disclosing the underlying data are being developed today and could enable users of the data commons to collaborate on public-interest A.I. services. The federal government should also invest in researching new and better ways to protect privacy and prevent misuse.

Second, a public option for artificial intelligence would include a significant increase in research and development spending. Proponents of big tech celebrate private-sector research and are right to do so. But big tech companies, like all companies, have an incentive to fund research that will support their bottom line, and the profit motive doesnt always mean a focus on the most important problems.

For generations, government R&D spending has been one of the central engines of economic growth and technological progress in America. Yet China is projected to spend far more than the United States on A.I. research over the next decade. A sizable increase in research funding for companies, governments and nonprofits developing public-interest technologies would help expand the types of research taking place and give scientists and engineers the option to do groundbreaking work on a broader range of problems.

Third, much of governments A.I. work takes place in the military sector and is applied to national security problems. But health care, transportation, energy and other areas could also benefit significantly from A.I. The federal government should expand its A.I. procurement across all of these sectors as an opportunity to improve public services for all Americans. In addition, the government should ensure that its use of algorithms meets the highest ethical standards.

A public option for A.I. cant solve every problem related to technology and surveillance, and it would require careful thinking about public governance of these programs including a commitment to privacy and awareness of biases. But it would help address the problem of a small number of companies having virtually all power over this technology. It would facilitate the conditions for a competitive market with many players and many new innovations, all while preserving our democratic values and improving our society.

Ben Gansky (@bengansky) is executive director of Free Machine and a researcher and designer at the Institute for the Futures Equitable Futures Lab. Michael Martin is policy director for Free Machine and head of communities at SignalFire. Ganesh Sitaraman (@ganeshsitaraman), a professor of law at Vanderbilt Law School, is a co-author of The Public Option: How to Expand Freedom, Increase Opportunity, and Promote Equality.

Follow @privacyproject on Twitter and The New York Times Opinion Section on Facebook and Instagram.

Visit link:

Artificial Intelligence Is Too Important to Leave to Google and Facebook Alone - The New York Times

(Washington, D.C.) Streamlining multiple targeting sensors to destroy long-range targets, arming forward- positioned robots to penetrate enemy defenses and receiving organized weather-specific terrain mapping from nearby drones - are all emerging combat dynamics increasingly made possible by AI-enabled weapons and technologies.

New applications of AI are consolidating data from otherwise disparate sensor systems, analyzing seemingly limitless amounts of targeting data in seconds and instantly sifting through hours of drone video to massively improve attack options and shorten sensor-to-shooter time.

We are developing an AI stack regarding how we pull together the sensors, computing layer and analytics to manage the data, Col. Doug Matty, Army AI Task Force Deputy Director, told Warrior in an interview.

New algorithms, AI-enabled computer processing and high-speed networking are all specific elements of work now underway with the Armys AI Task Force, an emerging Army effort to collaborate with industry and academia, find technology breakthroughs and develop new applications for AI, Matty explained. The Task Force is now working on prototyping systems for integration onto UH-60 Black Hawk helicopters, Long-Range Precision Fires systems and the Armys emerging fleet of Next-Gen Combat Vehicles, he said.

We are focusing on the Next Gen Combat Vehicle. As you know they are progressing with two different platforms - the optionally-manned vehicle and the robotic combat vehicle. How do we have AI-aided threat recognition? We have to be aware of how robotic and manned vehicles share information to conduct operations, Matty explained.

The first prototype AI-focused integrated threat recognition sensor will be ready by January of next year, Matty said.

The Army Vice Chief of Staff Gen. Joseph Martin told Warrior about some of the prototyping now being done for the Next Generation Combat Vehicle; he said the service is looking at firepower, metallurgical breakthroughs and other emerging systems.

"Weve got a great team there.(on NGCV) Theyre looking at the requirements, theyre starting to work to prototype equipment in the future, and thats going to allow us to arrive at the appropriate solution, just like we did with the Abrams tank back in the mid 70s," Martin said in an interview with Warrior.

"Were looking at new mobile firepower capabilities, were looking at leveraging the technology thats available in the metallurgical development field so that we can have the lightest vehicle possible, but also the most survival vehicle as possible," he added.

