IBM's Watson artificial intelligence platform is getting a new enhancement that will help the system detect human emotion in customer service situations. According to a blog post published Thursday, IBM Watson distinguished engineer and master inventor Rama Akkiraju said the new Tone Analyzer for Customer Engagement tool is designed to help customer service agents and chatbots craft appropriate responses to frustrated, sad, or satisfied customers. Through linguistic analysis, the tool can pick up on seven different types of tone via conversations with customer service agents and chatbots: frustration, satisfaction, excitement, politeness, impoliteness, sadness and sympathy. The system also claims to be able to detect these sentiments in emojis, emoticons, and slang. The Tone Analyzer was developed with a machine learning algorithm that trained on customer support conversations on Twitter.

Artificial intelligence (AI) is well and truly on the map, according to IBM CFO Martin Schroeter. The exec told analysts on a Q4 earnings call Thursday that the debate about whether the technology is real or not is "over". Schroeter was specifically referring to AI's utility in addressing business problems, citing IBM's stepped-up deployment of its Watson AI platform as an enterprise-level, problem solver for vertical industries serves as evidence. Schroeter called Watson the "silver thread" in IBM's cognitive solutions portfolio, but stopped short of terming it a lever significant enough to overcome the vendor's nearly five-year sales tumble. In IBM's just completed Q4, its cognitive solutions sales rose a scant 1.4 percent to $5.3 billion.

Artificial intelligence (AI) on its own isn't enough to compete – companies need industry-specific solutions to business problems. So said Martin Schroeter, IBM's senior vice-president and chief financial officer, on the company's quarterly earnings call. Cognitive computing technology (IBM's term for AI) is just "table stakes," said Schroeter, claiming that his company is going the extra mile. IBM is building datasets for Watson to serve specific industries, including healthcare and finance. "You need more than public data or algorithms to solve real-world problems," Schroeter said.

Artificial intelligence (AI) on its own isn t enough to compete -- companies need industry-specific solutions to business problems. So said Martin Schroeter, IBM Corp. (NYSE: IBM) s company senior vice president and chief financial officer, on the company s quarterly earnings call Thursday afternoon. Cognitive computing technology (IBM s term for AI) is just "table stakes," said Schroeter, claiming that his company is going the extra mile. IBM is building datasets for Watson to serve specific industries, including healthcare and finance. "You need more than public data or algorithms to solve real-world problems," Schroeter said.

Artificial intelligence (AI) on its own isn't enough to compete -- companies need industry-specific solutions to business problems. So said Martin Schroeter, IBM Corp. (NYSE: IBM)'s company senior vice president and chief financial officer, on the company's quarterly earnings call Thursday afternoon. Cognitive computing technology (IBM's term for AI) is just "table stakes," said Schroeter, claiming that his company is going the extra mile. IBM is building datasets for Watson to serve specific industries, including healthcare and finance. "You need more than public data or algorithms to solve real-world problems," Schroeter said.

IBM's chief financial officer, Martin Schroeter, made a bold statement on the future of technology on Thursday -- and he got some pushback. "The debate about whether artificial intelligence is real is over," Schroeter said, referring to IBM's cognitive computing platform, Watson. "And we're getting to work to solve real business problems." Katy Huberty, managing director at Morgan Stanley, challenged IBM on the topic during Thursday's earnings conference call. Huberty: "My other question is Watson: From the outside, it seems this business gets a pretty significant share of the press, but not contributing to revenue. Do you have visibility yet to when we can expect an inflection in revenue recognition from Watson? Or should we just not think about this as a contributing factor, or moving the needle in our models over the next couple of years? Schroeter: "....Watson is a silver thread.

IBM has given its investors a sign that Big Blue may finally be turning things around. Revenue increased for the first time in a key unit -- cognitive solutions, including its Watson artificial intelligence platform -- that the company has been touting as crucial to future growth. The results may signal that Chief Executive Officer Ginni Rometty is making good on her promise to shift IBM's software and services offerings to match customers' increasing appetite for cloud-based solutions. It's been an uphill battle. Overall, sales have declined for 17 quarters in a row, while margins have also narrowed.

Once your business is no longer the innovative upstart and you become the establishment entity, how do you maintain an entrepreneurial and disruptive spirit that gets results? That's the question Apple had to ask itself this week, following an iffy earnings report. This week, Apple posted the earnings results for the second quarter of 2016, and reported a year-over-year decline in quarterly revenue for the first time in 13 years. The company took in 50.6 billion in quarterly revenue and 10.5 billion in quarterly net income. On a call with investors, CEO Tim Cook characterized that 13 percent dip in revenue as a "pause in our growth," that had stemmed from "ongoing macroeconomic headwinds in much of the world." Despite the break in the company's decade plus streak of "record" growth, it's unlikely that the tech giant's standing as one of most valuable and authentic brands in the world will be dinged in any significant way.

Apply now to be an Enterpreneur360 company and let us tell the world your success story. Once your business is no longer the innovative upstart and you become the establishment entity, how do you maintain an entrepreneurial and disruptive spirit that gets results? That's the question Apple had to ask itself this week, following an iffy earnings report. This week, Apple posted the earnings results for the second quarter of 2016, and reported a year-over-year decline in quarterly revenue for the first time in 13 years. The company took in 50.6 billion in quarterly revenue and 10.5 billion in quarterly net income. On a call with investors, CEO Tim Cook characterized that 13 percent dip in revenue as a "pause in our growth," that had stemmed from "ongoing macroeconomic headwinds in much of the world."

After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted the particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.