Enterprise AI

Chairpersons welcome and opening remarks. 

As investment in AI continues to grow rapidly, many businesses continue to face the challenge of effectively leveraging AI and maximizing business impact. Although AI has shown incredible power when solving certain problems and driving tremendous value, building enterprise AI excellence is a journey that requires vision, strategy plan, and execution. With the right framework and clear focus on key aspects to drive success including organization, data & analytics capability, technology, and value delivery, the power of AI can be fully unleashed and drive exponential impact across enterprises.

Labeling training data is the bottleneck most NLP teams face today. The key to unlocking transformative (e.g., 10x to 100x) speedup for NLP application development is to change the interface to labeling altogether—and move from manual labeling to programmatic labeling.

Learn how to harness the power of programmatic labeling and weak supervision techniques for automating parts of the labeling process, and leveraging a data-centric approach to AI development. In this talk you will also learn about real-world NLP use cases from Fortune 500 companies that have made the transition from manual to programmatic labeling.

• Human-in-the-loop review processes – how can we decide where the buck stops in decision making?  
• Reconfiguring data input to tackle non-ethical AI bias. 
• The potential of labour replacement and corporate practice that can be done to mitigate the risk of job-loss. 

• 5% model, 95% everything else: Three truly comprehensive pillars to evaluate AI ideas for enterprise “viability”
• Tips on building AI teams that get things done
• Agile AI? Waterfall agile AI? Examples of bringing agile principles to AI and machine learning

The role of the data team is changing. Instead of focusing purely on data infrastructure; data leaders are now responsible for the core strategy across their organization and are expected to deliver increasing value from data. After a decade of working with organizations from across the globe, we’ve developed best-in-class practices to increase your data sophistication and drive more value from your data. Join us to discover:

• What does good data for AI look like? AI powered by the right data is highly effective but what tools can you use to confidently collect the best quality data from your platforms?
• How creating your own first-party, proprietary behavioral data on how your customers use your digital products and services drives a significant competitive advantage
• Aspirational case studies from your peers, including the common pitfalls we’ve seen across the industry, and our most exciting success stories.

Session focusing on utilizing Cloud and AI in the energy sector. 

Internal intelligence is one of your companies’ biggest competitive assets. A tremendous amount of information lives in PowerPoints, PDFs, videos, Word Documents and more. This data is the most difficult to extract from, yet the most valuable, as time, money and insight was spent converting data into knowledge. It’s a constant challenge to find the information when needed, especially when information is spread across repositories and geographies. Recent advances in AI-knowledge management platforms are now providing teams the capability to instantly access answers across all sources of internal knowledge and delivering businesses huge advantages from freeing up thousands of hours per year spent on searching for existing information to saving millions of dollars in redundant work.

Data driven decisions are essential for organizations to thrive in the digital age. With vast amounts of data being collected and processed, businesses are in the position to leverage artificial intelligence and machine learning to enhance business decisions. In this talk, UPS, a global transportation and logistics leader, will discuss case studies in how AI has successfully been implemented to drive business value. 

Word Error Rate (WER) is the primary quality metric for all speech providers. Engineers use it to make almost every research decision and customers use it to evaluate vendors. But it has many flaws… 

Who cares about an extra um, a missing hyphen, or an alternative spelling. Or how about a difference in written numerical formatting (1,000 vs 1000 or eleven vs 11)? These all increase WER but do not affect meaning in the slightest. 

How about omitting the word ‘suspected’ before murderer? Or predicting a wrong word that changes the sentiment? An ideal metric would take these minor and serious errors into account and score a system appropriately. 

If you’re interested to find out how we at Speechmatics are thinking of solving this problem, then come along to the talk!

Headspace is a values-first company: through 10 years of innovating the use of technology in meditation and mindfulness, the focus has never shifted from making sure that our approach to technological implementation is as mindful as our content.

Over the last year, much of that innovation has centered around ML-powered personalization: from in-app content recommendation to push notifications to AI-driven dynamic welcome flows. But implementing ML/AI solutions brings unique challenges: personalization is inherently intimate and demands great sensitivity in the ways we use and communicate member data.

In this talk, we’ll explore how Headspace balances the intimate and data-centric process of building ML solutions while maintaining our brand identity as a mindful actor.

Responsible AI is quickly becoming a requirement for brands everywhere—but it doesn’t have to be hard. Faraday’s CEO will walk you through a handful of straightforward steps you can take to develop a Responsible AI practice, covering privacy, fairness, and more. This session, like Faraday itself, does NOT require a data science background.

To follow soon …

Pre-recorded session 

• Human-centred AI for the optimum patient experience. 
• AI enhancing the human expertise, not replacing it.  
• Private and sensitive data – how it is handled and processed is more important than ever.  
• Adopting AI quickly into existing processes, think automating routine tasks. 

View day 2 content here:

Applied Data & Analytics

Data Optimisation | Big Data | Intelligent Decision Making | Data Storage | Industry Use-cases 

Transformational AI

Personalisation | Edge AI | Digital Transformation | Future Progressions | Industry Use-case