Daily Archives: June 9, 2017

Artificial intelligence, AI, is all the rage these days -- analysts are proclaiming it will change the world as we know it, vendors are AI-washing their offerings, and business and IT leaders are taking a close look at what it can potentially deliver in terms of growth and efficiency.

For people at the front lines of the revolution, that means developing and honing skills in this new dark art. In this case, AI requires a blend of programming and data analytics skills, with the necessary business overlay.

In a recent report at the Dice site, William Terdoslavich explores some of the skills people will need to develop a repertoire in the AI space, noting that these skills are in high demand, especially with firms such as Google, IBM, Apple, Facebook, and Infosys absorbing all available talent.

Machine learning is the foundational skill for AI, and online courses such as those offered through Coursera offer some of the fundamental skills. Abdul Razack, senior VP and head of platforms at Infosys, notes that another way to develop AI expertise is to "take a statistical programmer and training them in data strategy, or teach more statistics to someone skilled in data processing."

Mathematical knowledge is also foundational, Terdoslavich adds, requiring a "solid grasp of probability, statistics, linear algebra, mathematical optimization--is crucial for those who wish to develop their own algorithms or modify existing ones to fit specific purposes and constraints."

Programs popular with AI developers include R, Python, Lisp, Prolog and Scala, Terdoslavich's article states. Older standbys -- such as C and C++ and Java -- are also being employed, depend upon applications and performance requirements. Platforms and toolsets such as TensorFlow also provide AI capabilities.

Ultimately, becoming adept in AI also requires a degree of a change in conceptual thinking as well, requiring deductive reasoning and decision-making.

AI skills -- again, which blend expertise n programming, data, and business development -- may continue to be in short supply, and David Kosbie, Andrew W. Moore, and Mark Stehlik sounded the alarm in a recent Harvard Business Review article, calling for an overhaul of computer science programs at all levels of education. AI is "not something a solitary genius cooks up in a garage," they state. "People who create this type of technology must be able to build teams, work in teams, and integrate solutions created by other teams."

This requires a change in the way programming is taught, they add. "We're too often teaching programming as if it were still the 90s, when the details of coding (think Visual Basic) were considered the heart of computer science. If you can slog through programming language details, you might learn something, but it's still a slog -- and it shouldn't be. Coding is a creative activity, so developing a programming course that is fun and exciting is eminently doable."

What's in demand right now in terms of AI skills? A perusal through current job listings yields the following examples of AI jobs:

Senior software developer - artificial intelligence and cognitive computing (insurance company): "Lead the application prototyping and development for on premise cognitive search and analytics technologies. Candidate should have experience with AI, machine learning, cognitive computing, text analytics, natural language processing, analytics and search technologies, vendors, platforms, APIs, microservices, enterprise architecture and security architecture."

Artificial intelligence engineer: (aerospace manufacturer): "Will join a fast-paced, rapid prototyping team focused on applied artificial intelligence. Basic qualifications: 5 years experience in C/C++ or Python. Algorithm experience. Experience with machine learning and digital signal processing (computer vision, software defined radio) libraries."

Artificial intelligence innovation leader (financial services firm): "Oversee strategic product development, product innovation and strategy efforts. Evaluate market and technology trends, key providers, legal/regulatory climate, product positioning, and pricing philosophy.... Work closely with IT to evaluate technology viability and application. Qualifications: 7+ years of senior level management experience, PhD/masters in computer science, AI, cognitive computing or related field."

Artificial intelligence/machine learning engineer (Silicon Valley startup): "Deal with large-scale data set with intensive hands-on code development. Collect, process and cleanse raw data from a wide variety of sources. Transform and convert unstructured data set into structured data products. Identify, generate, and select modeling features from various data set. Train and build machine learning models to meet product goals. Innovate new machine learning techniques to address product and business needs. Analyze and evaluate performance results from model execution." Qualifications: "Strong background and experience in machine learning and information retrieval. Must have experience managing end-to-end machine learning pipeline from data exploration, feature engineering, model building, performance evaluation, and online testing with TB to Petabyte-size datasets."

Read the rest here:

What it takes to build artificial intelligence skills - ZDNet

Analyzing a company's earnings call gives an investor a first hand heads-up on the company's latest status with regards to operational and financial health. Investors can read the transcript, look at the numbers, and draw their own conclusions.

