Daily Archives: November 19, 2019

For many, just mentioning artificial intelligence brings up mental images of sentient robots at war with mankind and mans struggle to avoid the endangered species list. While this may one day be a real scenario for when (perhaps a big if?) mankind ever creates an artificial general intelligence (AGI), the more pressing matter is whether embedded software developers should be embracing or fearing the use of artificial intelligence in their systems. Here are five reasons why you may want to include machine learning in your next project.

Reason #1 Marketing Buzz

From an engineering perspective, including a technology or methodology in a design simply because it has marketing buzz is something that every engineer should fight. The fact though is that if there is a buzz around something, odds are it will in the end help to sell the product better. Technology marketing seems to come in cycles, but there are always underlying themes that are driving those cycles that at the end of the day do turn out to be real.

Artificial intelligence has progressed through the years, with deep learning on the way. (Image source: Oracle)

Machine learning has a ton of buzz around it right now. Im finding this year that had industry events, machine learning typically makes up at least 25% of the event talks. Ive had several clients tell me that they need machine learning in their product and when I ask them their use case and why they need it, the answer is just that they need it. Ive heard this same story from dozens of colleagues, but the push for machine learning seems relentless right now. The driver is not necessarily engineering, but simply leveraging industry buzz to sell product.

Reason #2 The Hardware Can Support It

Its truly amazing how much microcontroller and application processors have changed in just the last few years. Microcontrollers which I have always considered to be resource constrained devices are now supporting megabytes of flash and RAM, having on-board cache and reaching system clock rates of 1 GHz and beyond! These little controllers are now even supporting DSP instructions which means that they can efficiently execute inferences.

With the amount of computing power available on these processors, it may not require much additional cost on the BOM to be able to support machine learning. If theres no added cost, and the marketing department is pushing for it, then leveraging machine learning might make sense simply because the hardware can support it!

Reason #3 It May Simplify Development

Machine learning has risen on the buzz charts for a reason. It has become a nearly indispensable tool for the IoT and the cloud. Machine learning can dramatically simplify software development. For example, have you ever tried to code up an application that can recognize gestures, handwriting or classify objects? These are really simple problems for a human brain to solve, but extremely difficult to write a program for. In certain program domains such as voice recognition, image classification and predictive maintenance, machine learning can dramatically simplify the development process and speed-up development.

With an ever expanding IoT and more data than one could ever hope for, its becoming far easier to classify large datasets and then train a model to use that information to generate the desired outcome for the system. In the past, developers may have had configuration values or acceptable operation bars that were constantly checked during runtime. These often involved lots of testing and a fair amount guessing. Through machine learning this can all be avoided by providing the data, developing a model and then deploying the inference on an embedded systems.

Reason #4 To Expand Your Solution Toolbox

One aspect of engineering that I absolutely love is that the tools and technologies that we use to solve problems and development products is always changing. Just look at how you developed an embedded one, three and five years ago! While some of your approaches have undoubtedly stayed constant, there should have been considerable improvements and additions to your processes that have improved your efficiency and the way that you solve problems.

Leveraging machine learning is yet another tool to add to the toolbox that in time, will prove to be an indispensable tool for developing embedded systems. However, that tool will never be sharpened if developers dont start to learn about, evaluate and use that tool. While it may not make sense to deploy a machine learning solution for a product today or even next year, understanding how it applies to your product and customers, the advantages and disadvantages can help to ensure that when the technology is more mature, that it will be easier to leverage for product development.

Real Value Will Follow the Marketing Buzz

There are a lot of reasons to start using machine learning in your next design cycle. While I believe marketing buzz is one of the biggest driving forces for tinyML right now, I also believe that real applications are not far behind and that developers need to start experimenting today if they are going to be successful tomorrow. While machine learning for embedded holds great promise, there are several issues that I think should strike a little bit of fear into the cautious developer such as:

These are concerns for a later time though, once weve mastered just getting our new tool to work the way that we expect it to.

Jacob Beningo is an embedded software consultant who currently works with clients in more than a dozen countries to dramatically transform their businesses by improving product quality, cost and time to market. He has published more than 200 articles on embedded software development techniques, is a sought-after speaker and technical trainer, and holds three degrees which include a Masters of Engineering from the University of Michigan. Feel free to contact him at [emailprotected], at his website, and sign-up for his monthly Embedded Bytes Newsletter.

January 28-30:North America's largest chip, board, and systems event,DesignCon, returns to Silicon Valleyfor its 25th year!The premier educational conference and technology exhibition, this three-day event brings together the brightest minds across the high-speed communications and semiconductor industries, who are looking to engineer the technology of tomorrow. DesignCon is your rocket to the future. Ready to come aboard?Register to attend!

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4 Reasons to Use Artificial Intelligence in Your Next Embedded Design - DesignNews

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

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

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

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

Discussions, articles, and reports about the AI opportunity across the financial services industry continue to proliferate amid considerable hype around the technology, and for good reason: The aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total, per Autonomous Next research seen by Business Insider Intelligence.

Most banks (80%) are highly aware of the potential benefits presented by AI, per an OpenText survey of financial services professionals. In fact, many banks are planning to deploy solutions enabled by AI: 75% of respondents at banks with over $100 billion in assets say they're currently implementing AI strategies, compared with 46% at banks with less than $100 billion in assets, per a UBS Evidence Lab report seen by Business Insider Intelligence. Certain AI use cases have already gained prominence across banks' operations, with chatbots in the front office and anti-payments fraud in the middle office the most mature.

