There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data. There are whole swaths (not swatches) of techniques that have been developed over the years like Linear Regression, K-means, Decision Trees, Random Forest, PCA, SVM and finally Artificial Neural Networks (ANN). Artificial Neural Networks is where the field of Deep Learning had its genesis from.
Fog computing refers to decentralized computation at the edges of the network, as opposed to being centralized in data centers. By distributing computing to the edges, the results will be sent to the cloud, not the raw data itself. This shift in paradigm will tremendously reduce the need for increased bandwidth and computational power in the cloud. Centralized computing in the cloud has provided several benefits for enterprises. Scalability, easy pricing schemes and minimal upfront cost are among the big ones. However cloud computing have certain disadvantages. Foremost latency and delay jitter, as well as there being a higher probability for security breaches when large amounts of data is moved through networks. Fog computing greatly reduces the amount of data being sent to and from the cloud, reducing latency as a result of local computation while minimizing security risks.
Customers expect that they can carry out even quite complex queries and transactions on their own terms. AI advances allow sophisticated Natural Language Processing and continuous improvement through Machine Learning. This will be the subject of future post because I consider this to be one of the most exciting and promising technology areas for community banks. Benefits include greater customer satisfaction, deeper relationships, cross-selling opportunities, and reduced personnel expense. ... Robadvisors are becoming sophisticated enough to be highly valued assistants for financial advisors. Community banks that offer wealth management and investment advisory services will benefit significantly. They will see increased customer interaction and deeper advisory abilities.
More is needed. No fork-lift upgrades, no more proprietary “boxes.” True SDN will be provided as software running on standard servers or virtualized only. The addition of SDN will be in a non-disruptive manner to allow partners to move as quickly or as slowly as their need determines. All current systems will be unaffected by the additions of SDN. Whether or not all these systems will be needed after implementing SDN, will be a decision that can be made at a future time. SDN offerings need to be flexible as well in implementation objectives. Both Layer 2 and layer 3 products should be available to address all possible scenarios and when used in conjunction can address not only major location connectivity, but also connectivity for road-warriors, work-at-home, the Internet of Things (IoT), and supervisory control and data acquisition (SCADA). This ensures a holistic approach — the SDN offering must have options for office locations and individual devices.
Digital tokens and blockchains, two distinct but complementary technologies, waste cheap storage to give data the continuity of real-world assets. Bitcoin is just the first application. The technologies are far from mature, but if scalability limitations are overcome, they will have long-term disruptive potential in complex transaction networks such as trade, health care, and the Internet of Things. And it is by no means obvious that traditional intermediaries will be able to control them. This essay outlines how the economics of transaction costs and trust could be reshaped by tokens and blockchains and by the stacked architecture on which they are built. The aim is not to prescribe exactly what leaders should do (every business is unique, and the devil is in the details) but to provide a strategic context to help executives frame the right questions.
An interconnected city grid of traffic and pedestrian cameras offers a wealth of actionable Big Data. As an example, in the Dutch city of Rotterdam, “the traffic authority monitors about 22,000 vehicle movements every morning, while the regional environment agency produces hourly data about air quality from sensors across greater Rotterdam resulting in over 175,000 observations per year.” In addition to better managing traffic and public transit, as well as controlling pollution, proponents highlight the ability of such data to enable enhanced policing, crowd control, and even public sentiment monitoring. However, others express grave concerns about the potential for abuse in such systems, especially given the integration of smartphones into connected apps utilized by many smart cities.
A study says that data science is going to open up as much as 10 million jobs in this decade. Now, since you already know there are many opportunities, how do you leverage your skills to tap into it? First and foremost look at what skills define you. Is it your expertise, your visualization skills or managing skills that you not only demonstrate but also enjoying working? Once you're through with it, work towards it and learn from the different software languages that are trending in the industry and are in high demand. Take up certification courses that can give the much-needed edge. After your build, your portfolio with technical skills, a broad range of data job profiles can help you settle in and earn a six figure salary. Beyond software industries, many industries like retail, manufacturing are turning to big data to ease the process of making efficient systems.
Dutch are very open, however, people in Asia tend to be less open, especially when authority is involved, i.e., “I’m not going to contradict my boss or project manager”. That may be seen as disrespectful. If the boss is in the West and I’m in the East, then my Western boss in turn will keep asking me to be more open or proactive. And I might get confused, because I’m not used to being allowed or even stimulated to voice my ideas. If my boss tells me “This is the way to do it,” I’d rather do that exactly, even if I think it’s a crazy idea. This behavioral difference impacts most of the agile ceremonies. For example, in sprint planning if a product owner asks 'Can you take more user stories?', regardless of the possibility, people in some Asian cultures tend to say "Yes" always, which defeats the whole purpose of doing planning
"While DDoS attack prevention is partly a technical issue, it is also largely a business issue," said Rachel Kartch, analysis team lead at the CERT Division of SEI, a federally funded research and development center sponsored by the US Department of Defense and operated by CMU, and author of the DDoS post. In general, organizations should begin planning for DDoS attacks in advance, Kartch noted in the post. "It is much harder to respond after an attack is already under way," she said. "While DDoS attacks can't be prevented, steps can be taken to make it harder for an attacker to render a network unresponsive." To strengthen resources against a DDoS attack, it's important to make the architecture as resilient as possible, Kartch said. "Fortifying network architecture is an important step not just in DDoS network defense, but in ensuring business continuity and protection from any kind of outage or disaster situation," she said.
While cloud-based IoT infrastructure is usually viewed as the next phase in tech-savvy markets, many developing nations are looking at it as a way to propel their economies into the 21st century without having to recreate decades’ worth of data center infrastructure. Systems developers like Fujitsu are hoping to tap these markets with turnkey solutions that allow organizations to launch full-scale IoT environments with relative ease and low start-up costs. The Fujitsu Cloud IoT Platform provides a broad set of APIs and a user-friendly dashboard to enable a high degree of customization and continuous development as technologies and markets evolve. Fujitsu also has a large managed services portfolio that focuses on providing solutions to clients rather than loading them up on technology.
Quote for the day:
"You never change things by fighting the existing reality. To change, build a new model that makes the existing model obsolete." -- @JamesSaliba