Traditional recruiting methods have typically had a poor track record at matching candidates with employers. San Francisco-based startup Scoutible is betting its AI-based gaming solution can do better. Most seasoned hiring managers know the sinking feeling that comes with realizing within months of onboarding that a new professional is ill-suited to the role. The pressing work that prompted the hire in the first place may stall, eliciting outcry from stakeholders and frustrating colleagues charged with picking up the slack. Meanwhile, the prospect of letting the employee go and starting the search anew creates even more headaches--not to mention added expense.

The fear of robots coming for your job is one of the many challenges confronting 21st-century workers, but the machines aren't ready to take on every industry just yet. Bridgewater Associates, the massive hedge fund founded by legendary investor Ray Dalio, just released a report on the changing relationship between labour and capital in the US. One of the big factors the Bridgewater authors highlighted was the ongoing rise in automation across industries, which they noted could be a support for corporate profits in the years to come as more efficient robots and software potentially replace slower and error-prone human labour. Bridgewater cited a 2016 report from consulting firm McKinsey & Company that looked at which industries in the US were most susceptible to being automated. The McKinsey report used data from the Department of Labour to estimate how much time workers in various industry sectors spent doing different types of tasks, and which of those tasks could, theoretically, be automated using present technology.

By enabling machines to perceive, learn from, abstract, and act on data, Artificial Intelligence (AI) researchers are building machines that can perform tasks humans do--ideally better than we do them. As a result, organizations like yours are implementing AI to accomplish their missions and better serve their clients while enabling employees to work on more complex problems.

The above explanation is of course simplified and AI and ML have many more cognitive advantages that deserve a more extensive explanation. One key aspect is that the aim of AI and ML is not to replace humans, but to augment their capabilities. As AI is able to tackle routine tasks and increasingly complex non-routine tasks, humans can concentrate their efforts on tasks that have the most added value – those that really need human judgement. For instance, staff deployed in operations do not need to go through every invoice and process it in the appropriate way for the supplier. Instead, they can focus on the more complex ones while the AI algorithm processes the great majority of the invoices – faster, cheaper and more accurately than humans.

Within Reinforcement Learning, there is a fledgling approach to conceptualizing the environment in terms of predictions. Central to this predictive approach is the assertion that it is possible to construct ontologies in terms of predictions about sensation, behaviour, and time---to categorize the world into entities which express all aspects of the world using only predictions. This construction of ontologies is integral to predictive approaches to machine knowledge where objects are described exclusively in terms of how they are perceived. In this paper, we ground the Pericean model of semiotics in terms of Reinforcement Learning Methods, describing Peirce's Three Categories in the notation of General Value Functions. Using the Peircean model of semiotics, we demonstrate that predictions alone are insufficient to construct an ontology; however, we identify predictions as being integral to the meaning-making process. Moreover, we discuss how predictive knowledge provides a particularly stable foundation for semiosis\textemdash the process of making meaning\textemdash and suggest a possible avenue of research to design algorithmic methods which construct semantics and meaning using predictions.

We all see the potential of artificial intelligence (AI). After all, this is brand new territory. It's easy to get caught up in the hype and to forget all the groundwork and tactical steps it takes to effectively establish and use AI in an organization. Having witnessed adoption by many clients, I've developed a short checklist of what's needed to be successful, and I plan to devote a blog to each one. These are big buckets holding lots of detail.

CFOs are planning to implement advanced technologies, including artificial intelligence, drones, robots and blockchain, at a rapid rate, according to a new survey by Grant Thornton. For the study, GT and CFO Research polled 378 senior finance executives about the ways technology is transforming nearly every division in their organization, especially the finance function. One out of four of the respondents said they use AI, compared to just 7 percent last year. Significant proportions of senior financial execs are currently implementing advanced analytics (38 percent) and machine learning (30 percent). Within two years, senior financial execs plan to roll out a battery of new technology, such as AI (41 percent), blockchain (40 percent), robotic process automation (41 percent) and drones and robots (30 percent), at their organization.

In his role as principal data scientist at consulting firm Booz Allen Hamilton Inc., Kirk Borne sees the world in terms of data connections. "Life is about who is connected to whom and what is connected to what," Borne said, and he pointed to graph databases and graph analytics applications as new ways to capitalize on such connections. That's because graph databases, a form of NoSQL software, document the connections between data points quite different compared to mainstream relational databases. Graph systems represent data not as elements in tables, but as nodes linked to one another by edges with a set of properties that delineate the relationship between nodes. Therefore, one of the advantages of graph databases is they allow data analysts to navigate through data sets without the need to create and run complex queries to join combinations of tables together, as in the relational model.

It doesn't have to be perfect, but it needs to have enough quality and consistency for useful patterns to emerge. However, many companies are overwhelmed by the volume, velocity and variety of their data and find themselves unable to access data's fourth V: value. So how should we think about data preparation strategies to avoid potential data paralysis or over-ambition with your AI projects? The better the data, the better the AI. But for many companies, there's a problem: 85 percent of their data is either dark (whereby its value is unknown), redundant, obsolete or trivial.

Large firms are finding that poor-quality customer and business data may be keeping them from leveraging digital tools to cut costs, boost revenues, and remain competitive, according to a survey by PricewaterhouseCoopers. Poor-quality customer and business data may be keeping companies from leveraging artificial intelligence (AI) and other digital tools to reduce costs, increase revenue, and stay competitive, according to a recent PriceWaterhouseCoopers (PwC) survey of 300 executives at U.S. companies in a range of industries with revenue of $500 million or more. While 76% of survey respondents said their firms want to extract value from the data they already have, just 15% said they currently have the right kind of data needed to achieve that goal. Most of the respondents said their firms see tremendous upside opportunity in fully optimizing the data they already have, but face multiple obstacles to achieving that goal including the quality limitations of the data. Companies working with older, unreliable data need to first assess that data by identifying its source, gauging its accuracy, and standardizing data formats and labels, according to PwC.