In what might sound like the plot of a “Terminator” movie, Gold says ML and later AI will play a big part in making this complexity manageable. He said EXFO sees ML being used to learn and identify what normal network traffic looks like to better identify the root cause of an issue when it crops up. Needless to say, our IoT devices probably won’t be turning against us anytime soon. But while EXFO is preparing to launch an AI and ML-based AIOPs platform in the near future, other vendors like Masergy, Nyansa, and Vitria have already opened their arms to our AI overlords. In fact, the degree of visibility made possible by AI has implications for more near-term applications than 5G and large scale industrial IoT including gains in operation efficiency, SD-WAN, broadband performance, compliance testing, and improved customer experiences. ... Nyansa’s Voyance AIOPs platform works by pulling in real-time and historical data from devices on the network. This information is then processed using a series of ML and AI algorithms designed specifically to solve network challenges.
By adding AI to the mix, businesses can detect anomalies. For instance, if there is a breach in a data center, management can train an AI-based solution to identify any cyber attack. For this purpose, it goes through machine learning algorithms and consumes voluminous amounts of data. As a result, when a cyber threat emerges, AI can pick out the pattern and notify the authorities in time before data is compromised. This also means that AI can add a lot of automation in the privacy, compliance, and security of data. Hence, companies can ensure that they have a 24/7 protector that, unlike human resources, can tirelessly monitor their data transmissions. AI makes sure that data reaches the right user without getting intercepted by cybercriminals who may employ man-in-the-middle, spear phishing, ransomware, spyware, or any other cyber attack. Essentially, AI is democratizing data governance. For instance, AI is used in automated process discovery to analyze behavioral data that is generated during data processing. In this way, digital records are derived from behavioral data.
There is no denying that JWT is a cool breeze and a relief from the feature insanity of OAuth. I once spent a week trying to understand OAuth, I had to give up. There was simply no way I could wrap my brain around it. I could explain JWT to a 5-year-old child in less than 5 minutes. If OAuth is a scrapyard of madness and radioactive waste, JWT is a green field swimming in warm rays of sun after the morning dew has sprinkled the fresh grass made. A JWT token consists of three simple parts: a header describing the token, a payload that's the actual token, and a cryptographically secured signature, ensuring the token was created by a trusted source. All three components are base 64 encoded, separated by a ".", concatenated, and normally provided as a Bearer token in the Authorization HTTP header of your HTTP REST invocations — dead simple in fact. The reason why this is secure is because some sort of "secret" has been used when creating the signature, which is the last part of the token. Without the secret, you might as well try to brute force the unified theory of science. The thing is solid as a rock! Yet as simple as a cup of coffee on a Sunday morning.
Certainly, McLaren’s Applied Technologies arm is growing, using its knowledge of sensors, data collection and analytics to drive innovation in other industries. As well as being the sole supplier of batteries for Formula E, a class of motorsport that uses only electric cars, the business is also the main supplier of temperature and pressure sensors in F1. It also supplies its ECU to other F1 and Indy Car teams. “We are moving from engineering services,” says Neale, a nod towards increased digital innovation and development. “As well as connected technologies in other sports such as football and rugby, we are working in healthcare and the public transport sector.” ... “We need 5G now,” says Neale, claiming that this is the missing connectivity piece that will enable increased capability at the edge. He sees this as an essential development leap, the sort of advance that will create an acceleration in innovation and increased efficiency in operations.
“The evolution of business applications from monolithic constructs to flexible containerized workloads necessitates the evolution of the edge itself to move closer to the application data,” Uppal said. “This, in turn, requires the enterprise network to adjust and meet and exceed the requirements of the modern enterprise.” Such changes will ultimately make defining what constitutes the edge of the network more difficult. “With increased adoption of cloud-delivered services, unmanaged mobile and IoT devices, and integration of networks outside the enterprise (particularly partners), the edge is more difficult to define. Each of these paradigms extend the boundaries of today's organizations,” said Martin Kuppinger, principal analyst with KuppingerCole Analysts AG. “On the other hand, there is a common perception that there is no such perimeter anymore with statements such as “the device is the perimeter” or “identity is the new perimeter”. To some extent, all of this is true – and wrong. There still might be perimeters in defined micro-segments. But there is not that one, large perimeter anymore.”
AI strategy is still pretty new—and for many organizations, nonexistent. According to a recent IDC survey, half of responding businesses believed artificial intelligence was a priority, but just 25 percent had a broad AI strategy in place. ... And government is behind the private sector on the strategy curve. In a 2019 survey of more than 600 US federal AI decision-makers, 60 percent believed their leadership wasn’t aligned with the needs of their AI team. The most commonly cited roadblocks were limited resources and a lack of clear policies or direction from leaders. But a coherent AI strategy can attack these barriers while building a compelling case for funding. A winning plan establishes clear direction and policies that keep AI teams focused on outcomes that create significant impacts on the agency mission. Of course, strategy alone won’t realize all the benefits of AI; that will require adequate investments, a level of readiness, managerial commitment, and a lot of planning and hard work. But an effective AI strategy creates a foundation that promotes success.
Despite the fact that open source has never been more broadly used, apparently we’re in an “open source sustainability” crisis. It’s the same “crisis” we’ve been in for the past 20 years, with persistent warnings that this can’t last. ... Red Hat CEO Jim Whitehurst has been agitating for customer-driven open source for well over a decade: “Ultimately, for open source to provide value to all of our customers worldwide, we need to get our customers not only as users of open source products but truly engaged in open source and taking part in the development community.” There are many reasons why such customer-driven (or user-driven) innovation might be best, but Linux veteran Matt Wilson put it this way: “If I can risk predicting the future, I think you’ll see a lot more new open source software emerging from companies that are building it and using it to solve their business problems. And it will be better because of a positive feedback loop of putting the software into an applied practice.”
Big data services companies were not in much use in the earlier times but now they are and this is because of the development of artificial intelligence and machine learning. Earlier, having big data was a problem as there were no methods to interpret such a large amount of data that too, in a wide variety. But this has changed now. Artificial intelligence and machine learning help a lot in interpreting all different types of data, be it in textual form or pictures, videos, etc. With the help of artificial intelligence and machine learning, enterprises are able to unleash a whole lot of different uses from the big data they have and acquire continuously. Let’s understand a bit more. The type of relationship that exists between artificial intelligence and big data or big data analytics solutions is of the reciprocative type. Artificial intelligence is somehow meaningless without big data. If artificial intelligence does not have any data, how or on what will it work? One can say that it is mainly made for this purpose, to tackle large amounts of data and extract out meaningful insights from it.
Imagination is one of those magical features of the human mind that differentiate us from other species. From the neuroscience standpoint, imagination is the ability of the brain to form images or sensations without any immediate sensorial input. Imagination is a key element of our learning process as it enable us to apply knowledge to specific problems and better plan for future outcomes. As we execute tasks in our daily lives, we are constantly “imagining” potential outcomes in order to optimize our actions. Not surprisingly, imagination is often perceived as a foundational enables of planning from a cognitive standpoint. Incorporating imagination into artificial intelligence(AI) agents have long been an elusive goal of researchers in the space. Imagine AI programs that are not only able to lean new tasks to plan and reason about the future. Recently, we have seen some impressive results in the area of adding imagination to AI agents in systems such as AlphaGo.
Quote for the day:
"Leadership is the wise use of power. Power is the capacity to translate intention into reality and sustain it." -- Warren Bennis