Fierce AI Week

• Ideas to consider         
• Questions to ask         
• Architectural approaches         
• Buy-in from the board         
• Connecting the dots inside a company         
• Real-world applications

Telcos such as AT&T are using artificial intelligence (AI) for their customer service contact centers. And according to Gartner, by 2023 more than 60% of all customer service engagements will be delivered via digital self-service channels, up from 23% in 2019. The COVID-19 pandemic accelerated the uptake of AI. How can AI be used in customer service centers? Is it difficult to integrate AI technologies with existing customer relationship management systems? What are some of the technical challenges to create AI-based conversation systems for customer service?

Schedule:

11:00-11:20am Keynote Interview: Shankar Arumugavelu, Senior Vice President and Global CIO, Verizon
11:20-11:40am Keynote Interview: Yossi Marouani, Director of AI Applications, Vonage

A platform that can automatically take care of prior authorization and benefits verification faster and more accurately than a human. A robot that can serve as a fitness assistant. An avatar named Buddy that can keep an extra eye on patients with chronic conditions. Here’s a look at some of the emerging and unexpected ways AI is changing healthcare and predictions about where the field is heading.  

Epidemiologists have drawn some parallels between the 1918 flu pandemic and today’s COVID-19 crisis. But today’s disease-fighters have powerful AI tools scientists in the early 20th century could never have imagined. Researchers are using AI to design virus-blocking proteins. Others are using a suite of tools to simultaneously expose the virus’ vulnerabilities and discover drugs that could exploit them. In fact, AI engines have already screened billions of molecules in their hunt for prospective treatments and vaccines. We’ll explore the possibilities—and the progress so far—with experts who are taking AI to the coronavirus fight.

The AI capabilities of consumer electronics and appliances including vacuum cleaners, voice-enabled personal assistants and speakers, VR headsets/displays, and even washing machines, have increased dramatically, thanks in large part to advances in computer processing power.  But what does it really take from a design engineering perspective to successfully develop an application like AI-enabled object detection/image recognition so that a vacuum cleaner, for example, “goes to where the dirt is”?

• Go behind-the-scenes with the companies who have successfully applied AI to real-world consumer electronics and appliances today and hear their stories
• Learn about the key AI building blocks necessary for applications like image recognition and voice processing
• Gain insight into the potential pitfalls of an AI implementation and how to avoid them

The artificial intelligence of things (AIoT) represents one of the most exciting and important technology pairings in the world today. It is anticipated to explode into life in 2025 and with the current Covid-19 pandemic, we anticipate that this will accelerate further still. But with this revolution comes a range of challenges. No matter how far you want to take the concept of AIoT, its success depends on being able to drive one of the most impressive feats of electronics engineering the digital era has ever seen. AIoT devices demand a processor with high-performance compute, efficient energy usage and a low eBOM.

As AIoT devices proliferate, security and privacy become one of the most pressing challenges for applications soon to infiltrate our homes, workplaces and day-to-day lives.

In this session, XMOS explores the way in which the AIoT will change the face of life as we know it, and introduces their recently announced crossover processor for the AIoT – xcore.ai.

In the rapidly evolving fight against COVID-19, pharmaceutical and healthcare organizations, governments, and the broader scientific communities around the world are working to assess the impact of the virus, and quickly develop fast, accurate solutions.

To get there, researchers and teams must have access to the best, and most up to date, evidence from a broad range of data sources including scientific literature, clinical trials and other textual sources. 

Linguamatics natural language processing (NLP) technology enables fast, systematic, comprehensive insight generation from unstructured text. Sources can include scientific literature, clinical trial records, preprints, internal sources, medical records, social media and news reports. Capturing the key information from these many sources, and synthesizing it into one hub, deepens understanding for our users, which will in turn accelerate insights and outcomes. 

In this webinar, we will discuss the benefits of natural language processing for both COVID-19 insights, and touch on broader applications of this technology across pharma and healthcare.

When does artificial intelligence get really powerful? Sometimes it’s when some of the most influential companies partner with some of them most creative startups working in the world of AI. We bring together some of these partners to discuss their partnerships, including the challenges and advantages they’ve experienced along the way, and their ultimate ambitions for using artificial intelligence.

Decades marketing to physicians has sharpened pharma and agency skills when it comes to doctors, but what about connecting with patients? Effectively reaching and messaging patients—especially those accustomed to dealing with sophisticated consumer marketing companies like Nike and Apple—is easier said than done. That’s where artificial intelligence is making strides. Pharma marketers and their agencies are increasingly adopting AI tools such as chatbots, Alexa voice-activated health skills, natural language processing in call centers and predictive analytics for audience targeting. The result? Better health outcomes for people and a true patient-centered focus for pharma. We’ll discuss the different types of AI tools that pharma marketers and agencies are using with real world examples that highlight both the challenges and opportunities today—and in the future.

Cloud providers and telcos are all racing to establish thousands of new edge computing locations to bring applications closer to consumers. Edge computing, itself, will reduce latency. But many applications will also require artificial intelligence (AI) to deliver information in near real time – think about autonomous driving. What are some of the edge computing/AI applications that are in hot demand by enterprises? How will owners of edge computing sites deliver AI capabilities that will require complicated data lakes? How much compute, memory and storage can stay at the edge and how much has to remain in more central locations?

