Accelerate AI | Keynote

Are you confused about what it takes to be a data scientist? Curious about how companies recruit, train and manage analytics resources? You are not alone. Many employers, educators, and managers are struggling with these issues. In fact, tremendous resources are being wasted by employers on interviewing candidates who claim knowledge of Data Science that are not even qualified for such positions. This presentation covers insight from the most comprehensive research effort to-date on the data analytics profession, proposes a framework for standardization of roles in the industry and methods for assessing skills.

We have been running an industry initiative named: Initiative for Analytics and Data Science Standards (IADSS) to support the development of standards regarding analytics role definitions, required skills and career advancement paths. The initiative kicked off a research study including a detailed survey for analytics executives and professionals, in-depth interviews with industry leaders and academicians as well as an extensive literature review. We will present our initial findings from the research and provide case studies of how bad this confusion and why it is important for the field, for practitioners and for employers and educators to have clarity on this front…more details

Accelerate AI | Keynote | Cross Industry | Beginner-Intermediate-Advanced

Enterprise leaders across every industry are seeking better ways to understand their business. They need new ways to model the data that flows through their systems – the life blood of their information architecture. Currently, the models are taken and used by human decision makers, or rigid algorithms are developed to make use of the models in an attempt to optimize one or more outcomes. But, what how can we get AI to drive the decision making? How do we AI-enable a fast paced business operating in an ever changing environment? How do we create smart, responsive businesses able to predict the organization’s need in real-time? We try to answer these questions, and provide real world examples of how to apply AI to businesses to create end-to-end intelligence across a variety of industries…more details

Accelerate AI | Keynote

From creating new revenue streams to preventing catastrophic mechanical failures, organizations are betting their future on predictive models. But can you really trust them?

Human interpretable models are an important topic in machine learning, and in this session RapidMiner founder Dr. Ingo Mierswa will describe their current state and the danger of the “hidden black boxes” that live inside models…more details

Accelerate AI | Keynote

To reimagine business in the age of AI, we need ethically aligned design to ensure we don’t become creatures of our own device(s).  Join John C. Havens, Executive Director of The IEEE Global A/IS Ethics Initiative as he explores the question: How will machines know what we value if we don’t know ourselves? …more details

Business Talk | Healthcare | Intermediate-Advanced

One of the most difficult industries for data science to take hold and gain effectiveness is the world of commercial pharma/biotech. Due to regulation of FDA, lack of identifiable patient data, and one of the last industries that uses a “traveling salesperson” approach, data science is still taking hold in this industry. This talk will talk in depth about steps that companies in this space can take to make the most out of their data science teams and out of their data in general. These steps will include standardizing internal data, utilizing 3rd party data in unique methodologies, bearing the course during marketing and sales initiatives, and creating validation methods.

We will dive into these issues through the context of how to bring the industry from one of “old school” sales and marketing techniques into one where machine learning can make tangible top and bottom line impacts. Through this lens we will identify areas of opportunity that should first be tackled by any organization and those areas which are often pitfalls (even though they may seem lucrative). Additionally, an ideal team make-up and time line will be outlined so that these companies can level-set where they are and where they can improve their data science processes…more details

Business Talk | Cross Industry | Beginner – Intermediate

Much excitement has been generated over the potential benefits that can be obtained from increasing use of artificial intelligence. This talk will describe points, by reviewing recent cases in a few industries, that should be considered before and while employing an AI system, in order to increase, its utility, as well as to mitigate potential risks. An understanding of the full performance capabilities and risks of an AI system is needed to obtain substantial value from the system. The necessity of use of enough outcome metrics will be demonstrated.
Trade-offs and design decisions associated with different types of performance metrics will be discussed. The import of human training needed to successfully operate an AI system is also illustrated. The quantity, quality, and scope of data used to train and test an AI system, as well as that of the data on which the system is employed, has a substantial effect on the success of the system. Examples will be presented to demonstrate how a comprehensive understanding of these data properties is needed for successful usage of an AI system…more details

