AI in the Enterprise

Utilizing AI technologies to advance business goals remains one of the most daunting challenges for many business leaders. Beth Partridge, assisted by Nick Paquin and Annie O'Connor, shares a breakthrough approach that bridges the gap between data science and business. Join in to gain a clear understanding of what AI can do for your business and how to go about implementing it. Read more.

Technology has opened up access to more information than ever before, but it’s still on humans to turn that data into knowledge. To solve this problem, organizations are turning to AI and natural language processing to augment human analysts. Sean Gourley explores how the world’s largest organizations use AI to summarize thousands of documents and scale human analysts. Read more.

Ashok Srivastava explains how to make your organization AI ready, determine the right AI applications for your business and products, and accelerate your AI efforts with speed and scale. Read more.

New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby shares challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past six years. Read more.

After a slow start, the finance industry is quickly catching up with others in its adoption of AI. Joe Rothermich explains how Thomson Reuters Labs is using AI to perform research in building quantitative investment models and discusses the company's research in deep learning for credit risk, machine learning and NLP for unlocking alternative datasets, and financial graph-based analytics. Read more.

Here at the dawn of the cognitive era, we must focus our design talents upon a new type relationship with machines—machines that can hold a conversation, interpret nonverbal cues, and draw from vast stores of human knowledge. But what should these relationships look like? Adam Cutler explains why designing for AI requires new considerations and new rules. Read more.

Now more than ever, success in AI requires expertise in multiple disciplines, including big data, efficient software, and novel models and algorithms. Forrest Iandola shares an approach for developing a full stack AI team that can use all of these diverse skills to execute on industrial-scale AI problems. Read more.

Given the revolution in data and healthcare, Shelley Zhuang predicts how certain sectors may unfold and shares opportunities for innovation. Along the way, Shelley discusses innovations that advance precision medicine by bringing together interdisciplinary fields across biology, engineering, data science, and clinical care. Read more.

Susan Etlinger explores how AI fundamentally changes the relationship between people and businesses, lays out its risks and opportunities, and demonstrates emerging best practices for designing customer-centric and ethical products and services. Read more.

Deep learning (DL) has transformed much of AI and demonstrated how machine learning can make a difference in the real world. With DL, the massive expansion of available training data and compute gave neural networks a new instantiation that significantly increased their power. Evolutionary computation (EC) is on the verge of a similar breakthrough. Risto Miikkulainen explains why. Read more.

Executives are often asked to "innovate with AI," but barriers to successful adoption for most enterprises are organizational, not technical. Mariya Yao explains why effective AI requires not only technical talent but extended interdisciplinary coordination between teams, investments in retraining your workforces at all levels, and cultivation of an experimental, data-driven culture. Read more.

Much attention in enterprise AI today is focused on automation. Jake Saper explains why the more interesting applications focus on worker augmentation and offers an overview of coaching networks, which gather data from a distributed network of workers and identify the best techniques for getting things done. Read more.

Leveraging structured knowledge will be a critical ingredient in the design of the next wave of intelligent applications. Mike Tung offers an overview of the current open source and commercial knowledge graphs and explains how consumer and business applications are already taking advantage of these to provide intelligent experiences and enhanced business efficiency. Read more.

Video games have used sophisticated AI techniques for decades to drive everything from area design to navigation to enemies to conversation and planning. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent offer an overview of the history of AI in video games and explain how the needs that drove AI advancement in the game development world map to almost-identical problems in the real world. Read more.

AI-powered chatbots are increasingly becoming viable solutions for customer service use cases. Technology leaders must consider adopting a multichannel chatbot strategy to avoid siloed chatbot solutions. Sharad Gupta shares a framework to ensure long-term strategic investment in chatbots. Read more.

For AI to serve each individual customer, we need much more complex natural language understanding, ideas, and behaviors. Will compositional deep learning put us on a new curve? Read more.

Machine learning is eating software. As decisions are automated, model interpretability must become an integral part of the ML pipeline rather than an afterthought. In the real world, the demand for being able to explain a model is rapidly gaining on model accuracy. Mayukh Bhaowal discusses the steps Salesforce Einstein is taking to make machine learning more transparent and less of a black box. Read more.

Estimating the performance of heavy earth-moving equipment on large construction projects is a complex task that can be riddled with uncertainty. Ramzi Roy Labban details how CCC uses machine learning, leveraging large datasets of actual performance of trucks on construction sites, to more accurately predict future performance and allow the company to make realistic performance assumptions. Read more.