In addition to the traditional approach of evaluating an earnings call, we used our Artificial Intelligence engine to objectively analyze a call transcript. The purpose of this exercise is to acquire additional insights directly from the company's perspective. This write-up focuses on the Executive Statement from the IBM (NYSE:IBM) 2017 Q1 Earnings Call.

The following is a summary of findings:

Analytics with Artificial Intelligence

Our AI Analytics is based on symbolic logic and propositional calculus. In other words, our algorithm discovers symbols that represent some level of importance based on propositional logic to drive a causational model. The causational model seeks out supporting context surrounding these situations. Thus, for each of the points, we expect AI to tell us the rationale.

In a nutshell, the AI part of the analysis is to read the transcript like a human researcher and bring out positive points, negative points, and points with both positive and negative aspects. It does so in an objective way using Meta-Vision.

Our AI analysis of the earnings call Executive Statement resulted in the following Meta-Vision:

Meta-Vision Legend:

Our AI engine discovers important points we call 'Meta-Objects'. There are two type of Meta Objects, namely, Machine Generated Hashtag (or MGH) nodes and Supporting Fact (or SF) nodes. MGH nodes are important points discovered by CIF from the given dataset. SF nodes are the text that is being analyzed. 'Meta-Vision' is the topological mapping of Meta-Objects across a quadrant chart by semantics, context, and polarity. The quadrant chart connects Meta-Objects (MGH and SF nodes) by edges to depict their respective relationships. Clicking on a node opens a new window showing corresponding context for that node. The North-East "NE" quadrant is called the "common-positive quadrant." The North-West "NW" quadrant is called the "common-negative quadrant." The South-West "SW" quadrant is the "negative quadrant." The South-East "SE" quadrant is the "positive quadrant." The name of each quadrant denotes the connotation (common, negative, positive). Placement of nodes are determined by the AI. Machine generated hashtag nodes are labeled. The relative location from the X-axis denotes the strength of a MGH node. The closer the FN nodes are to the center, the higher the number of MGH nodes that it supports.

For each of the important points (MGH node), the co-ordinate indicates the connotation. Clicking a MGH will bring out all the corresponding quotes in verbatim from the transcript (supporting facts and context). MGH nodes are also connected to fact nodes. Each Fact node represents the excerpts from the original document. Clicking a fact node will bring out the semantic and sentiment analytics on that excerpt.

In summary, without any human interaction or influence, our AI algorithm has determined that the following points, represented by machine generated hashtags, are negatively stated in the earnings call: #Income, #GBS, #Earning, #Workforce

Our AI algorithm determined that the following points, represented by machine generated hashtags, are positively stated in the earning call: #Cloud, #Solutions, #Digital, #Profit, #Investment, #IBM

Our AI algorithm determined two points carried a negative connotation, but also has positive aspects. They are: #Software, #Track

Our AI algorithm determined that the following points contained both positive and negative supporting facts, while the positive supporting facts are dominant: #Margin, #Client

Our AI algorithm determined that the following points contained both negative and positive supporting facts, while the negative supporting facts are dominant: #Performance, #Revenue

Evaluating the Executive Statement with Meta-Vision

Based on our examination, we identified strategic points and corresponding supporting facts. We did so with the following agendas in mind:

The following are points (MGH nodes) that we picked out are based on the above criteria:

#income #workforce

#gbs

#cloud

#ibm

#margin, #solutions, #profit

#clients

Deriving Insights through Bionic Fusion

While the details of the technology behind the analysis is beyond of scope of this article, the general concept is not difficult to understand. The idea is to equip a software system with the ability to master a language, such as English, to the equivalent of a graduate student or researcher who can learn a core subject from a lecture or research medium. In this scenario, the medium uses English to introduce new subjects. In the process of knowledge transfer, the medium draws relationships between subjects and expresses the properties of the underlying context. The researcher, using English as a medium, can learn any subject and acquire new knowledge by listening to lectures. In a similar manner, the software system uses visual charts to depict the discovered subjects, relationships, underlying context, properties, and references to source documents. When a user navigates through these properties, together with human thinking, it forms a bond of bionic fusion which enables the user to gain insights by drawing inference from these visuals.