Business Talk | Healthcare | Intermediate

Blockchain has the potential to solve many common, chronic challenges including: multiple sources-of-truth, disjointed processes, expensive conflict-resolutions, lack of transparency and little party collaboration. Blockchain has shown promise to be utilized in the areas of track-traces, longitudinal data sharing, and digital asset management. Artificial Intelligence (AI) has been utilized to solve other challenging problems by processing complex and heavy computations, gleaning insights into customers business interests in real-time, and automating many complex manual business processes. The optimal solution to the aforementioned problems is not an “either or” decision between Blockchain and AI, but rather an implementation of both of these emerging technologies in conjunction with one another.

Interestingly, AI and Blockchain sit on opposite sides of the emerging technology spectrum with AI promoting centralized intelligence on closed data platforms. While Blockchain promotes utilizing decentralized applications in a transparent and auditable environment. If we were able to find a responsible way to make them work together, the potential positive effects could be amplified.

In this talk, Dr. Ayubi will explain –based on multiple real project experience– why and when this convergence makes sense, how to do it, what challenges may emerge, and its use cases in the healthcare and life sciences area…more details

Business Talk | Cross Industry | Beginner-Intermediate

  Keyence is a name that isn’t exactly household. For more than 45 years it have operated as one of the most important companies, that no one has ever heard of. As a company, Keyence manufactures sensors, which are the devices used by everything to collect data . Be it a barcode reader to a pressure sensor, a ultrasound to a beam of light. These are the devices that generate the massive amounts of data that are driving our economy. But for years Keyence has confound the investment world on how it has seen such staggering growth, has release hit product after product, and manage to reach a market cap per employee to rival the hottest startups.
The secret to this companies success? Every single decision, from the largest to the small is driven by data analytics. Information and data guided business practices mold and shape every detail from who is hired, to what products are launched, all the way down to the smallest detail.
But using that much data can be time consuming, or at minimum requires an army of data scientists and analysts to churn through the data and help make decisions. Or so people thought.
Join Keyence as they unlock the secrets that have led them to being the 6th largest firm on the Tokyo stock exchange, be ranked on Forbes “Most innovative Companies” list every year since inception, and maintain a market cap per employee that would rival the hottest startups. It’s a journey of data, and business decisions. Meeting in the middle to make real change happen…more details

Business Talk | Cross Industry | Beginner

As Machine Learning becomes a core component of any forward-looking company, how can we engage the entire workforce to help with ML and automation initiatives? This talk will cover how at Square we have adopted a “machine learning mindset” by 1. providing training to all employees (both technical and non-technical folks) on what ML is and how it works, including current applications and ethical considerations, 2. conducting structured brainstorming sessions to elicit automation opportunities, where everyone can contribute what ML could mean for their team or their customers, and 3. implementing a subset of those ideas by partnering with infrastructure, operations, and product teams, resulting in improved risk management, more efficient internal operations, and novel customer-facing product features…more details

Business Talk | Finance | Intermediate

In order to understand customer behaviors and provide better services, wealth management firms have started to invest vastly in data analytics. During this session, Meina Zhou will discuss how she has successfully helped different financial institutions implement in-house predictive analytics solutions to improve wealth management services. She will also address multiple use cases that built at different stages of the customer journey, including customer acquisition, customer personalization, and customer retention. She will discuss both the analytics components and the challenges involved throughout the implementation process….more details

Business Talk | Cross Industry | Beginner-Intermediate-Advanced

The advent of the “citizen data scientist” has been both concerning and irritating to many so-called “real” data scientists at work in the business world today. Some argue that the prospect of new, less well-trained AI-builders has been made established data scientists threatened, insecure, and paranoid. Others argue that using untrained modelers to build AI is a recipe for disaster. In this talk, Dr. Greg Michaelson will discuss the truth and the hype of the citizen data scientist and will propose a framework for utilizing both skilled data scientists and citizen data scientists in the same organization…more details