The companies mentioned in this report are: Capital One, Citi, HSBC, JPMorgan Chase, Personetics, Quantexa, and U.S. Bank

Here are some of the key takeaways from the report:

In full, the report:

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AI IN BANKING: Artificial intelligence could be a near $450 billion opportunity for banks - here are the strat - Business Insider India

This is a preview of a research report from Business Insider Intelligence. To learn more about Business Insider Intelligence, click here. Current subscribers can log in and read the report here.

New technology is disrupting legacy automakers' business models and dampening consumer demand for purchasing vehicles. Tech-mediated models of transportation - like ride-hailing, for instance - are presenting would-be car owners with alternatives to purchasing vehicles.

In fact, a study by ride-hailing giant Lyft found that in 2017, almost 250,000 of its passengers sold their own vehicle or abandoned the idea of replacing their current car due to the availability of ride-hailing services.

This will enable automakers to take advantage of what will amount to billions of dollars in added value. For example, self-driving technology will present a $556 billion market by 2026, growing at a 39% CAGR from $54 billion in 2019, per Allied Market Research.

But firms face some major hurdles when integrating AI into their operations. Many companies are not presently equipped to begin producing AI-based solutions, which often require a specialized workforce, new infrastructure, and updated security protocol. As such, it's unsurprising that the main barriers to AI adoption are high costs, lack of talent, and lack of trust. Automakers must overcome these barriers to succeed with AI-based projects.

In The AI In Transportation Report, Business Insider Intelligence will discuss the forces driving transportation firms to AI, the market value of the technology across segments of the industry, and the potential barriers to its adoption. We will also show how some of the leading companies in the space have successfully overcome those barriers and are using AI to adapt to the digital age.

Here are some key takeaways from the report:

In full, the report:

The choice is yours. But however you decide to acquire this report, you've given yourself a powerful advantage in your understanding of AI in transportation.

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THE AI IN TRANSPORTATION REPORT: How automakers can use artificial intelligence to cut costs, open new revenue - Business Insider India

Department of Defense officials routinely talk about the need to more fully embrace machine learning and artificial intelligence, but one leader in the Marine Corps said those efforts are falling short.

Were not serious about AI. If we were serious about AI we would put all of our stuff into one location, Lt. Gen. Eric Smith, commander of the Marine Corps Combat Development Command and the Deputy Commandant for Combat Development and Integration, said at an AFCEA Northern Virginia chapter lunch Nov. 15.

Smith was broadly discussing the ability to provide technologies and data thats collected in large quantities and pushed to the battlefield and tactical edge. Smith said leaders want the ability to send data to a 50-60 Marine cell in the Philippines that might be surrounded by the Chinese. That means being able to manage the bandwidth and signature so that those forces arent digitally targeted. That ability doesnt currently doesnt exist, he said.

He pointed to IBMs Watson computer, noting that the system is able to conduct machine learning and artificial intelligence because it connects to the internet, which allows it to draw from a much wider data pool to learn from. Military systems arent traditionally connected to the broader commercial internet, and thus are limited from a machine learning sense.

We have stovepipes of excellence everywhere from interagency, CIA, NSA. The Navys got theirs, Marine Corps got theirs, everybodys got theirs. You cant do AI when the machine cant learn from one pool of data, he said.

Brown noted that he was not speaking on behalf of the entire department.

Pentagon leadership has come to similar conclusions. Top officials have noted that one of the critical roles the Joint Enterprise Defense Infrastructure cloud program will do is provide a central location for data.

The warfighter needed enterprise cloud yesterday. Dominance in A.I. is not a question of software engineering. But instead, its the result of combining capabilities at multiple levels: code, data, compute and continuous integration and continuous delivery. All of these require the provisioning of hyper-scale commercial cloud, Lt. Gen. Jack Shanahan, director of the Joint AI Center, said in August. For A.I. across DOD, enterprise cloud is existential. Without enterprise cloud, there is no A.I. at scale. A.I. will remain a series of small-scale stovepipe projects with little to no means to make A.I. available or useful to warfighters. That is, it will be too hard to develop, secure, update and use in the field. JEDI will provide on-demand, elastic compute at scale, data at scale, substantial network and transport advantages, DevOps and a secure operating environment at all classification levels.

Overall, Smith said that industry should start calling out DoD when policies or technical requirements hinder what it can offer.

If were asking for something that is unobtanium or if our policies are keeping you from producing something we can buy, youve got to tell us, he said

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One way for the Pentagon to prove it's serious about artificial intelligence - C4ISRNet

Microsoftsrecent victoryin landing a $10 billion Pentagon cloud-computing contract called JEDI could make life more complicated for one of the software giants partners: the independent artificial-intelligence research lab OpenAI.

OpenAI wascreated in 2015by Silicon Valley luminaries including Elon Musk to look to the far horizon, and save the world. The newborn nonprofitsaidit had commitments totaling $1 billion and would work on AI to benefit humanity as a whole, unconstrained by a need to generate financial return. But OpenAIrestructured into a for-profitthis year, saying it needed more money to fulfill its goals, andtook $1 billionfrom Microsoft in a deal that involves helping the companys cloud division develop new AI technology.

Now Microsofts JEDI win raises the possibility that OpenAIs work for the benefit of humanity may also serve the US military.

Asked if anything would prevent OpenAI technology reaching the Pentagon, the labs CEO, Sam Altman, said its contract with Microsoft requires mutual agreement before any particular technology from the lab can be commercialized by the software giant. He declined to discuss what OpenAI might agree to or what the companys stance is on helping the US military.

Read full, original post: Can AI Built to Benefit Humanity Also Serve the Military?

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Artificial Intelligence lab OpenAI wants to develop technology 'to save the world'. Will it also assist the US military? - Genetic Literacy Project