We, of course, will never know everything. But with the arrival of Big Data, machine learning, data interoperability, and all-to-all connections, our machines are changing the long-settled basics of what we know, how we know, and what we do with what we know. Our old—ancient—strategy was to find ways to narrow knowledge down to what our 3-pound brains could manage. Now it’s cheaper to include it all than to try to filter it on the way in. But in connecting all those tiny datapoints, we are finding that the world is far more complex, delicately balanced, and unruly than we’d imagined. This is leading us to switch our fundamental strategies from preparing to unanticipating, from explaining to optimizing, from looking for causality to increasing interoperability. The risks are legion, as we have all been told over and over. But the change is epochal, and the opportunities are transformative.

Only a small fraction of global firms increase productivity year after year, according to the Organization of Economic Cooperation and Development (OECD). Creating and using unique stocks of data capital is one of the key tactics these firms use to widen their lead. Come learn how two new ideas—data trade and data liquidity—can help all companies, not just superstars, take advantage of the data revolution, and hear examples of firms already putting these ideas into practice.

Artificial intelligence (AI) is finally coming into its own as cognitive computing becomes the norm.

While the industry is abuzz talking about the rise of artificial intelligence, the term itself is not new. In fact, the term “AI” was first coined in 1956 but fell off the radar after no monumental achievements were accomplished in the following years. But given recent advancements in analytics, visualization, and machine learning, artificial intelligence has re-emerged with a promising future. However, the question remains—will it succeed this time around?

How can you unlock the power of data science for data-driven decision support? From a business standpoint, Bulusu details how to use machine learning techniques for predictive analytics. He also illustrates how AI can meet BI (business intelligence) through industry use cases and talks about integrating results with a BI platform, using Oracle Advanced Analytics.

Search is not a new activity for most of us, but cognitive search adds new dimensions and functionalities that enhance the UX.

Since 1994, when the first search engine was deployed, search has worked like this: A user entered search terms in a field, hit the search button, got a list of documents called search results, reviewed the list to find a few that might be relevant, downloaded one document, manually scanned it to see if it’s on point, and then returned to the list and continued the process until frustrated or out of time. Users are overwhelmed, tired, and wish they had a personal search assistant. They want to dispense with search altogether. They demand to know why the machine can’t just read all those documents for us and tell us what it finds. Well, now it can! Machine learning can change the entire search paradigm.

As one of the technologies getting loads of attention recently, machine learning has interesting applications for many types of enterprises.

Reuters News Tracer is a capability that applies AI in journalism to find events breaking on Twitter. It assigns them a newsworthiness score so people can focus on the events that are important. The real magic of Reuters News Tracer is that it gives a confidence score about how likely it is that those events are true. This is really critical, given the rapidly changing landscape of news, including “fake news,” and the distrust of media reports.

Access to information and insights is critical for today’s complex enterprises, unlike consumer experiences with Google, Alexa, or Siri, enterprise search is broken. Advancements in ML, NLP, and text analytics are changing the dynamic for enterprise employees. This session explores how one large, global bank is delivering a research portal for its analysts that understands not just what the user asks, but what is meant; uses intent to determine which ML relevancy model to apply; and creates a graphical, interactive results view that delivers actionable insight. The session concludes with an overview of the results the bank has experienced, and how it’s looking to extend the ML-based search capabilities into other areas.

Another technology gaining traction in our cognitive computing world is deep learning, which changes many business processes.

The hottest topic in computer science today is machine learning and deep neural networks. Many problems deemed "impossible" only 5 years ago have now been solved by deep learning—playing GO, recognizing what is in an image, and translating languages are but a few examples. Software engineers are eager to adopt these new technologies as soon as they come out of research labs and the goal of this session is to equip you to do so. This session focuses on two demos: using TensorFlow for linear regression (making a numerical prediction from inputs) and using a neural network to make predictions from text inputs. Along the way, I'll show some live demos and give you tips to apply these techniques in your own projects. No PhD required.

While tech giants have attempted to allay public concerns about “inhuman” negative or conflicting behaviors on the part of AI applications by proposing pledges to be good, many stakeholders, including governments, corporations, researchers, nonprofits, auto companies, and consumers, are asking for true accountability for the actions of emerging intelligent systems. This panel of experts discusses the growing body of work by the academic, scientific, and standards communities geared specifically to expand and tighten our understanding of the ethical context implicit (and explicit) in AI applications, from diagnosing disease to driving autonomous vehicles.

As the market moves from fascination with the wonders of the current bloom of Big Data technologies to reconsideration of their risks and perils, enterprises and IT operations are left to figure out how these developments might impact their business—and when. Hadley Reynolds, co-founder of the Cognitive Computing Consortium, presents an open reference framework jointly developed with Babson College’s technology management program. The framework gives executives and operating managers a tool to characterize the impact and behaviors of potential AI applications. Beyond impacts and behaviors, the framework integrates profiles of skills and resources required to effectively execute cognitive tasks.

Tuning search relevancy is a leading candidate to be replaced by machine learning.

Learning to Rank (LTR) is now avialable for both Solr and Elasticsearch. Why is this such a hot topic? What does an organization need to leverage a Learning to Rank solution? Haubert explains the LTR pipeline in terms of what is available as an off-the-shelf solution and what isn't. She discusses the challenges faced when implementing LTR and some open research areas moving forward.

We still have lots to learn about the practical applications of machine learning, social media, and data science within our organizations.

Machine learning can be applied for sentiment analysis of unstructured data in the context of social media. For example, a large telecommunications organization leverages modern data science approaches to ease the daily business of communication experts and to give them features not available before. Learn how to use key capabilities of advanced text analytics such as language detection, genre identification, named entity extraction, key influencer or key opinion leader about a trend or brand lovers. Gain new ideas about predicting and determining potential social media crises before they happen.

Cognitive computing platforms involve artificial intelligence, graph technology, and a host of other potential considerations.

Today’s knowledge workers want the findability and discoverability of Amazon’s recommendation engines and the intuitive usability of Google’s search rankings and knowledge graphs. These features have been organizational desires for years, but technology has finally caught up with the requirements backlog. Ivanov and Midkiff explain how ontologies serve as a framework for enterprise knowledge graphs, show how semantic layers complement traditional information models, and demonstrate how semantic knowledge models can be used as a basis for text mining. They use real-world production examples, including the Department of Veterans Affairs, the National Park Service, and the Harvard Business School.

Security is a pervasive concern across a wide range of companies and industries. What do cognitive technologies bring to the table to ensure data is secure?

Businesses and data security leaders are constantly looking for ways to better anticipate and even predict threats before they happen. Two major challenges: Companies have a huge amount of data to process and very little time to do it. New forms of targeted attacks have evolved. These new threats require new thinking, and that’s where the latest cognitive capabilities can help.

For the last 2 days, we've heard about exciting developments with technologies focused on the business uses for data, including machine learning, cloud computing, Hadoop, and, of course, cognitive computing. What we do with these technologies to advance business opportunities and avoid business risks is now up to each individual attendee. What are the takeaways that impact us?