The famous inventor and computer scientist Ray Kurzweil has made some very bold predictions about the pace at which human technology is advancing toward the ultimate threshold. That epithet is a metaphor borrowed from physics terminology to express the point at which information technology--specifically artificial intelligence--becomes sufficiently advanced as to irreversibly alter the course of history on earth. Kurzweil's model predicts that by 2029 technological advancement will be occurring at such a rapid and explosive rate that humans will not be able to keep up without merging symbiotically with machines. And by 2045, AI is predicted to surpass human beings as the most intelligent and capable beings on the planet.
On the other hand sophisticated robots make it possible to move from mass production into personal production. Already today the internet sector employs a significant number of programmers, designers and marketing people. As we move from one dimensional internet into more dimensional virtual reality employment will increase dramatically. The first implicit assumption behind this thought is that while our technology creates sophisticated machines and software, our education system will not make use of these new opportunities.
A recent ban affecting three of China's biggest online platforms aimed at "cleaning up the air in cyberspace" is just the latest government crackdown on user-generated content, and especially live streaming. This edict, issued by China's State Administration of Press, Publication, Radio, Film and Television (SAPPRFT) in June, affects video on the social media platform Sina Weibo, as well as video platforms Ifeng and AcFun. In 2014, for example, one of China's biggest online video platforms LETV began removing its app that allowed TV users to access online video, reportedly due to SAPPRFT requirements. China's largest social media network, Sina Weibo, launched an app named Yi Zhibo in 2016 that allows live streaming of games, talent shows and news.
This is the story of how GE has accomplished this digital transformation by leveraging AI and Machine Learning fueled by the power of Big Data. Bill Ruh, the CEO of GE Digital and the company's Chief Digital Officer, emphasizes the role and importance of data and analytics in the company's transformation. Machine Learning technology, according to Ruh, is critical to making the "digital twin" concept successful. Because there may also be changes over time relative to which variables and models best predict the need for required maintenance, machine learning represents the best technology approach to addressing these requirements.
The best way for companies to fully understand the real potential of AI is to dispel some common misconceptions, starting with the following three. Examples include data mining and analysis; machine learning, which enables machines to teach themselves without any help from human programmers; natural language processing (NLP), which enables a machine to understand human speech as it is spoken; and digital image processing, used to analyse and interpret pictures and photographs. Artificially intelligent machines will replace humans in some areas of work, but certainly not all of them. We will undoubtedly see an increase in job vacancies that specifically deal with managing artificial intelligence technologies, including software engineers, analysts, researchers and project managers.
Consent is becoming a thorny issue and marketers everywhere need to understand what measures their data protection officers are putting into place. Martech vendors are adding machine learning features and client-side companies are looking to employ data scientists to take advantage of their wealth of customer data. Now is the perfect time for marketers to increase their understanding, not least because the General Data Protection Regulation (GDPR) is due to come into force in May 2018, stipulating that processing of personal data must be "lawful, fair and transparent". Consent is becoming a thorny issue and marketers everywhere need to understand what measures their data protection officers are putting into place for the GDPR, and how that affects marketing workflow.
It's revolutionized seemingly non-digital industries--think of how different financial services, for instance, are today from what they were two decades ago--and investors expect it to soon transform others, which is why Tesla Motors is worth more than General Motors despite making a tiny fraction as many cars as GM makes and earning a tiny fraction of the revenue. But it positioned the company to benefit from the network effect: having third-party sellers made Amazon more appealing to customers, which in turn made it more appealing to sellers, creating a virtuous cycle for the company. Beyond the network effects is another, related way that the sheer scale of the Big Five helps them stay on top: through the access they have to enormous amounts of user data. That data, which is far more detailed and granular than anything companies have been able to access in the past, helps these companies improve their products and services, which in turn helps them add more users, which gives them access to more data, and so on.
The European Commission's decision to fine Google reignites debate about the role of competition regulators, how governments police the internet and whether a single public body should act as judge, jury and executioner in such massive legal cases. That's why the second element of Tuesday's decision -- the Commission's instruction that Google change its algorithm -- matters so much. In the Google case, the problem was centered on how the company's Google Shopping algorithms favored the company's own products and services over those of competitors. It is simply that a small company has no dominant position to abuse, and in a bigger company more things can go wrong or unnoticed.
The media approaches automation from multiple angles, though most publications often address modern day automation as a new frontier taking over mundane tasks now, but possibly our jobs in the future. The vast amount of data, in petabytes per second, produced by today's network of IoTs and other applications, form the launching pad for machine learning applications in cybersecurity. The lack of confidence will likely result in increased resource cost, but more importantly shift the focus from real threats to false positives. Additionally, machine learning needs reinforcement to detect Advanced Persistent Threats (APTs) and eliminate false positives.
A new study reveals the mathematics underlying this phenomenon, modeling how information overload can erode an individual's ability to distinguish high-quality information from its opposite, causing falsehoods to propagate. "It was the first paper I've seen in this area that quantifies what many people thought was happening, and that's basically with limited attention we're unable to see the full range of potential arguments or sides of the story," says Dr. Uzzi, who has studied how social media users isolate themselves into echo chambers. The researchers suggest that social networks could curb information overload by aggressively limiting content shared by so-called bot accounts, software agents that flood social networks with low-quality information. The research reveals some of the math that drives what psychologists have long known: Information overload makes it harder to make decisions.