Two AI Trends That Will Change the World — And How To Benefit From Them (Part I)

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Artificial Intelligence / Big Data / Machine Learning

In a recent interview of Andrew Ng and Neil Jacobstein on AI, Wall Street Journal reporter Scott Austin claimed that, “artificial intelligence is shaping up as the next industrial revolution, poised to rapidly reinvent business, the global economy and how people work and interact with each other.” Andrew Ng is chief scientist at Chinese internet giant Baidu Inc. and co-founder of the education startup Coursera. Neil Jacobstein is chair of the artificial intelligence and robotics department at Silicon Valley think tank Singularity University.

The pair share several intriguing predictions and insights. Namely, Ng says that “in a few years everyone will be using speech recognition. It will feel natural. You’ll soon forget what it was like before you could talk to computers.”

Speaking to an even larger looming trend of the impact of AI on jobs, Ng claims that, “things may change in the future, but one rule of thumb today is that almost anything that a typical person can do with less than one second of mental thought we can either do now or in the very near future automate with AI.”

If artificial intelligence and machine learning are truly the technologies that will transform business — and society itself — similar to the overwhelming impact the industrial revolution had on the way we work, live and interact with others, what are the most salient trends entrepreneurs should be aware of as they build the coming AI economy? Read More

5 Ways Artificial Intelligence Can Make Your Business More Profitable

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Artificial Intelligence / Big Data / Machine Learning

1. Automation

Artificial intelligence is becoming so advanced that we’re currently seeing honorary disciplines being aided, though not yet replaced, with computerized assistance. Some customary journalistic tasks are already being carried out by AI, such as summarizing reports, analysis, and pulling data from A to B to gauge further intelligence. Other advancements are being made within the administrative field at a growing rate, with AI able to schedule meetings, note down important items, and take customer requests with minimal supervision.

2. Deeper and Better Insights

Without analysis, a lot of company data is largely useless. However, with more of business’ data being pulled into cloud servers, a growing, vast quota of information – big data – is constantly being surveyed for insights. This kind of new mobility can allow companies to make predictions based on older data, and can even offer prescriptive insights to forecasting future trends. Soon, restaurants could also be able to benefit from utilizing AI to discover specific insights, allowing them to determine which music they should play based on the data profiles of the patrons who’ve made reservations at their locations.
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Nobody To Hire? The Real-Life Artificial Intelligence Developer Talent Grab

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Artificial Intelligence / Machine Learning
artificial intelligence

2017 has been dubbed the year of artificial intelligence.

Deloitte’s 2017 Global Human Capital Trends report claims that AI has “revolutionized” the way people work and live while remaining vague on the details of how.

If our media is any indication of what trends are here to stay, AI and machine learning technologies are it—and their staying power appears to be multiplying.

From our driving, socializing and working habits, AI seems to be touching—or slowly creeping into—every aspect of our lives. And savvy entrepreneurs are paying attention to the clear, significant business opportunities that this new technological paradigm presents.

So, what is a resourceful startup to do? The immediate and obvious answer is: hire a developer, or several developers, with experience in AI and machine learning.

Developers are already notoriously in scarce demand across the nation, with Silicon Valley bearing the brunt of the shortage. With Google and Amazon snapping up engineers experienced in AI and claims that there are only several true AI experts in the field—most of whom reside in the nation’s leading universities—engaging an engineer with the right experience seems like a long-shot, especially as a startup.

But finding this breed of developer requires us to step back and re-assess what the term AI, thrown around so casually today, really means. Read More

What is the Difference Between Machine Learning and Big Data?

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Artificial Intelligence / Big Data / Machine Learning

Machine learning has been around for a while. What has made it less attractive — until now — has been the absence of large and robust datasets. Probably one of the most famous quotes defending the power of data is that of Google’s Research Director Peter Norvig claiming that “we don’t have better algorithms. We just have more data.”

In essence, the recent explosion of machine learning has been largely due to the availability of big data. But why?

Machine learning involves finding patterns in big data and learning from it. The core idea is that once past patterns in data are identified, the ability to predict future patterns—that often contain valuable insight—is realized. Machine learning can be applied to smaller datasets too; however, the insights will not be as accurate, because the learning opportunity, intimately tied to the dataset size, is too low.

So why exactly is big data critical, apart from the fact that it can enable machine learning tools to learn better and extract more valuable insights?

As Martin Hack, writing for Wired, explains:

Only with advanced analytics, and specifically machine learning, can companies truly tap into their rich vein of experience and mine it to automatically   discover insights and generate predictive models to take advantage of all the data they are capturing. This advanced analytics technology means that instead of looking into the past for generating reports, businesses can predict what will happen in the future based on analysis of their existing data. The value of machine learning is rooted in its ability to create accurate models to guide future actions and to discover patterns that we’ve never seen before.

But what is the difference between big data and machine learning exactly? Read More