Daily Tech Digest - March 25, 2017

The future of AI: 10 scenarios IBM is already working on

The approach of Karasick's team is ridiculously practical, since their mandate is to incubate technologies that could be useful to businesses. As you'd expect, a lot of things they're working on boil down to automation and big data. "The reason we use machine learning in these problems is because there's too much data," said Karasick, whose team at IBM Research contains a mashup of mathematicians and systems analysts. The team uses AI for three types of things: Develop industrial strength solutions; Make more efficient use of people; and Improve time-to-value. Karasick's IBM InterConnect session "Looking Ahead: The Future of Artificial Intelligence" offered a window into the AI projects IBM is already working on. Here's a quick summary of 10 of them.

What happens when every device is smart and you don't even know it?

"Could I attach my dog to the internet? Could I automate the process of ordering a taxi on my mobile phone? We're obsessed with could we problems. That's how we live our lives and careers, we invent things and we solve problems. We're good at 'Could we'," he said, also speaking at Cloud Expo Europe. No matter the reason why things are being connected to the internet, Thomson agrees with Hyppönen about what the end goal is: data collection. "The connectivity of those devices is impressive and important. But what's more important is how that's coming to bare across various markets. Every single sector on the planet is in a race to digitise, to connect things. And very importantly, to collect data from those things," he says.

An Emotionally Intelligent Computer May Already Have an ‘EQ’ Higher Than Yours

Researchers are learning to replicate human emotions in robots for a variety of applications. One example? Consider Wall Street stock traders, who have to make split-second decisions with millions of dollars of other peoples’ money. It’s a high pressure environment, and emotional health of employees isn’t typically optimal. This can lead to life-changing errors in judgment. Now, large businesses like Bank of America and JPMorgan Chase are partnering with tech companies to monitor the emotional health of traders in hopes of preventing serious mistakes, improving performance, and ensuring compliance. ...  Sony has announced plans to create customer service robots that will develop emotional bonds with customers. SoftBank’s Pepper— billed as an emotional polyglot robot and interactive humanoid— is another robot that has serious customer service potential.

Innovation under the hood will rev the engines of a fintech revolution

In recent years, financial services architecture has opened up in a way that we have never seen before. Data APIs like Yodlee, Plaid, and Quovo now make it easy for developers to pull user financial data. SDKs like Card.io make it easy to onboard payment cards into mobile apps, financial market APIs like Xignite pull live stock prices, and payments APIs like Braintree and Stripe make it simple for developers to accept payments. The combination of this development at the infrastructure layer, with what my partner Sarah Tavel notes as the growing distrust of traditional financial institutions, has created an opportunity for fintech startups similar those in internet and television: to create application layer companies with massive mindshare and value capture without having to innovate at the infrastructure layer themselves.

Infographic: A Beginner's Guide To Machine Learning Algorithms

Only recently have we been able to really take advantage of machine learning on a broad scale thanks to modern advancements in computing power. But how does machine learning actually work? The answer is simple: algorithms.  Machine learning is a type of artificial intelligence (AI) where computers can essentially learn concepts on their own without being programmed. These are computer programmes that alter their “thinking” (or output) once exposed to new data. In order for machine learning to take place, algorithms are needed. Algorithms are put into the computer and give it rules to follow when dissecting data. Machine learning algorithms are often used in predictive analysis. In business, predictive analysis can be used to tell the business what is most likely to happen in the future.

Pi-powered Linux computer:  Packs a keyboard and display into a phone-sized case

By turning the Pi into a ready to use computer, the Terminal gets around a fundamental limitation of the Pi, which generally needs to be hooked up to a monitor and keyboard for use on the move. The limitations of the Zero's specs mean the device is better suited to undemanding tasks such as coding, working in the command line, word processing or running old games in emulators, rather than using the web browser and other desktop programs. However, Node promises an alternate model of the Terminal will use the more powerful, but slightly larger, Raspberry Pi 3 Model B, which can run a desktop OS reasonably comfortably. Powered by a rechargeable 1,500mAh battery, the Zero Terminal can also be hooked up to a monitor and mouse, via its HDMI and full USB port, unlike the vanilla Pi Zero W, which requires additional adapters.

This Bitcoin Botnet is Vying to Be Future of Secure IoT

NeuroMesh's idea is to mimick the same tactics hackers use when trying to compromise machines in the first place – installing lightweight code that hijacks the kernel and then dials out to a command and control (C&C) server, adding the machine's resources to a botnet directed by the bot 'herder'. "We wanted to create a vaccine for IoT devices by first installing our own security software on the kernel," said Li. "It's like playing 'King of the Hill', so we become the only ones that can control the device." One of the main points of vulnerability for a botnet is an attack on the C&C server, something that's often observed when competing hackers try to knock their rivals' botnets offline and commandeer the devices. NeuroMesh's solution is to send commands to devices secured by their technology via OP_RETURN codes in the bitcoin blockchain – code that allows for the transmission of arbitrary data

12 Interesting Big Data Careers That Everyone Should Know

Ever wondered how people as young as 25-30 become CTOs and attain exponential growth in a short time? Sure, they have the talent, but they also take the right steps to grow in their career. They are very clear what they want to achieve and create milestones to make it. Like them, have you planned your career to succeed? If not, it is not too late to rework your career plan. If you are a graduate, then some data science opportunities await you. ... Beyond software industries, many industries like retail, manufacturing are turning to big data to ease the process of making efficient systems. In turn, they are leveraging the skills of data managers to improve operational efficiencies. Why wait? Follow these steps and take your career to new heights!

The 4 big ethical questions of the Fourth Industrial Revolution

Since these technologies will ultimately decide so much of our future, it is deeply irresponsible not to consider together whether and how to deploy them. Thankfully there is growing global recognition of the need for governance. Professor Klaus Schwab, Executive Chairman of the World Economic Forum, for example, has called for “agile governance,” achieved through public-private collaborations among business, government, science, academia and nongovernmental civic organizations. Wendell Wallach and Gary Marchant, both scholars in this area, have proposed “governance coordinating committees” or GCC’s that would be created for each major technology sector and serve as honest brokers. Whatever forms governance takes, and it will (and should) take many forms, we need to make sure that governing bodies and public discussion address four critical questions.

Future of the SIEM

Complex mission aside, one key shortcoming of today's SIEM products is their reliance on humans. "SIEM is, in that sense, more rule-based and expert-described," says Chuvakin. "That's a main weakness because at this point, we're trying to get developed tools to try and think for themselves." The dependence on human experts is a problem because there simply aren't enough of them, he continues. If a business needs five SIEM experts and its entire IT team consists of five people, they don't have the bandwidth to ensure the SIEM is effective. Amos Stern, co-founder and CEO of Siemplify, explains there is need for better SIEM automation and management of people and systems. Businesses often have several security tools in many silos. SIEM systems will need to connect these silos and automate processes and investigations across these tools, evolving to the point where they function as a "Salesforce for security."

Quote for the day:

"You should learn from your competitor but never copy. Copy,and you die." -- Jack Ma

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