This is the first of a three-post series on machine learning. Is machine learning just a fancy word for the same old computer programming we have employed for decades? Or, is machine learning a mystical computer that can learn anything? More importantly, why does it matter to your business? The most effective way to define machine learning is to compare it with traditional computer programming.

It is mostly corporate bullshit, but I do think it is noteworthy that Accenture is starting to take AI/ML seriously. Accenture is perhaps the world's largest consulting firm.

But what if you are not one of these? I received the following job ad in my mailbox (see below in italics), from a third-party recruiter, and it's probably for a data science position at Nike near Portland, Oregon (my guess). Basically, it's a 6-month gig to build an A/B platform. I discuss here a few aspects that make this job ad unlikely to attract talent, as well as remedies. This skills mix is not found in typical employees.

First up was KPMG, which announced that it is signing up to IBM's "Jeopardy!"-winning Given the time, cost and (dare I say it) inane world of audit, this could be an interesting move. IBM is, of course, pushing Watson incessantly as its huge opportunity for growth -- it acquired The Weather Company partially as a vertical big data opportunity, but also for its huge data sets to continue improving Watson's performance. Healthcare, too, has seen IBM push its Watson luxe. But now financial services are the game, and KPMG advises that its consultants will use Watson to analyze massive volumes of financial data in order to detect anomalies.

New Delhi: Accenture Operations, the 7-billion business segment of technology firm Accenture Plc, which deals with process outsourcing, infrastructure consulting and outsourcing, security and cloud services, is betting on automation platforms backed by artificial intelligence (AI) and machine learning for its next phase of growth, said Manish Sharma, senior managing director, Accenture Operations in an interview earlier this week. "Analytics and automation are critical and are high-focus areas for us. We are using automation technologies at scale for our client base," he said. "We are investing a lot of money and time into the AI piece. In automation, there is simple automation like Mini-Bots and then there is high-end automation solutions like Virtual Assistance and artificial or cognitive solutions," he explained.

Software's immersion within businesses, process and objects has expanded the scope of what could be automated. When you add in rapid advances in artificial intelligence, you start seeing a lot more interest in technologies that are increasing how well machine sense, learn and act. Investors are taking notice – investments in artificial intelligence start-ups by venture capitalists have increased roughly 20x in the last four years. What's your Intelligent Automation play?

Deloitte today announced an alliance with Kira Systems to bring the power of machine learning to the workplace, an innovation that could help free workers from the tedium of reviewing contracts and other documents. The alliance will combine Deloitte's business insights in cognitive technologies with Kira Systems' advances in machine-learning in creating models that quickly "read" thousands of complex documents, extracting and structuring textual information for better analysis. This capability holds broad applications for the marketplace, said Craig Muraskin, Deloitte LLP, managing director of Deloitte's US Innovation group, as the extensive review of documents goes into many pressing business activities, including investigations, mergers, contract management, and leasing arrangements. "Wading through miles of corporate jargon hunting for key words and patterns can consume considerable time and resources," said Muraskin. "By teaming with Kira Systems we can help organizations reduce their review time while redeploying talent to higher value activities--let's save our eyes for more strategic matters." Noah Waisberg, CEO of Kira Systems, said recent innovations by his company, such as Kira Quick Study, are graduating machine learning to new levels of accomplishment.

If you want to get a jump on the latest developments in machine intelligence, head down to where Manhattan's Lower East Side borders Chinatown, a still-gritty but gentrifying neighborhood where hip coffee houses are springing up next to industrial-supply outlets and wholesale stores. The location, an area in the midst of transition and modernization, is a fitting spot for Fast Forward Labs, a startup founded to help companies innovate and compete using what founder and CEO Hilary Mason calls "recently possible" machine intelligence techniques and technology. Fast Forward Labs has an unusual business model. It produces quarterly reports on emerging (or what Mason calls "near future") technology, builds prototypes to demonstrate the technology and offers advisory services. The business is based on a subscription model: For an annual fee, clients get access to the reports and prototypes plus time with Mason and her colleagues, who offer advice on how to apply the technology.

The advent of audit analytics and cognitive technology does not mean the end of human auditors. It means an end to painstaking checking and crossfooting of debit and credit entries and the beginning of auditing careers that thrive on understanding, monitoring, and improving analytical and cognitive systems. I have worked for a couple decades with professional services firms that perform financial audits, but I have never done one--nor have I ever wanted to do one, to be honest. I'm not good with work that involves structured processes, details, and rigorous checking, and audits always seemed heavily infused with those kinds of tasks. Now, however, I am becoming quite interested in audits for two reasons.

Artificial intelligence (AI), one of 20 core technologies I identified back in 1983 as the drivers of exponential economic value creation, is rapidly working its way into our lives from Amazon's Alexa and Facebook's M, to Google's Now and Apple's Siri. An example of how far AI has come is the recent news that a Google supercomputer, using its advanced AI software, was able to win a stunning 3-0 victory in a man vs. machine face-off against Go grandmaster Lee Sedol, one of the game's all-time champions. For those who are not familiar with Go, it is a 3,000-year-old game that is widely considered to be the most complex game ever invented because it is reported to have more possible board configurations than there are atoms in the universe. Until just a few months ago, it was thought that a computer could not defeat a human grandmaster for at least another decade due to the game's complexity. How did Google's AlphaGo program advance so much faster than many expected?