Inevitable architecture: Complexity gives way to simplicity and flexibility
In the next 18 to 24 months, CIOs and their partners in the C-suite may find an answer to this question in a flexible architecture model whose demonstrated efficiency and effectiveness in start-up IT environments suggest that its broader adoption in the marketplace may be inevitable. In this cloud-first model—and in the leading practices emerging around it—platforms are virtualized, containerized, and treated like malleable, reusable resources. Systems are loosely coupled and, increasingly, automated to be self-learning and self-healing. Likewise, on-premises, private cloud, or public cloud capabilities can be deployed dynamically to deliver each workload at an optimum price and performance point. Taken together, these elements can make it possible to move broadly from managing instances to managing outcomes.
2017 is the year your startup gets funded
After about 20 meetings, you should have a pretty good feel for whether your round is going to come together quickly (i.e. 2 months) or be a drawn-out slog (3-6 months); most tend to be the latter. That’s normal. Hopefully your meetings are leading to progressively deeper dives on the part of the investors. This means they’re interested, and if they’re interested, the discussion should start to veer toward valuation and terms. Next, you will receive a term sheet (if it’s a priced round) or commitments if you’re raising a convertible note. But even if you don’t have a term sheet after 15-20 meetings, don’t despair. Fundraising is a numbers game — remember our “hit rate” from Step 1? If you’re averaging a 5-10 percent ratio of pitches to commitments, you’re doing OK. This also means you’re getting rejected 90-95 percent of the time. Accept it as the way the game works, and don’t give up prematurely.
Biases in algorithms: The case for and against government regulation
Daniel Saraga, head of science communication at the Swiss National Science Foundation, asks in a recent Phys.org column, Should algorithms be regulated? For instance, think about a driverless car and its ability to recognize obstacles in the road: "The control algorithm has to decide whether it will put the life of its passengers at risk or endanger uninvolved passers-by on the pavement." With algorithms being proprietary "closed source," the answer to who is put in jeopardy—the driver or a passerby—is an unknown and carries the bias of the person/s who designed the algorithm-based control system. "They (algorithms) do not have prejudices and are unemotional," writes Alan Reid, senior lecturer in law at Sheffield Hallam University in this Conversation column. "But algorithms can be programmed to be biased or unintentional bias can creep into the system."
Kansas City rolls out online map using traffic, parking data from sensors
The interactive online map is operated by city contractor Xaqt, a Chicago-based company that provides integrated intelligence and collaboration tools for cities. With the map, a user can see the location of streetcars as well as how many vehicles passed streetcars at each hour in the past 24 hours and their real-time speeds. Available street parking is also shown in green, and by clicking on a parking area icon, it is possible to see available spaces at that location. Eventually, the city hopes to expand the use of sensors to other congested corridors. In the future, Alexa will be able to tell a user the location of the nearest free parking space and provide a daily forecast of public activities and meetings, said Bob Bennett, the city’s chief innovation officer, in an interview.
How to Boost Your Career in Big Data and Analytics
The world is increasingly digital, and this means big data is here to stay. In fact, the importance of big data and data analytics is only going to continue growing in the coming years. It is a fantastic career move and it could be just the type of career you have been trying to find. Professionals who are working in this field can expect an impressive salary, with the median salary for data scientists being $116,000. Even those who are at the entry level will find high salaries, with average earnings of $92,000. As more and more companies realize the need for specialists in big data and analytics, the number of these jobs will continue to grow. Close to 80% of the data scientists say there is currently a shortage of professionals working in the field.
Digital Transformation and high-tech Robo-Advisor - do you need one?
Big data and advanced analytics can help broaden the scope of robo-advice dramatically, incorporating financial planning into broader retirement planning, tax planning, vacation savings, higher education planning. Robo-Advisors have typically targeted millennials segment because these young investors want to save & multiple money faster and often don't have enough patience & wealth to warrant the attention and interest of a human advisor. High Net worth Individuals also think, online and automated investment tools can positively affect their wealth manager's advice and decision-making. Overall, robo-advisors provide a good user experience with latest digital technologies such as slick apps and fancy interfaces.These platforms make sure that they fit right in with your daily online browsing, and are great options for novice investors who are just starting out and want to dip their toes in the world of investments
IT unbounded: The business potential of IT transformation
Creating an unbounded IT organization will require that CIOs think beyond their own experiences and domain expertise and begin viewing IT through a different operational and strategic lens. For example, they can take a look at the efficiency and effectiveness of current budgeting, portfolio planning, and vendor selection processes and try to identify procedural, administrative, and other constraints that can be eliminated. Or they can work with business partners, start-ups, academics, IT talent, and vendors to explore nontraditional innovation, collaboration, and investment opportunities. ... Important to development, IT organizations can work to replace bloated, inefficient skillset silos with nimble, multiskill teams that work in tandem with the business to drive rapid development of products from ideation all the way through to deployment.1
The Promise of FinTech – Something New Under the Sun?
FinTech’s true promise springs from its potential to unbundle banking into its core functions of: settling payments, performing maturity transformation, sharing risk and allocating capital. This possibility is being driven by new entrants – payment service providers, aggregators and robo advisors, peer-to-peer lenders, and innovative trading platforms. And it is being influenced by incumbents who are adopting new technologies in an effort to reinforce the economies of scale and scope of their business models. In this process, systemic risks will evolve. Changes to customer loyalties could influence the stability of bank funding. New underwriting models could impact credit quality and even macroeconomic dynamics. New investing and risk management paradigms could affect market functioning. A host of applications and new infrastructure could reduce costs, probably improve capital efficiency and possibly create new critical economic functions.
What software engineers are making around the world right now
Hired’s study explores a range of other data, including how much data scientists and product managers are being paid across 16 major cities and how that salary information has changed over time. Of greater interest to us, however, is another section focused on the impact of bias on salaries and hiring practices. It’s something Hired began following roughly a year ago by collecting voluntary demographic data from candidates and examining how their identity impacts the wages they ask for — and what they receive. Bias is nothing new, of course. In fact, in a survey released Tuesday by the job site Indeed.com, one quarter of U.S. workers in the tech sector said they’ve felt discrimination at work due to their race, gender, age, religion or sexual orientation. Roughly 29 percent of female respondents said they experienced discrimination, compared with 21 percent of men.
Anomaly Detection for Time Series Data with Deep Learning
The increasing accuracy of deep neural networks for solving problems such as speech and image recognition has stoked attention and research devoted to deep learning and AI more generally. But widening popularity has also resulted in confusion. This article introduces neural networks, including brief descriptions of feed-forward neural networks and recurrent neural networks, and describes how to build a recurrent neural network that detects anomalies in time series data. To make our discussion concrete, we’ll show how to build a neural network using Deeplearning4j, a popular open-source deep-learning library for the JVM.
Quote for the day:
"Hardships often prepare ordinary people for an extraordinary destiny." -- C.S. Lewis
