The last and trickiest aspect of assessing ML technology is understanding how improvements on the ML task will impact which business metrics and by how much. Sometimes there’s a very direct relationship. For instance, for ad placement in search results, the ML metric is typically predicting the probability of ad click-through (possibly weighted by expected CPC). The rate and revenue-generated ad click-through is either a core business metric or closely related to one. In this setting, it makes a lot of sense to invest heavily in ML, because gains will likely improve business metrics. In other settings, the relationship is less clear. For instance, at Netflix, improving movie recommendation quality by 0.5 percent, while difficult, does not necessarily mean that month-over-month subscriber retention will necessary budge
Beyond being a marketing buzzword, Data Storytelling careerists know what it takes to tell a concise, actionable, data-driven narrative. Classic data mining and knowledge discovery trainingalways starts with identifying the business problem first, identifying clear goals or hypothesis to test, and then iterating through a data modeling process to select the right algorithms to produce recommendations (and then lather, rinse, repeat). As new statistical techniques were introduced and big data challenges emerged, these processes evolved but stayed generally the same. Organizations have since been trying to capture and scale this practice more recently, as evidenced by newly trending roles in the uppermost echelons like Chief Data Analytics Officer. These roles are charged with rolling out a data science team within their organizations, albeit an easier said than done practice.
What’s perhaps most interesting here is that these startups are targeting almost every industry out there. The first layer is general-purpose AI platforms that get fed large amount of data and automatically discover interesting patterns such as Valley-based Ayasdi, Germany-based Blue Yonder, or Israel-based SparkBeyond. Then there are companies that sell AI-based products to enterprises. These include AI-based personalization and marketing tools such as Radius and Dynamic Yield, sales and retention prediction tools such as 6sense and Gainsight, and AI-based customer support company Wise.io. But AI startups don’t stop at the enterprise. They are disrupting many traditional industries such as ground transportation, agriculture, industrial, and healthcare.
Imagine the following scenario: A family with parents and children is using the self-driving vehicle service within a smart city. All surrounding vehicles are also centrally controlled to better account for minimum risk of traffic accidents. A child runs after a ball across the street in front of the vehicle, transporting the family. This is an external factor to the traffic system and was not planned. The central traffic system now has to follow its primary routines and avoid harm to passengers and surrounding humans as good as possible. A crash is imminent even though a breaking process was started. The vehicle can now only “choose” the best possible option based on data it computes.
With emergent design, a development organization starts delivering functionality and lets the design emerge. Development will take a piece of functionality A and implement it using best practices and proper test coverage and then move on to delivering functionality B. Once B is built, or while it is being built, the organization will look at what A and B have in common and refactor out the commonality, allowing the design to emerge. This process continues as the organization continually delivers functionality. At the end of an agile release cycle, development is left with the smallest set of the design needed, as opposed to the design that could have been anticipated in advance.
Identified as a significant business opportunity, circular economy models have gained increasing momentum over the last five years. Combine the principles of a regenerative and restorative economy, where the utilization and useful life of assets is extended, with IoT technologies, which provide information about the condition, location and availability of those assets, and there may be an even greater opportunity to scale new models more effectively, while providing new direction to the digital revolution. To pose an example, the average European car currently spends 95 percent of the time parked. Large automotive manufacturers, including the likes of GM and Ford, have identified economic advantages to be gained by leasing vehicles through car-sharing models, rather than restricting themselves to a one-time sales model.
International Data Corporation (IDC) has identified robotics as one of six Innovation Accelerators that will drive digital transformation by opening new revenue streams and changing the way work is performed. In the new Worldwide Commercial Robotics Spending Guide, IDC forecasts global spending on robotics and related services to grow at a compound annual growth rate (CAGR) of 17% from more than $71 billion in 2015 to $135.4 billion in 2019. The new spending guide measures purchases of robotic systems, system hardware, software, robotics-related services, and after-market robotics hardware on a regional level across thirteen key industries and fifty-two use cases. … Not surprisingly, worldwide robotics spending is dominated by the discrete and process manufacturing industries, which represented 33.2% and 30.2% of total spending in 2015, respectively.
"There may be no software that has been better proven, from a security standpoint, than Bitcoin," Bagley said. "Building a stock trading platform atop such well proven software should leave all parties feeling very confident, from a security point of view." In addition, he said, settlement times are reduced from three days to 10 minutes, settlement costs are cut by 80 percent, and counterparty risk is eliminated because the cash and assets are accounted for ahead of time and instantly swapped. Finally, the blockchain is completely transparent, he said, and cannot be changed. "Put transparency and immutability together and you have a dream scenario for regulators, auditors and compliance officers," he said.
“The Human Face of Big Data” demonstrates that giving more people access to the Internet does not automatically include them in “the discussion.” China has more people connected to the Internet than any other country, but there is no one from China among the two dozen “experts” identified by name in the film—all are based in the U.S. No one from Russia, India, Japan, Brazil—countries where one may find talking heads or, even better, data scientists, that may represent a different point of view about the role of technology, the Internet, and big data. It would have enriched this documentary tremendously if we heard their take on the pros and cons of big data, how they define it, what it means to them, and what specific types of data collection and analysis will make a difference in their countries.
"At the centre of this transformation will be our individual digital identity that we alone curate to allow chosen organisations to interact with us. "Yes, we will give up some privacy but the pay-off will be more convenience. At first, we hesitated to use paywave but now everyone uses it because it saves time and effort. "Having a digital identity will give us freedom from information as well as more control of who we share our information with and what they do with it." Professor Kowalkiewicz said proactive organisations would become trusted partners and an invisible part of our lives. "For example, you are turning 16, the proactive organisation sends you a learners application. Driving instructors and defensive driving course providers will have contacted you. "Banks will analyse your credit card spend and alert you to suspicious charges, or to a possible data breach in an organisation you deal with, then automatically cancel your compromised card and send you a new one."
Quote for the day:
"You are never too old to set another goal or to dream a new dream." -- C.S. Lewis