The Armys fast-moving AI-task force, based in Pittsburgh at Carnegie Mellon Universitys National Robotics Engineering Center, was first established by the Secretary of the Army in October of 2018.

As part of a dual-pronged strategy to both rapidly advance technology and analyze its impact upon strategy, doctrine and combat tactics, the AI TF is working closely with Army Futures Command and its Cross Functional Teams.

We are working through an initial set of mission analysis with the CFT. We are now aligning these efforts with research opportunities, Matty said.

Army Vice Martin offered some additional detail regarding some of this research.

"Were looking at new mobile firepower capabilities, were looking at leveraging the technology thats available in the metallurgical development field so that we can have the lightest vehicle possible, but also the most survival vehicle as possible," he said. .

An interesting 2017 essay from the Hague Centre for International Studies offers some significance context regarding the anticipated operational impact of AI.

AI systems could provide probabilistic forecasts of enemy behavior, anticipate and flag bottlenecks or vulnerabilities in supply lines before they occur, and suggest mitigation strategies; draw on data (e.g. weather conditions collected by drones), to examine factors affecting operations and assess the viability of different mission approaches, the essay, called Artificial Intelligence and the Future of Defense, states.

Matty and his team are now refining potential applications of new algorithms being uncovered through the Armys current collaboration with scientists and engineers at Carnegie Mellon -- to help bring the vision outlined by the essay to life. There are a number of efforts showing early promise, Matty explained.

Some of these include sensor consolidation, an effort which not only reduces the hardware footprint created by building sensors into armored vehicles but also increases long-range sensor fidelity and organizes otherwise separate pools of data. This, among other things, can introduce new attack options, Matty explained.

We are working on sensor consolidation. For fire control there are a number of processes that are conducted in advance of finding the entities that you want to generate effects on. How do we leverage enhanced capabilities based on longer-range types of systems and overhead commercial imagery or UAS so we can rapidly perform PED (Processing, Exploitation, Dissemination)? he said.

Here Matty seems to be referring to how AI-aided algorithms can now immediately sift through drone-video feeds, instantly finding and disseminating intelligence of pressing combat relevance. This kind of information synergy described by Matty is also reinforced by the Hague Centre for International Studies essay, which states that new adaptations of AI technologies can perform analysis of reports, documents, newsfeeds and other forms of unstructured information.

Perhaps of greatest relevance, the essay explains how these AI-enhanced functions can forecast enemy behavior, anticipate maneuver challenges and find vulnerabilities in supply lines.

AI is also changing the readiness equation, Matty explained, by virtue of accelerating condition-based maintenance on board certain large platforms such as Strykers and Black Hawk helicopters.

We are working to refine the analytics and machine learning to not only increase the specificity but also improve the time horizon to predict when maintenance needs will occur. We can get to the granular level of specificity, not just for the fleet but eventually for tail numbers, Matty said.

The Army has been working on conditioned-based maintenance for many years now, an effort which includes engineering and integrating key data-collection sensors onto large platforms. These sensors can, among other things, monitor system health, engine rotations and other essential combat functions in real time - at times even wirelessly - to help commanders anticipate when a given system may malfunction. In recent years, the Army completed a proof of principle exercise using AI-enabled wireless technology to download and organize maintenance data from sensors on board Stryker vehicles. Collaborating with IBM, the Army was able to draw upon IBMs Watson computer to perform analytics in real time to produce indispensable maintenance information in seconds. The Armys AI Task Force is now working on this on Black Hawk helicopters.

How can information be analyzed to understand maintenance status? This includes developing new algorithms and interfaces for maintenance and mechanics to address supply chain and logistics challenges and take advantage of recently-available data assets, Matty said.

Automation and AI, which are of course progressing at near lightning speed these days, are often described in terms of easing the cognitive burden, meaning they can quickly perform analytics and a range of procedural functions to present to a human operating in a command control capacity.

