Before 2020, fully autonomous vehicles will become a fixture on our highways and not long after, autonomous taxi networks will experience unprecedented growth that will radically transform the nature of travel and transportation, with a corresponding boost in productivity. Autonomous travel, costing only half as much as driving a personal car, will drive car sales down. The decline in battery costs will make electric vehicles (EVs) more preferable to gas-powered vehicles because it will be far less costly to own an EV. This will lead to widespread adoption of EVs and companies like Tesla will stand to gain the most ... Although it is the auto industry that might have driven the sale of industrial robots, it’s now far from being the only industry that employs the use of this technological innovation. Especially as capital and programming costs continue to decline, manufacturing companies will benefit more from employing robots and automating more of their processes.
With all of the excitement around blockchain technology, it’s easy to think what we have now is the foundation for the next wave. Yet, it’s worth remembering we are still in the early stages. The blockchains we have today probably won’t be the blockchains of tomorrow. ... It also has a lot of technical questions that surround it. As Muneeb Ali of Blockstack said, “At scale, Ethereum is designed to fail” — though he was quick to add that there’s always room to make changes in the future. He didn’t mean, “it will intentionally fail.” However, if you think about the nature of blockchains — everyone has a copy of the ledger, which these days is about a 100GB download. Furthermore, in the case of Ethereum, ever more third-party applications and sub-economies are being launched to run on top of it, and all of that code runs on the distributed network too. So it makes sense to start asking questions.
Because Microsoft and its partners offer fee-based SAM services, concerns on the part of customers about their practices could easily dampen enterprise enthusiasm for the evaluations, and thus reduce revenue from SAM programs. And Microsoft clearly sees SAM as a money maker for its partners. "The SAM opportunity in enterprise has never been bigger. Learn about Microsoft's plan for enterprise and industry accounts, and how you can build new revenue streams with SAM," states a description of one of several SAM-related sessions listed on the schedule for the upcoming Inspire conference in Washington, D.C. July 9-13. Microsoft Inspire is the renamed Worldwide Partner Conference, long the yearly massive meet-up of the firm's global partner network, on which Microsoft relies for much of its software and services sales.
In an RDF data store, we can pre-define the schema models - called Ontologies - as well as load new dataset as they come in. So, instead of spending enormous amount of time in creating the data model, we started out with a standard – Financial Industry Business Ontology (FIBO) model and decided to extend it as we encounter a new set of data. The expense involved with mastering custom code was avoided through the use of RDF Graph DB features. We could load multiple datasets into RDF Graph DB, as they are maintained in the source system without creating special extract files. The connections happen at the database at the attribute level between multiple domains as well as with transaction data. The major mindset change required is to not process master and transaction data separately and then build dimensional model, but to build an integrated RDF Graph DB where they can co-exist and fully connected through a single set of processes.
"It's a constant game of cat and mouse between the defenders and the attackers," Maor noted. With technology constantly changing, security has a tough time keeping up. Maor explained that the security industry moves significantly slower than the cybercrime industry because there are no regulations for cybercrime. Maor said it's imperative for people to change how they approach security. Companies are not doing basic things to protect themselves from cybercrime, they need to have backups in place and always be prepared, Maor furthered. The mindset around cybersecurity and cybercrime must shift. Businesses need to run under a "when will I get hacked" instead of an "if I get hacked" mentality, making security more of a priority than expediency to release a product.
As we enter the Fourth Industrial Revolution, rapid and unpredictable shifts in technology will present both challenges and opportunities. The sheer volume of available data in the new world could fundamentally change the way society operates by developing previously unthinkable solutions to problems we didn’t know existed. Digitization of everyday things, when coupled with the ability to self-enhance through artificial intelligence, will drive significant change in the global economy. Failure to prepare for and respond to digitization in the Fourth Revolution will be costly, especially as new market entrants test and evolve. The dramatic rise and fall of video rental giant, Blockbuster, is a poignant illustration of how digital innovator, Netflix, overtook the $5 billion incumbent by gradually siphoning off its customer base.
Evaluating motivation is about improving sourcing, which is typically a low-yield, labor-intensive business. Every recruiter knows that reaching out to candidates who have not applied often produces few results because of low response rates. However, a machine learning system can identify people who are more likely to to consider a solicitation for a job; in other words, those who are more motivated to change jobs or accept a new one. There’s an abundance of data on social networks and other places that can be tapped for this purpose. For example, Google’s Timeline tracks your every move (check it out) and can be used to accurately determine a person’s commute. A candidate with a long commute is more likely to respond to a solicitation than someone who has a short one, especially if the former travels through heavy traffic.
The increased speed of these cyber incidents allows for more such attacks to occur, and Shahani suggests that has an had adverse impact on organizations' bottom line. "The penalty is huge as the cost of data breach incidents for companies in India and Asia [and] is significantly increasing this year from what was observed during the previous year," Shahani says. According to the study, the cost of a data breach in India this past year increased by 12.3 percent. The cost of lost or stolen records in the past year rose by 12.8 percent. The study cites malicious or criminal attacks, insider negligence and system glitches as the root causes of data breaches and that, Shahani says, makes a huge impact on the cost, besides the time to detect and contain the incident.
Digital transformation is not all about tools or technology—it’s about people too. Today's workplaces are becoming increasingly multigenerational. Older employees are staying in the workforce longer and mixing with younger colleagues who are just starting their careers. As such, the range of ages in the workplace is naturally expanding. A recent survey from executive development firm Future Workplace and Beyond, The Career Network, found that 83 percent of respondents have seen millennials managing Gen X and baby boomer workers in their office. However, 45 percent of baby boomers and Gen X respondents said millennials lack managerial experience, which could have a negative impact on a company's culture. More than a third of millennial respondents said managing older generations is challenging.
Optimizing computational cost of the machine learning model like all other use cases there is a trade-off between accuracy and image resolution. Also the lower the resolution that optimizes accuracy, the shorter the flight time of a drone to criss-cross a field and the longer the battery life. In addition to saving the time and cost of deploying IoT devices and networks to interconnect them, machine learning could be a separate path to confirm an IoT system is working. A critical IoT device could fail and report a false condition. For instance, IoT sensors might fail to report critical conditions such as a fire, an unauthorized person entering or a door left open, but a machine learning model sampling a video feed could recognize the critical condition, all as adaptations of Resnet 50 or another convolution network.
Quote for the day:
"The obvious is that which is never seen until someone expresses it simply." -- Khalil Gibran