Schedule:

11:00-11:15am Keynote: Harnessing the Benefits of AI Through Edge - Jason Hoffman, Chairman and CEO, MobiledgeX
11:15-11:30am Partner Keynote: Gavin Horn, Senior Director in Wireless Research, Qualcomm
11:30-12:15pm Panel

Poised for prime time and now enabled by the Industrial IoT, AI and machine learning offer manufacturers and industrial equipment makers the potential to tap into a rich stream of information to do everything from improving the consistency and quality of visual inspections to gaining key insights into operations. To fully reap the benefits, though, companies need not only the right technology but also must develop core analytical capabilities and leverage domain knowledge.

• Gain insight into what it will take to accelerate adoption of AI in industrial and manufacturing applications and hear from experts on when it will likely become mainstream
• Hear first-hand from companies about their successful AI applications and lessons learned
• Learn about the steps to take to get started on your first AI use case and the building blocks you’ll need for use cases like visual inspection

Patient-focused drug development requires a strong integration of the patient’s voice at every step of the drug development process. Developing clinical endpoints that are meaningful to patients and that are qualified to measure a relevant change in quality of life is challenging. The patient’s perspective is traditionally investigated with scientific literature reviews, exchange with clinical experts and patient organizations, as well as interviews with patients. However, all of these methods suffer from shortcomings such as underrepresenting the direct view of the patients or representing the view of a limited number of them.

We addressed these shortcomings by using Social Media Listening (SML) as an abundant information source to complement insights from traditional methods. Patients share their perspective not just with their clinicians, but also on social media. They talk about their symptoms and the impacts these symptoms have on their daily lives. SML strengthens the patient-centricity of the drug development process. The level of burden to the patients is significantly lower than in concept-elicitation interviews and it can reach more patients.

Social media data are highly diverse and the analysis of these data comes with significant challenges. One example is the understanding of the patient’s language and its mapping to scientific language. Given the growing amount of data available in social media, robust computational approaches have to be developed to produce meaningful insights.

This webinar will highlight how we integrate the patient’s voice via social media listening studies into the early part of the drug development process. We will give insights into the tasks and tools required for social media listening and how artificial intelligence and natural language processing can help to translate the patients’ voice into actionable insights.

While investment across the biopharma industry in R&D has been consistent or increased over the past 15 years, productivity has remained largely unchanged. Even with the projection that knowledge of medicine is doubling every 18 months, there must be better ways to understand biology and pathobiology and better ways to treat disease and manage healthcare.

Convergence, the result of the sharing of methods and ideas across multiple fields and industries, is the integration of insights and approaches from historically distinct scientific and technological disciplines. At AbbVie, our Convergence initiative seeks to create integrated R&D data and analytics capabilities that power data-driven insights – leading to better, faster decisions and therapies for patients. We are building a foundation for change, and setting out to double R&D productivity.

While hype alarms go off any time someone mentions Artificial Intelligence or ‘AI’, there are boundless opportunities to redefine AI – to engage it as the power of Automation and Insights – and drive value and impact in R&D. Tune in to this keynote to learn how AbbVie data scientists are helping our researchers scale the ladder of inference, how they are helping build foundations for growth and new capabilities, and how they are freeing up our people to perform higher value tasks. Ultimately guided by human empathy and experience, this will drive an increased ability to make a remarkable impact in the lives of patients.

Insurance companies have jumped in with both feet when it comes to taking advantage of the data insights available with the help of AI. Here we bring some of top payer execs to talk about how machine learning is changing the way they do business.

Drugmakers have been working for years to streamline clinical trials, the costly, time-consuming tests required to bring all new drugs and vaccines to market. Enter AI, which can match patients to appropriate trials, manage workflow, analyze incoming data, and help keep participants on track with treatment. These tools can not only save time and money—and flag any problems early—but also increase the chances of success in the clinic. We’ll examine today’s AI tools, discuss how they’re used in the field and find out what’s coming next.

The whole idea behind software-defined networking, SD-WAN and network functions virtualization (NFV) was to bring more automation into telecommunications networks. Forward-thinking service providers such as AT&T and CenturyLink jumped on the network automation bandwagon early. Many other telcos have since followed suit. And now newcomers such as Rakuten have taken network automation to the next level. What’s the status of network automation at service providers and how are they using artificial intelligence (AI) in their networks? What have been some big lessons learned in terms of network automation and AI?

Schedule:

11:00-11:15am Keynote Interview: Rahul Atri, Head of Automation COE and Products at Rakuten
11:15-11:30am Partner Keynote: Rob Wilmoth, Chief Architect at Red Hat North America
11:30-12:15pm Panel

In order for autonomous cars to perform even seemingly basic tasks, they will depend on AI and machine learning to make sense of the massive amounts of data generated by multiple, redundant sensors. The sheer processing power required means the realization of self-driving cars is likely still years away. But there are many examples of successful AI deployments today in autonomous vehicles such as drones and self-guided robotic vehicles (SGVs) that operate in radically more predictable environments.

• Hear from companies who will discuss their real-world applications for AI in drones, robotic vehicle, and cars
• Learn about the key AI building blocks for applications in autonomous technologies
• Gain insight the technology advancements that are accelerating adoption of AI in autonomous technologies

The outcome of a clinical trial is the deciding factor for the future success of a biotechnology and pharmaceutical company. It is therefore important that every critical parameter for a clinical trial is taken into consideration to ensure the highest possible chance of a positive outcome. Using AI and an advanced analytics-enabled engine Innoplexus made it possible to calculate, understand, and predict the outcome of future and ongoing clinical trials using real-world, outside-trial, and biomedical data sets. Hence, enabling companies to choose the best set of parameters for their clinical study.