By now we have all heard the buzz. Siri has plenty of company. Alexa and Google have invaded the home. Tesla’s artificial intelligence is, well, driving itself. Google’s Deep Mind is running the company’s multi-billion-dollar search business. Even they don’t know how it works — it just does. IBM’s Watson has its own office and is predicting cancer treatments for real patients. You can’t even date online without artificial intelligence (A.I.) hooking you up.
There are even A.I.-based autonomous ships, bots, games and investment and commerce platforms that are emerging. Eventually A.I. will be everywhere. Are you ready for the A.I. revolution? Of course, the answer is no one is ready, because this is only A.I. 1.0 — the beginning of time. So let’s review.
Artificial intelligence has finally emerged, and the prospects for it transforming the enterprise are excellent. Every enterprise should be looking at what blend of A.I. they want to investigate. From machine learning to neural nets, recommendation engines and decision bots — the landscape of the marketplace is full of choices. The real question I would pose is, “What should CIOs be telling their CEOs about the A.I. revolution?” The answer: “Show me the business strategy.”
I would challenge CIOs to work out the strategy question first. What is the business strategy that would benefit and even justify adding artificial intelligence? How could we generate a competitive advantage from investing in machine learning or automated decision support that A.I. could offer? There needs to be a business value or competitive advantage that investing in A.I. makes clear. If not, then you are not ready. Or don’t invest much until you can answer this strategic question.
Too many companies are so impressed with A.I. buzz that they are moving headlong into A.I. without clarifying what the business strategy case could be. Are we going to be able to generate better customer service? Help employees make better decisions? Enable our customer to use more of our products or services? How about fix that problem that those pesky humans don’t seem to be able to handle? How will we be able to monetize artificial intelligence?
AI and trust and respect – first impressions matter!
We are hardwired for judgment. Our paths up from the primordial soup have imbued in us the spirit of quick conclusions, especially when it comes to one another. As Harvard Business School psychologist Amy Cuddy puts it, we size each other up along two key questions: Can I respect this person? Can I trust this person?
Westboro, MA – February 13, 2017 – Trends Equity, Inc. has announced that AI World Conference & Expo 2017 – www.aiworld.com – will be held September 18-20, 2017 at the Boston Marriott Copley in Boston, MA. Now in its’ second year, AI World has become the industry’s largest event focused on the state of the practice of enterprise AI and machine learning. AI World’s mission is to enable enterprise business and technology executives to learn how to successfully harness intelligent technologies to build competitive advantage, drive new business opportunities and accelerate innovation efforts.
The machines haven’t taken over. Not yet at least. However, they are seeping their way into our lives, affecting how we live, work and entertain ourselves. From voice-powered personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, suggestive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today.
However, the technology is still in its infancy. What many companies are calling A.I. today, aren’t necessarily so. As a software engineer, I can claim that any piece of software has A.I. due to an algorithm that responds based on pre-defined multi-faceted input or user behavior. That isn’t necessarily A.I.
A true artificially-intelligent system is one that can learn on its own. We’re talking about neural networks from the likes of Google’s DeepMind, which can make connections and reach meanings without relying on pre-defined behavioral algorithms. True A.I. can improve on past iterations, getting smarter and more aware, allowing it to enhance its capabilities and its knowledge.
Congress set up the H-1B program to help American companies hire foreigners with exceptional skills, to fill open jobs and to help their businesses grow.
But the program has been failing many American employers who cannot get visas for foreigners with the special skills they need.
Instead, the outsourcing firms are increasingly dominating the program, federal records show. In recent years, they have obtained many thousands of the visas — which are limited to 85,000 a year — by learning to game the H-1B system without breaking the rules, researchers and lawyers said.
In some years, an American employer could snag one of these coveted visas almost anytime. But recently, with the economy picking up, the outsourcing companies have sent in tens of thousands of visa requests right after the application window opens on April 1. Employers who apply after a week are out of luck.
“The H-1B program is critical as a way for employers to fill skill gaps and for really talented people to come to the United States,” said Ronil Hira, a professor at Howard University who studies visa programs. “But the outsourcing companies are squeezing out legitimate users of the program,” he said. “The H-1Bs are actually pushing jobs offshore.”
#H-1B #Visas #Students
Which mainstay IT certifications should be in your list of credentials? What’s the next up-and-coming certification? It can all be very confusing. Global Knowledge Group recently published a survey identifying the top paying IT certifications in 2016. 6 of those Top 15 are in IT Security, which is reflective of what we’re seeing in the employment market. Information Technology is hot, and Security is extremely hot.