The AI algorithm did the work of identifying important points, connotation, and supporting facts. We examined each point and supporting fact to draw inference into perceived strengths and weaknesses. To corroborate our findings, we also referred to our enterprise data lake for business intelligence around competitive marketspace and external market forces.

RE: GBS, Strategic Imperatives

If management saw growth in its Strategic Imperatives, IBM would need the following:

This needs upfront investment, a substantial increase in human capital, and a faster time to market with industry-specific vertical applications. This proposition is contradicted by the decline in Global Business Services (or GBS). If management was dedicated to building a backlog and pipeline in its GBS unit, the subsequent rebalance of workforce should result in an increase in expense. Judging from the continuing rebalancing of workforce in the negative column, and the need to build industry specific solutions, GBS will have problems with scale. Customers cannot put their business on hold and will seek for alternative competitive solutions in the marketplace such as open source or off-the-shelf solutions. Consequently, we do not believe that management is confident in GBS pipeline growth.

RE: Cloud

IBM is transforming their business into a 'data and cloud first' company. The super set of cloud business consists of private cloud (enterprise cloud), public cloud, and hybrid cloud. IBM's cloud is not a public cloud like Amazon (NASDAQ:AMZN)'s AWS offering. IBM only focuses on enterprise. The public cloud space has a market cap that is projected to exceed $500 billion by 2020. IBM's Executive Statement did not reflect any initiative that would position IBM for a share of this huge market. The enterprise cloud space has major competitors such as HP (NYSE:HPE), Microsoft (NASDAQ:MSFT), and Google (NASDAQ:GOOG). Moreover, IBM's enterprise cloud is a service that will compete with IBM's legacy mainframe business for the same customer IT budget. IBM recognizes that this shift will require a level of investment a longer return profile which is already being reflected in their margins and will require continued investment.

RE: Cognitive

Cognitive is industry-specific. It will cost substantial time and additional investment in building each of the vertical problem domains. Artificial Intelligence is becoming a crowded market. IBM will have to compete with new startups. Time, cost and efficiency will weigh against IBM just like its legacy Personal Computing and server business. Technology is changing at a fast pace; custom-built solutions that takes years to materialize will face obsolescence before it is put to use.

Conclusion:

Products and services that make up the Strategic Imperatives are part of the "red-ocean" in a crowded market. If Strategic Imperatives as identified by IBM is its main turnaround strategy, it is going to face a lot of competition. Based on the Meta-Vision analysis of IBM's 2017 Q1 earnings call, we do not see any counter initiatives that will improve IBM's outlook in near-term.

Additional Notes - Process of Analysis:

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Additional disclosure: I am neither a certified investment advisor nor a certified tax professional. The data presented here is for informational purposes only and is not meant to serve as a buy or sell recommendation. The analytic tools used in this analysis are products of SiteFocus.

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An Artificial Intelligence Retrospective Analysis Of IBM 2017 Q1 Earnings Call - Seeking Alpha

Forbes

How Will Artificial Intelligence Change Healthcare? Forbes How will AI change healthcare? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. Answer by Abdul Hamid Halabi, Business Lead, Healthcare & Life Sciences at ... and more »

Excerpt from:

How Will Artificial Intelligence Change Healthcare? - Forbes

In BriefThe line between human and artificial intelligence isincreasingly blurring. When AI software isn't too busy beatinghumans at their favorite games, they are also finding time tocompose music, write movies, and edit film trailers. Soon no onemay be able to point out AI artists amidst humans. Intelligence Challenge

Lets start with a little challenge: which of the following tunes was composed by an AI, and which by an HI (Human Intelligence)?

Ill tell you at the end of the answer which tune was composed by an AI and which by an HI. For now, if youre like most people, youre probably unsure. Both pieces of music are pleasing to the ear. Both have good rhythm. Both could be part of the soundtrack of a Hollywood film, and you would never know that one was composed by an AI.

And this is just the beginning.