Business Talk | Cross Industry | Beginner-Intermediate-Advanced

While humanoids and computer wizardry attract attention, how are normal businesses currently using artificial intelligence? Leading organizations are deepening their commitments to AI and are eager to scale AI. But many companies have discovered, often to their surprise, that it is easy to apply AI and get quick results. What is not so easy is building a system of AI applications along with associated data pipelines that interact and are reliable. I will share the results of research that combines a global survey with 3,076 respondents and in-depth interviews with 36 business executives. The research tells a story of measurable benefits from current AI initiatives, increased investments, and determined efforts to expand AI across the enterprise…more details

Business Talk | Healthcare | Intermediate

The widespread popularization of machine learning and its potential capabilities has increased the risk for exaggerated expectations and insufficiently critical thinking concerning the scaled implementation of AI in healthcare. Drawing on a well described framework for technology assessment this talk will step audience members through several examples of healthcare implementations of AI in order to highlight critical regulatory, data, policy, and human-factor issues required for optimal introduction of these technologies in clinical practice…more details

Business Talk | Cross Industry | Intermediate

Many of the 1.5M nonprofits in the US face increasing pressure to achieve their missions due to fundraising inefficiencies, lack of access to talent pools, and budget constraints. This limits the social good that can be cultivated to improve our world.

Through this lens, we’ll explore how artificial intelligence has played a crucial role in expanding the capabilities of nonprofit organizations, allowing them to build more and deeper relationships with supporters without having to hire more people (or fire anyone). As practitioners of vertical AI products, we’ll show how we’ve utilized open source technologies including TensorFlow, NLTK, CRFsuite, SKlearn, and others to determine who is most likely to give a donation to an organization and learn from communications to mimic the cognitive functions of fundraisers to expand their reach.

The discussion will center around case studies involving the College of Charleston, where AI was used to increase their workforce throughput by 160%, and University of Delaware, where AI uncovered a $50M donor. Similar examples will be pulled from cancer research, human rights, and Alzheimer’s funding.

The talk is intended for beginner-to-intermediate attendees. The main takeaways will be an exploration of AI in SaaS products, how behavioral psychology affects AI adoption, and insight into the nonprofit industry….more details

Business Talk | Healthcare | Intermediate-Advanced

Artificial intelligence is revolutionizing many industries from manufacturing to automated driving, and the healthcare industry, though relatively recent to penetrate, is no exception. But diagnostics and therapeutics of child behavioral conditions like autism, ADHD, and language disorders remain relatively behind. This is unfortunate because early detection of such disorders is proven to improve the prospects of affected children dramatically. It also happens to be a healthcare context where there is a significant unmet need for streamlining and scalability of services.

We discuss the potential of artificial intelligence to disrupt that domain and talk about specific artificial intelligence techniques, challenges, and outcomes of experiments that have been applied successfully in practice.

We present the challenges, solutions, insights, and validation results of our user-facing solution that screens for autism and ADHD by combining multiple predictors based on various media inputs while allowing for inconclusive determination on hard-to-screen subjects. We will talk about incorporating signal from parental questionnaires, expert analysis of short video uploads, as well as short doctor questionnaires. We will get into leveraging automatable, complementary signals like audio-video streams from interactive storytelling and gaming sessions on tablets and mobile phones. Finally, we will explore the potential of deep learning to help with this problem-setting.

This talk is intended to raise awareness of the potential benefit of applying AI to the field of healthcare in general and child behavioral diagnostics in particular, and share with the audience a concrete example of a context where proper application of artificial intelligence has yielded demonstrably superior results to the traditional standard of practice…more details

Business Talk | Cross industry | Beginner-Intermediate-Advanced

Many events and conferences have gender and general diversity issues. These can be a product of a long history of predominately male speakers – the Known Quantity Factor– or, of an industry where there is little diversity at the top (where speakers often come from) or in the ranks.