This article can help you answer these questions by providing a review of the 15 top-paying certifications. They provide a brief description of each, as well as the average salary that each certification commands based on the 2016 IT Skills and Salary Survey that Global Knowledge conducted in the fall of 2015. Checkout the survey results:
The second directive to the assembled pre-teens at the first Monday caddy school was to “shut up!” I mistakenly thought our caddymaster Pat Higgins meant that only for when we were on the course with our assigned loop. I learned (not too painfully because I had three older brothers caddying) that it was even more important in the caddy yard and around the caddy shack. Someone once asked me “if you could give your early professional self two words of advice what would they be?” My answer without hesitation – “shut up.”
I was told I was a pretty smart kid but early in my career I couldn’t stop trying to show that I was the smartest guy in the room. It wasn’t usually with my superiors as I guess I viewed them as the “golfers I was caddying for” but it was with my co-workers, fellow caddies if you will. I was 10 years into my professional career and in my second tour of public accounting before a partner in a law firm who our firm shared a client with told me (politely) to shut up again. I was in mid-sentence of telling him how I was going to present information at a meeting he had called with the four owners of this client when he said “actually you don’t need to be prepared to say anything. If there is a question I believe you should answer I’ll ask you, so just know your material.” I was really perturbed on the inside but in short order I understood what a blessing it was to hear that “Shut Up” lesson again.
Just a reminder to ask good questions and listen carefully to the responses before being the one with all the answers. My lesson came from Michael J. Burke, Sr. who at the time was Managing Partner of the Keating Muething & Klekamp Law Firm. Mike passed away far too soon in December 2001 and I’m certain I’m not the only one who is carrying important lessons they learned from him.
FROM THE HBR.ORG INSIGHT CENTER “FROM DATA TO ACTION” | 2© 2014 Harvard Business Publishing. All rights reserved.
WHAT TO ASK YOUR “NUMBERS PEOPLE” BY TOM DAVENPORT
If you’re a manager working with the analysts in your organization to make more data-driven business decisions, asking good questions should be one of your top priorities. Many managers fear that asking questions will make them appear unintelligent about quantitative matters. However, if you ask the right kinds of questions, you can both appear knowledgeable and advance the likelihood of a good decision outcome. In my new book (co-authored with Jinho Kim) Keeping Up with the Quants, and in a related article in this month’s HBR, we list a lot of possible questions for various stages of analysis. But in this short article, I thought it might be useful to mention not only a couple of the most important questions you can ask about data, but also what some of the ensuing dialogue might involve.
1. Questions about assumptions You ask: What are the assumptions behind the model you built? You think in response to the answer: If your numbers person says there are no particular assumptions, you should worry—because every model has assumptions behind it. It may be only that you’re assuming that the sample represents a population, or that the data gathered at a previous time are still representative of the current time. Follow-up: Is there any reason to believe that those assumptions are no longer valid? You think in response: You are really looking only for a thoughtful response here. The only way to know for sure about whether assumptions still hold is to do a different analysis on newly gathered data— which could be very expensive. Perhaps a particular relationship holds only when the values of a variable are moving in a particular direction (e.g., “This mortgage risk model holds true only when housing prices are going up”—nah, that could never change!).
For all IT Professionals who talk and wonder about BIG DATA…Consider what a DATA LAKE is all about! A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business question arises, the data lake can be queried for relevant data, and that smaller set answer the question.
“Only three things you need to know to be successful” according to my first boss, “show up, shut up, keep up.” I’m actually not certain if my first caddymaster Pat Higgins said it or if after hearing it during my Evans Scholar days at Miami University I just attributed it to him, but as time went on I learned just how universally true those keys to success were.
A friend shared that he recently had to terminate an employee and it emphasized once again that it all begins with Show Up! A hard to fill position was finally filled with a well qualified professional. During the first two weeks there are some problems with getting to work on time, and not just those nagging few minutes. My friend did the right thing and had a candid discussion about the culture of the organization and how timeliness really matters. Things got a little better for a couple of weeks but as my friend said, “I don’t think he really got it.” Friday morning another late arrival and another meeting where it is made clear that future late arrival could result in termination.
You guessed it – late arrival on Monday. This talented professional is met in the lobby by my friend and the Human Resources Director and thus ends a promising career opportunity. Harsh as it may seem, like my caddymaster said – Show Up, and yes On Time!