In recent years, AI has managed to

Now, dont get me wrong: most of these achievements dont even come close to the level of an experienced human artist. But AI has something that humans dont: its capable of training itself on millions of samples, and constantly improve itself. Thats how Alpha Go, the AI that recently wiped the floor with Gos most proficient players, got so good at the game: it played a few million games against itself, and discovered new strategies and best moves. It acquired an intuition for the game, and kept rapidly evolving to improve itself.

And theres no reason that AI wont be able to do that in art as well.

In the next decade, well see AI composing music and even poems, drawing abstract paintings, and writing books and movie scripts. And itll get better at it all the time.

So what happens to art, when AI can create it just as easily as human beings do?

For starters, we all benefit. In the future, when youll upload your new YouTube clip, youll be able to have the AI add original music to it, which will fit the clip perfectly. The AI will also write your autobiography just by going over your Facebook and Gmail history, and if you want will turn it into a movie script and direct it too. Itll create new comic books easily and automatically both the script and the drawing and coloring part and whats more, itll fit each story to the themes that you like. You want to see Superman fighting the Furry Triple-Breasted Slot Machines of Pandora? You got it.

Thats what happens when you take a task that humans need to invest decades to become really good at, and let computers perform it quickly and efficiently. And as a result, even poor people will be able to have a flock of AI artists at their beck and call.

At this point you may ask yourselves what all the human artists will do at that future. Well, the bad news is that obviously, we wont need as many human artists. The good news is that those few human artists who are left, will make a fortune by leveraging their skills.

Let me explain what I mean by that. Homer is one of the earliest poets we know of. He was probably dirt poor. Why? Because he had to wander from inn to inn, and could only recite his work aloud for audiences of a few dozen people at the time, at most. Shakespeare was much more succesful: he could have his plays performed in front of hundreds of people at the same time. And Justin Bieber is a millionnaire, because he leverages his art with technology: once he produces a great song, everyone gets is immediately via YouTube or by paying for and downloading the song on iTunes.

Great composers will still exist in the future, and they will work at creating new kinds of music and then having the AI create variations on that theme, and earning revenue from it. Great painters will redefine drawing and painting, and they will teach the AI to paint accordingly. Great script writers will create new styles of stories, whereas the old AI could only produce the old style.

And of course, every time a new art style is invented, itll only take AI a few years or maybe just a few days to teach itself that new style. But the human creative, crazy, charismatic artists who created that new style, will have earned the status of artistic super-stars by then: the people who changed our definitions of what is beautiful, ugly, true or false. They will be the people who really create art, instead of just making boring variations on a theme.

The truly best artists, the ones who can change our outlook about life and impact our thinking in completely unexpected ways, will still be here even a hundred years into the future.

Oh, and as for the two tunes? The first one was composed by a human being and performed by Morten Faerestrand in his YouTube clip 3 JUICY jazz guitar improv tools. The second was composed by the Algorithmic Music Composer and demonstrated in the YouTube clip Computer-Generated Jazz Improvisation.

Did you get it right?

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Artificial Intelligences are Quickly Becoming Better Artists - Futurism

How AI is changing the nature of cybersecurity (iStock) How AI is changing the nature of cybersecurity (iStock)

Speed of Change

Published Friday, Jun. 09, 2017 1:08PM EDT

Last updated Friday, Jun. 09, 2017 1:08PM EDT

With the rise of cloud-based apps and the proliferation of mobile devices, information security is becoming a top priority for both the IT department and the C-Suite. Organizations enthusiastic about the Internet of Things (IoT) are equally guarded as global cyberattacks continue to dominate headlines.

Businesses ranging from startups to large corporations are increasingly looking to new technologies, like artificial intelligence (AI) and machine learning, to protect their consumers. For cybersecurity, AI can analyze vast amounts of data and help cybersecurity professionals identify more threats than would be possible if left to do it manually. But the same technology that can iimprove corporate defences can also be used to attack them.

On Wednesday, June 28 at 11:30 a.m. (ET), Aleksander Essex will join us for a live discussion on the impact of artificial intelligence on cybersecurity. Essex is the head of Whisper Lab, a cyber-security research group at Western University, and an associate professor of software engineering and a speciality in cryptography.

To leave a question or comment in advance of the discussion, please fill in the comment field below.

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Q&A: How artificial intelligence is changing the nature of cybersecurity - The Globe and Mail