Perhaps the most significant trend is that CIOs are facing ever-tougher competition today for their internal customers. In an earlier era, one simply had to go through the IT department to get the technology one needed that would actually work with the existing infrastructure, technology standards, and enterprise architecture. No longer. The cloud and especially software-as-a-service (SaaS), has changed this equation forever. Every IT department is now faced with the most formidable possible day-to-day competitor: The combined services inventory of the entire SaaS industry, along with all the available mobile and enterprise app stores. These new sources of marketing IT collectively represent to the CMO ... a genuine explosion of new options, going from a mere 150 business-ready marketing apps in 2011 to over an astonishing 3,500 in 2016.
More importantly, digital assets are designed for today’s era of digital information, and the underlying blockchain technology has the power to completely overhaul the current financial system, making it more efficient, transparent and accessible. When taking a look at the industry over the last 12 months, the first quarter of this year saw total investment in blockchain startups topping a staggering $1 billion. But that investment is starting to pull back. In the first nine months of 2016, blockchain startups raised $429 million across 92 equity financings. Compared to the same period in 2015, the deal activity fell this year by 16 percent, and funding was down by 7 percent. And we are already seeing some of this reticence play out in the market. For example, just last week, Circle announced they were pivoting away from the buying and selling of bitcoin through their wallet app.
According to an HP study earlier this year, the Android operating system is the second-most heavily targeted operating system with the second-most vulnerabilities, after Windows. Fortunately, in July, Google announced new measures to increase memory-level protections and reduce the overall attack surface of Android’s Linux kernel. ... It's no secret that breaches cost companies a pretty penny, but so often the costs are residual -- lost business, breach notifications, fines for late breach notifications -- but not punishments for the bad security itself. This year, however, some companies felt an extra sting for failing to protect their customers in the first place. ... Congratulate the San Francisco Municipal Transit Agency (SFMTA) for sticking up to ransomware operators, despite most likely losing money in the process. Instead of paying their $73,000 ransom demands, SFMTA gave passengers free rides at affected stations for days while they dealt with the situation.
That's easier said than done, of course. Getting themselves and their IT departments to adopt those ideas requires a shift in IT mindset, which, in turn, calls for a fair bit of psychology. Interviews with CIOs and organizational experts, however, suggest that change is indeed possible -- with a regimen that includes rethinking cherished beliefs and working to overcome barriers that impede a new work culture. Atilla Tinic, CIO at Level 3 Communications, has an educational background in IT, with a focus on software development, economics and psychology. He says, somewhat facetiously, that the last degree sometimes proves the most valuable. "It might be the psychology that helps me the most at times," he said. "Change management is one of the hardest things [and] I think the IT transition might be one of the most challenging."
Data scientist often frames a question into its business value and data context. It makes question more readable. Those questions could go in several different levels so rather than asking it all in one, the question itself could be break down into smaller business questions. There are methods to further reduce complexity by dimension reduction, variable decomposition or principle component analysis, etc. There are many analytic algorithm and modeling options. Choosing a proper algorithm could be a challenge. The alternatives are to run large number of algorithms to search. With that, large number of results will need to be analyzed. Interpreting results is a complex task. By running a large number of algorithms, the results tend to partial converge or partial conflicting. The conflict resolution and the weights of the variables require further modeling or ensemble.
“Everybody is doing deep learning today,” says William Dally, who leads the Concurrent VLSI Architecture group at Stanford and is also chief scientist for Nvidia. And for that, he says, perhaps not surprisingly given his position, “GPUs are close to being as good as you can get.” Dally explains that there are three separate realms to consider. The first is what he calls “training in the data center.” He’s referring to the first step for any deep-learning system: adjusting perhaps many millions of connections between neurons so that the network can carry out its assigned task. In building hardware for that, a company called Nervana Systems, which was recently acquired by Intel, has been leading the charge. According to Scott Leishman, a computer scientist at Nervana, the Nervana Engine, an ASIC deep-learning accelerator, will go into production in early to mid-2017.
Most of the considerations made so far were either general or specific to big players, but we did not focus on different startup business models. An early stage company has to face a variety of challenges to succeed, and usually, they might be financial challenges, commercial problems, or operational issues. AI sector is very specific with respect to each of them: from a financial point of view, the main problem regards the absence of several specialized investors that could really increase the value of a company with more than mere money. The commercial issues concern instead the difficulties in identifying target customers and trying head around the open source model. The products are highly new and not always understood, and there might be more profitable ways to release them.
"Near-term opportunities for cognitive systems are in industries such as banking, securities and investments, and manufacturing," IDC program director Jessica Goepfert said in an October statement about a report on global cognitive computing and AI spending. "In these segments, we find a wealth of unstructured data, a desire to harness insights from this information, and an openness to innovative technologies." In its report, IDC predicted that healthcare and manufacturing will be the biggest drivers of cognitive computing and AI revenues between now and 2020, while the education sector will also invest heavily in such technologies. Earlier this month, Tony Baer, principal analyst in information management at Ovum, predicted that machine learning in particular "will be the biggest disruptor for big data analytics in 2017." That trend will also make it increasingly important for organizations to treat data science as a "team sport," he added.
The smartphone has become a commodity to us. You don’t need to own the latest and most expensive model of all, but you are very likely to use a smartphone. There is even a small group that owns and uses a variety of smartphones. But don’t you feel that product innovation has stalled for a while? What was really new in recent years? Bigger display, smaller frame, better camera, stereo speakers, waterproof casings? All really nice but did they really change the game? No. There has not been a disruptive innovation in the mobility area since Steve Jobs was around. Not that he hoarded all the ideas and was the only thinker of our time, but he was driving disruption and therefore he was also pushing the competing manufacturers to be innovative.
Bot adoption is a confluence of two key technological and marketplace trends over the last few years. First, bots reflect the popularity of instant message platforms, a derivative of social media. Instant Messaging (IM) platforms include Facebook Messenger, Slack, WhatsApp, and Telegram. People have been steadily using these platforms. Back in 2015 Business Insider declared that IM platforms have more active users than any other internet application including social networks and email applications. Many bots are designed to complement services with these applications, in the same vein as being an extension for browser or an API for software. And many of the users access these applications on mobile devices, giving bot makers a dedicated avenue to connect with customers.
Quote for the day:
"Don't ever be afraid to admit you were wrong. It's like saying you're wiser today than you were yesterday." -- Robert Newell