Depending on which Terminator movies you watch, the evil artificial intelligence Skynet has either already taken over humanity or is about to do so. But it's not just science fiction writers who are worried about the dangers of uncontrolled AI. In a 2019 survey by Emerj, an AI research and advisory company, 14% of AI researchers said that AI was an "existential threat" to humanity. Even if the AI apocalypse doesn't come to pass, shortchanging AI ethics poses big risks to society -- and to the enterprises that deploy those AI systems. Central to these risks are factors inherent to the technology -- for example, how a particular AI system arrives at a given conclusion, known as its "explainability" -- and those endemic to an enterprise's use of AI, including reliance on biased data sets or deploying AI without adequate governance in place.

Current chatbots tend to be awkward and even agonizing to use, limited to answering a set of simple queries -- often incorrectly. But what if a chatbot could be designed to support more complex and multistep tasks, such as organizing a daily schedule or pinpointing a fault lurking inside a complex mechanical device? Conversational AI technology promises all of this and more. Conversational AI is built on natural language processing (NLP) and other machine learning (ML) technologies, with the goal of enabling human-like interactions between machines and people. So far, conversational AI has mostly been used to create sophisticated chatbots -- as opposed to scripted, rules-based chatbots.

When it comes to artificial intelligence, don't try to go it alone. IT departments, no matter how skilled and ready, can only go so far past proofs of concept. Industry experts say that AI initiatives need everyone across the enterprise on board. "A copious amount of training data and elastic compute power are not the cornerstones for successful AI implementations," says Sreedhar Bhagavatheeswaran, global head of Mindtree Consulting. That cornerstone of AI success is people -- not only people with AI skills, but also those from all disciplines, from marketing to supply chain management.

Organizations seeing the most benefits from Artificial Intelligence (AI) work are more likely to be true believers in cognitive capabilities. Indeed, AI high performers, as identified by McKinsey, invested more of their digital budgets in AI than their counterparts, were more likely to increase their AI investments in the next three years, and employ more AI-related talent, such as data engineers, data architects, and translators, than their counterparts. Winning over the end users of AI-enabled capabilities is just as – if not more – important to your success. "Winning support for AI across the business is crucial for CIOs and other IT leaders hoping to scale their programs," says Dan Simion, vice president of AI & Analytics at Capgemini North America. The implementation of AI across the entire Lenovo organization is enabling greater efficiency and effectiveness.

Pre-pandemic, artificial intelligence was already poised for huge growth in 2020. Back in September 2019, IDC predicted that spending on AI technologies would grow more than two and a half times to $97.9 billion by 2023. Since then, COVID-19 has only increased the potential value of AI to the enterprise. According to McKinsey's State of AI survey published in November 2020, half of respondents say their organizations have adopted AI in at least one function. "As the grip of the pandemic continues to affect the ability of the enterprise to operate, AI in many guises will become increasingly important as businesses seek to understand their COVID- affected data sets and continue to automate day-to-day tasks," says Wayne Butterfield, director of ISG Automation, a unit of global technology research and advisory firm ISG.

In the wake of the COVID-19 pandemic, there has been a rapid increase in the deployment of artificial intelligence (AI) systems due to an increased need for automation, advanced analytics, and remote work. In fact, machine learning and other forms of AI are being applied to address the increasing scale of the pandemic itself. Now, say experts, is a good time to step back and consider the ethics of these (and all) AI applications. "These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivaled pace has left the internet and technology watchers aghast," Abhishek Gupta, founder of the Montreal AI Ethics Institute, said in the introduction to that organization's inaugural State of AI Ethics report this June. "It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other."

Artificial intelligence (AI) capabilities, from machine learning and deep learning to natural language processing (NLP) and computer vision, are rapidly advancing. "Technology has never moved at such pace, meaning the role of the CIO is harder than ever to stay current and up to date with technology overall, so understanding the vast array of AI capabilities is a stretch for most CIOs right now," says Wayne Butterfield, director of cognitive automation and innovation technology research at advisory firm ISG. Naturally, IT leaders are increasingly exploring AI applications in the enterprise. However, AI-enabled initiatives do not necessarily lend themselves to traditional IT approaches. "It is imperative for CIOs to know AI in reasonable depth to understand its realistic and pragmatic adoption," explains Yugal Joshi, vice president of digital, cloud, and application services research for Everest Group.

Artificial intelligence (AI) capabilities, from machine learning and deep learning to natural language processing (NLP) and computer vision, are rapidly advancing. "Technology has never moved at such pace, meaning the role of the CIO is harder than ever to stay current and up to date with technology overall, so understanding the vast array of AI capabilities is a stretch for most CIOs right now," says Wayne Butterfield, director of cognitive automation and innovation technology research and advisory firm ISG. Naturally, IT leaders are increasingly exploring AI applications in the enterprise. However, AI-enabled initiatives do not necessarily lend themselves to traditional IT approaches. "It is imperative for CIOs to know AI in reasonable depth to understand its realistic and pragmatic adoption," explains Yugal Joshi, vice president of digital, cloud, and application services research for Everest Group.

The adoption of artificial intelligence (AI) in the enterprise continues: More than half (58 percent) of respondents to McKinsey & Company's recent global AI survey say their organizations have embedded at least one AI capability into a process or product in at least one function or business unit, up from 47 percent in 2018. Those increases were reported across all industries. What's more, nearly a third (30 percent) are using AI in products or processes across multiple business units and functions, McKinsey's data says. But, as the McKinsey research and others point out, some organizations are much further along in scaling their AI initiatives. What are teams succeeding with AI doing that others can emulate to propel their efforts?

Recently, a new convex formulation of IBM Model 2 was introduced. In this paper we develop the theory further and introduce a class of convex relaxations for latent variable models which include IBM Model 2. When applied to IBM Model 2, our relaxation class subsumes the previous relaxation as a special case. As proof of concept, we study a new relaxation of IBM Model 2 which is simpler than the previous algorithm: the new relaxation relies on the use of nothing more than a multinomial EM algorithm, does not require the tuning of a learning rate, and has some favorable comparisons to IBM Model 2 in terms of F-Measure. The ideas presented could be applied to a wide range of NLP and machine learning problems.