Working on the cutting edge regarding both the promise and limitations of AI, the Armys Task Force is currently grounded in a doctrinal emphasis upon having a human in the loop. Essentially, AI and advanced computer processing speed are well suited to exponentially improve procedural functions, the organization of sensor data and other pressing computational operations -- yet Matty emphasized the importance of having humans in a role of command and control in order to make complex decisions, reason and best leverage the benefits of AI.

For instance, there are many combat variables which are often a complex byproduct of a range of more subjectively determined variables impacted by concepts, personalities, individual psychology, historical nuances and larger sociological phenomena. This naturally raises the question as to how much even the most advanced computer programs could account for these and other somewhat less tangible factors.

Advanced computer algorithms can analyze data and quickly perform procedural functions far more quickly than human cognition yet there are nonetheless still many things which are known to be unique to human cognition. Humans solve problems, interpret emotions and at times respond to certain variables in a way that the best computer technology cannot.

This concept, broadly speaking, is mirrored in an interesting 2017 essay from the Chatham House, Royal Institute of International Affairs called Artificial Intelligence and the Future of War. (M.L.Cummings). The essay explains how typical algorithms cannot necessarily generalize but rather only consider quantifiable variables.

Replicating the intangible concept of intuition, knowledge-based reasoning and true expertise is, for now, beyond the realm of computers, the essay states.

.AI is advancing, but given the current struggle to imbue computers with true knowledge and expert-based behaviours, as well as limitations in perception sensors, it will be many years before AI will be able to approximate human intelligence in high-uncertainty settings as epitomized by the fog of war.. The Chatham House essay states.

Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army - Acquisition, Logistics& Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel and The History Channel. He also has a Masters Degree in Comparative Literature from Columbia University.

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The Army Is Bringing Artificial Intelligence To Its Armored Vehicles - The National Interest Online

CHAMPAIGN, Ill. A new proof-of-concept study details how an automated system driven by artificial intelligence can design, build, test and learn complex biochemical pathways to efficiently produce lycopene, a red pigment found in tomatoes and commonly used as a food coloring, opening the door to a wide range of biosynthetic applications, researchers report.

The results of the study, which combined a fully automated robotic platform called the Illinois Biological Foundry for Advanced Biomanufacturing with AI to achieve biomanufacturing, are published in the journal Nature Communications.

Biofoundries are factories that mimic the foundries that build semiconductors, but are designed for biological systems instead of electrical systems, said Huimin Zhao, a University of Illinois chemical and biomolecular engineering professor who led the research.

However, because biology offers many pathways to chemical production, the researchers assert that a system driven by AI and capable of choosing from thousands of experimental iterations is required for true automation.

Previous biofoundry efforts have produced a wide variety of products such as chemicals, fuels, and engineered cells and proteins, the researchers said, but those studies were not performed in a fully automated manner.

Past studies in biofoundry development mainly focused on only one of the design, build, test and learn elements, Zhao said. A researcher was still required to perform data analysis and to plan for the next experiment. Our system, dubbed BioAutomata, closes the design, build, test and learn loop and leaves humans out of the process.

BioAutomata completed two rounds of fully automated construction and optimization of the lycopene-production pathway, which includes the design and construction of the lycopene pathways, transfer of the DNA-encoding pathways into host cells, growth of the cells, and extraction and measurement of the lycopene production.

BioAutomata was able to reduce the number of possible lycopene-production pathways constructed from over 10,000 down to about 100 and create an optimized quantity of lycopene-overproducing cells within weeks greatly reducing time and cost, Zhao said.

Zhao envisions fully automated biofoundries being a future revolution in smart manufacturing, not unlike what automation did for the automobile industry.

A hundred years ago, people built cars by hand, he said. Now, that process is much more economical and efficient thanks to automation, and we imagine the same for biomanufacturing of chemicals and materials.

Zhao also is affiliated with the departments of chemistry, biochemistry and bioengineering, and is a theme leader at the Carl R. Woese Institute for Genomic Biology and at the Center for Advanced Bioenergy and Bioproducts Innovation at the U. of I.

The U.S. Department of Energys Center for Advanced Bioenergy and Bioproducts Innovation and the IGB supported this research.

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Artificial intelligence to run the chemical factories of the future | Illinois - University of Illinois News Bureau