An #allmalepanel can get you mocked, ridiculed and harassed on social media. There’s a “Congrats you have an all-male panel” Tumblr. Gender Avenger (@GenderAvenger) looks for events light on female speakers and creates social media-ready images with your event hashtag. The Gender Avenger community can bury your event’s social media stream in a flash.

Speaking at conferences is an important path to career and business success. Speaking at conferences gets you access to prospective customers, partners, employees and investors. Meanwhile, more diverse conferences are more interesting and better attended.

So, how to do you create a pipeline for future success and a diverse conference? You likely already have a number of diversity speakers inside your own community. We help educate your existing community of women and diverse candidates on what it takes to be a speaker – how to source speaking opportunities, write abstracts, pitch and then rock the gig itself. We also help them understand the benefits of public speaking through this hands-on workshop.

We talk to professional women about:
• The Speaker’s Paradise (why there are so many speaking opportunities today)
• The 5 “C’s” – the benefits of public speaking
• Rebranding their fear – getting over the nervousness of public speaking
• How to handle the “no” – getting turned down to speak…more details

Business Talk | Cross Industry | Intermediate-Advanced

Machine learning and “AI” systems often fail in production in unexpected ways. This talk shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries.

The covered topics include concept drift (identifying and correcting for model decay due to changes in the distribution of data in production), common pitfalls in A/B testing (like the primacy and novelty effects), offline versus online measurements, and systems that learn in production (such as adversarial learning use cases). This talk is intended for executives, technical leaders and product managers who want to learn from others’ mistakes how to best set up their teams & products for success…more details

Business Talk | Cross Industry | Begginer-Intermediate

Data scientists spend 30% of their time building shoddy infrastructure. Our data shows that many AI teams can accelerate their progress by 10x at least. Deep Learning brings with it enormous amounts of data, complicated experiment results and intense compute requirements. Decades of experience in moving code to production yielded best practices in engineering that have not yet found their place in deep learning teams. Breaking silos to foster trust, a transparent culture, and shared responsibility – we introduce DeepOps – deep learning ops. A set of methodologies, tools and culture where data engineers and scientists collaborate to build a faster and more reliable deep learning pipeline.
Surveying hundreds of AI companies, we’ve learned that adopting DeepOps practices helped them ship faster, with more confidence and improved customer experiences.
In this talk, Yuval Greenfield, Deep Learning Developer Relations at MissingLink.ai, will discuss:
DeepOps checklist – insights from leading AI teams and how to bring them to your team.
Increase productivity within data science teams
Reduce time to market
Increase deployment speed
Increase visibility and transparency across teams…more details

Business Talk | Cross Industry | Intermediate

Artificial intelligence is the buzzword that is everywhere in the media in 2018. Although this subject is the focus of attention, it still raises many questions about how this impacts businesses and how can they better leverage AI. More specifically, Olivier Blais, experienced data scientist will demonstrate how to develop an agile data science community as he has developed practice communities in various companies from different industries. He will also prove why Artificial Intelligence is not as complex as everybody thinks and why you should start deploying artificial intelligence capabilities now.

During this presentation, Olivier will:
* Introduce key concepts such as artificial intelligence, machine learning and deep learning
* Present great use cases, in which machine learning can give companies an edge in enhanced productivity, cost reduction or revenue generation
* Demonstrate strategies to enable your company to become more data driven
* Detail useful techniques on how you can make it easy to deploy artificial intelligence in your company…more details

Business Talk | Cross industry | Beginner-Intermediate-Advanced

Things (IoT) will mean that the amount of devices that connect to the Internet will rise massively. This is already giving rise to the creation of massive amounts of data. Spatial and temporal mobility patterns of things and societies as a whole can be characterized based on the interactions that we are able to capture from the IoT sensors.
In this presentation, we will review what we can learn from human mobility patterns, how they can be used to optimize traffic, city planning and tourist attractions. We will review the challenges associated with privacy and security regulation when analyzing mobility patterns. As an application we will present an study on AIS data that describes the locations of vessels traveling in Norwegian seas. We will close the presentation with an overview of the kind of AI techniques we can apply to analyze mobility patterns…more details

Business Talk | Cross Industry | Intermediate-Advanced

So you’re doing some machine learning. But have you really thought about what needs to happen once you put it into production? This is the challenge lurking behind every promising machine learning initiative: making it work in the real world.
Robbie Allen, author of the book Machine Learning in Practice and the CEO of two data-centered companies, breaks the challenge down into key production issues like:
On-Prem Deployment. If you’re not in the cloud, deployment on your own servers or hardware can be difficult.
Workflow Integration. When companies work with legacy and/or closed systems, connecting prediction API’s into products/workflows is hard.
Who Owns Deployment and Maintenance? Is DevOps responsible for maintenance and deployment of ML models into production? Is this a job for data scientists? Engineers? When data scientists build models, will they see the light of day?
Monitoring. Today’s tools lack the ability to measure model drift and identify when a model’s production data is no longer representative of training data.
Still Learning? Machine learning is not “learning” unless there is a continuous feedback loop of data from production; many ML solutions do not have an established method for doing this.
Changing It Up. Measuring and adjusting machine learning models is challenging enough; these tasks are even harder when dealing with changing models, parameters, data labels, and goals…more details

Business Workshop | Finance | Advanced

In today’s financial services industry, competitors are rushing to enable the AI-driven enterprise by making strategic investments in AI and machine learning technology, financial institutions not investing in AI and machine learning technology risk losing their competitive edge. However, due to an increased reliance on AI and machine learning models with everyday business processes and for strategic decisions, model risk must not be ignored and must be effectively managed. If left unchecked, the consequences of model risk can be severe; where model risk is defined as the risk of financial or reputation loss due to errors in the development, implementation or use of models.
Therefore, AI and machine learning models require constant monitoring and effective validation. This is not only a regulatory requirement, but it is also sound business practice. In this session, Seph will present the cornerstones of effective modern model risk management in the age of AI and machine learning by first providing an overview of AI and machine learning in the financial serves industry, summarizing the regulatory background and the machine learning model lifecycle, and then finally presenting the challenges and emerging best practice for the validation of models, in an ever-changing world of AI and machine learning…more details

Business Talk | Cross Industry | Beginner-Intermediate-Advanced

Data science managers (and senior leaders managing data science teams) need to think through many questions relating to how to best execute their data science efforts. For example, what is the most effective way to lead a data science project? How to make sure my data science team does not expose my organization to issues relating to the misuse of data and/or algorithms? How do I validate the results provided by the data science team?
This workshop will provide a framework managers can use to help ensure a successful data science project. The focus of this framework is not on which specific algorithm a team should use, but rather, how to ensure that the data science effort is progressing effectively and efficiently.
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Business Talk | Healthcare | Beginner-Intermediate

The latest AI advances have the potential to massively improve our health and well being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare. For example, we will explore how AI can diagnose major life-threatening conditions even before those conditions emerge. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI understanding of patient’s medical history and current conditions. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone…more details

Business Talk | Cross Industry | Intermediate

Each day, AI and data science become less of a competitive advantage for businesses and more of a requirement for survival. Yet, many companies struggle to effectively leverage their data science and AI investments. New Vantage Partners reports that although 92% of businesses are increasing investment in AI, 77% continue to face challenges with adoption. What makes some companies reap the benefits of AI while others struggle to see return on investment? The difference is strategy.

Whether your teams are distributed or centralized, many or few, sophisticated or just starting, your company needs a unified data strategy. But, data science and AI bring new challenges to strategic planning. The nature of their output demands that data science and AI projects undergo a more risk-aware scoping process. To scale smoothly and evolve quickly, a data strategy needs to provide broad and specific standards. Finally, proposed projects need to be contextualized within the larger data ecosystem in order to amplify their impact.

This talk is designed for business leaders, data science managers, and decision makers that want to ensure the effectiveness of the AI and data science capabilities they are building. Attendees will leave equipped with the tools to:
Critically evaluate pitched projects and select the most strategic ones;
Build an effective, impactful, and high yield data science project portfolio;
Evolve your data science roadmap to quickly adapt to new opportunities…more details

Business Talk | Cross Industry | Advanced

The opportunities are endless in the global economy. However, monetizing data analytics in the global space is like a free fall, hoping to have a parachute when landing. To succeed, you need a solid data science strategy that can be deployed across multiple geographies, each with unique business risk factors.

LexisNexis® Risk Solutions is well-established in helping companies mitigate financial risk and has been quite successful across global markets. Prabhu Sadasivam, leader of Analytic Technology, discusses how LexisNexis established its analytic solutions with a global presence, how it has overcome unique geographical challenges, and lessons learned that continue to inform and improve their data science efforts.

The discussion topics and examples will include standardized analytic framework, data dogmatism, scaling solutions, managing market size, developing solutions with no defined target or data, attribute and model monitoring, cloud variability, agility, and data governance across geographies…more details

Business Talk | Finance | Healthcare | Intermediate

Deep Learning is an incredibly powerful technique, which has found uses in wide range of applications such as image object detection, speech translation, natural language processing and time series modeling. However, training deep neural network models requires a tremendous amount of time, training data and compute resources. A technique called transfer learning allows data scientists to increase their productivity dramatically by sharing neural network architectures and model weights. Reuse of a pre-trained model on a different but related task enables training of deep neural networks with comparatively less data. In this talk, you will learn the details of how transfer learning works and will see demonstrations in both financial and healthcare domains. We will talk about specific use cases and lessons learned that are applicable to many other industry sectors…more details

Business Talk | Cross Industry | Intermediate

Based on her experience of building analytics teams from the ground up, Hillary will walk through the process of creating an analytics team.
We’ll begin by examining why analytics teams exist and how they are different from Data Science teams. Next, we’ll discuss possible structures for analytics team, including embedded, independent, and hybrid structures.
We’ll talk about best practices in hiring a diverse and talented analytics team, including good interview questions, and interview tools, such as CoderPad to ensure that applicants have the necessary skill set.
Once the team is up and running, it needs to integrate with Product teams. Creating best practices around data creation and experimental design, can make sure that your team is involved early, before problems can surface.
Success can bring challenges, such as too many under-defined requests. Creating a ticketing system unique to your team can ensure that ad hoc requests can be handled in a systematic and efficient manner. This is key to scaling an analytics team.
There are many approaches to becoming the voice of data at a company. Building a data reporting ecosystem ensure that all internal clients have access to what they need when they need it. The talk will cover dashboarding, alert systems, and data newsletters. Finally, we’ll discuss promoting responsible data conception through continuous training in statistics and tooling for all members of an organization…more details

Business Talk | Cross Industry | Intermediate

If we look back at the major developments in data over the last decade, we often think about advances in data storage, in AI, and in machine learning. While these are significant, over the last several years, a series of quiet revolutions have been driving equally big changes. A new class of data tools are closing the technology gap between the world’s leading companies and everyone else, making data science far more accessible to a much wider range of companies. This is opening up new opportunities for companies to serve and sell to an emerging “middle class” of data consumers.

As these technologies spread, we’ve collectively become remarkably skilled at processing, modeling, analyzing, and learning from data. And yet, despite being we’re massively more informed than ever before, we haven’t seen comparable improvements in outcomes.

Why? Because we’re rarely in the room where these decisions happen. We often hand off our work, and tell executives “you take it from here.” