Current Capabilities of Artificial Intelligence (AI)
Surprisingly, despite AI’s breadth of impact, the types of it being deployed are still extremely limited. Almost all of AI’s recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B)…
Today’s supervised learning software has an Achilles’ heel: It requires a huge amount of data. You need to show the system a lot of examples of both A and B…
So what can A?B do? Here’s one rule of thumb that speaks to its disruptiveness:
If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.
The Smart Speakers Market
A new study by Juniper Research has found that revenue from smart audio hardware will more than triple over the next four years, rising from an estimated $1.4 billion this year to over $5.5 billion by 2020.
While Juniper expects part of this to be driven by ear-based wearables like the Bragi Dash and HERE Active Listening, the category will be most successful in the smart home space, with Amazon Echo, Google Home and other unit-based smart audio devices vying for popularity as the next platform for smart devices.
Smart Audio Needs a Home
The new research, Future Hearables & Smart Audio: Roadmap, Opportunities & Forecasts 2016-2021, found that the reason for the dominance of home-based smart audio is two-fold: consumers are not yet willing to talk to machines in public, and also that smartphones can provide similar functions through simple earphones with a mic. These factors combine to limit the market for people who want a digital assistant device with them on-the-go.
Home-based smart audio devices, however, operate in a private environment, overcoming the social reluctance, and do not have to challenge the smartphone to the same degree. For this reason we expect more success from unit-based smart audio devices than hearables.
Niche is Nice for Hearables
There will however be a smaller market for hearables that offer various audio features, like active noise cancellation and call handling as well as providing audio, but the biggest market for these will be fitness devices. These can provide accurate biometrics as well as voice feedback from a piece of coaching software, but only a certain kind of consumer will buy that sort of device.
“Smart speakers win out because while they also need a context, their form factor gives them an almost universal one, while hearables fill specific audio niches,” remarked research author James Moar. “As a result, smart speakers and hearables will fill very different roles, despite relying on similar software capabilities in many cases.”
The whitepaper, ‘Sensing the Hearables Opportunity’, is available to download from the Juniper website together with further details of the full research.
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Predicting the Presidential Election: What Went Wrong? (Part 2)
When asked about what qualities matter most, about four in 10 people picked the ability to bring about change, and Mr. Trump won more than 80% of their votes. Mrs. Clinton was heavily favored by voters who put more value on someone who “cares about people like me,” has good judgment and, especially, has the right experience.
Long before election night, Trump’s data operatives, in particular those contracted from Cambridge Analytica, understood that his voters were different. And to better understand how they differed from Ryan-style Republicans, they set off to study them.
The firm called these Trump supporters “disenfranchised new Republicans”: younger than traditional party loyalists and less likely to live in metropolitan areas. They share Bannon’s populist spirit and care more than other Republicans about three big issues: law and order, immigration, and wages.
They also harbored a deep contempt for the reigning political establishment in both parties, along with a desire to return the country to happier times. Trump was the key that fit in this lock.
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Predicting the Presidential Election: What Went Wrong?
…a good lesson for Data Scientists is to question their assumptions and to be especially skeptical when predicting a rare event with limited history using human behavior.
Machine Learning and AI Market Landscape, 2016
For the first time, a “one stop shop” of the machine intelligence stack is coming into view—even if it’s a year or two off from being neatly formalized. The maturing of that stack might explain why more established companies are more focused on building legitimate machine intelligence capabilities. Anyone who has their wits about them is still going to be making initial build-and-buy decisions, so we figured an early attempt at laying out these technologies is better than no attempt.
If this year’s landscape shows anything, it’s that the impact of machine intelligence is already here. Almost every industry is already being affected, from agriculture to transportation. Every employee can use machine intelligence to become more productive with tools that exist today. Companies have at their disposal, for the first time, the full set of building blocks to begin embedding machine intelligence in their businesses.
And unlike with the internet, where latecomers often bested those who were first to market, the companies that get started immediately with machine intelligence could enjoy a lasting advantage.
Country Ranking of IoT Preparedness
IDC:
[This is an] updated index ranking the Group of 20 (G20) nations on their preparedness for Internet of Things (IoT) development. The original index was first published in 2013 but this updated index is now comprised of 13 criteria that IDC views as necessary for sustained development of the IoT and reflects each nation’s economic stature, technological preparedness, and business readiness to benefit from the efficiencies linked to IoT solutions.
The United States, South Korea, and the United Kingdom ranked as the three countries most ready to generate and benefit from the IoT. The U.S. scored particularly well on measures such as ease of doing business, government effectiveness, innovation, and cloud infrastructure, as well as GDP and technology spending as a percent of GDP. South Korea, despite a modest GDP, scored extremely well on IoT-specific spending and has a business environment that fosters innovation and promotes attractive investment opportunities. Similarly, the U.K. scored very highly on measures of ease of doing business, government effectiveness, regulatory quality, start-up procedures, innovation, and broadband penetration.
The standout country in the ranking proved to be Australia, which, despite its relatively small GDP, scored exceptionally high on ease of doing business and start-up procedures, government effectiveness and regulatory quality, and innovation and education. Australia’s scores point to a country that has the necessary ingredients for a business environment that is ready for the growth of IoT.
Mobile advertising now accounts for nearly half of online ad budgets
Spending on mobile advertising in the US soared 89 per cent to $15.5bn in the first half of the year, taking up nearly half of online ad budgets, new data show. Mobile makes up 47 per cent of all online ad expenditures — up from 30 per cent a year ago and far surpassing the 19 per cent share taken by banner ads, according to a report from the Interactive Advertising Bureau and PwC, the professional services firm.
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9 Categories of Data Scientists
- Those strong in statistics: they sometimes develop new statistical theories for big data, that even traditional statisticians are not aware of. They are expert in statistical modeling, experimental design, sampling, clustering, data reduction, confidence intervals, testing, modeling, predictive modeling and other related techniques.
- Those strong in mathematics: NSA (national security agency) or defense/military people working on big data, astronomers, and operations research people doing analytic business optimization (inventory management and forecasting, pricing optimization, supply chain, quality control, yield optimization) as they collect, analyse and extract value out of data.
- Those strong in data engineering, Hadoop, database/memory/file systems optimization and architecture, API’s, Analytics as a Service, optimization of data flows, data plumbing.
- Those strong in machine learning / computer science (algorithms, computational complexity)
- Those strong in business, ROI optimization, decision sciences, involved in some of the tasks traditionally performed by business analysts in bigger companies (dashboards design, metric mix selection and metric definitions, ROI optimization, high-level database design)
- Those strong in production code development, software engineering (they know a few programming languages)
- Those strong in visualization
- Those strong in GIS, spatial data, data modeled by graphs, graph databases
- Those strong in a few of the above. After 20 years of experience across many industries, big and small companies (and lots of training), I’m strong both in stats, machine learning, business, mathematics and more than just familiar with visualization and data engineering. This could happen to you as well over time, as you build experience. I mention this because so many people still think that it is not possible to develop a strong knowledge base across multiple domains that are traditionally perceived as separated (the silo mentality). Indeed, that’s the very reason why data science was created.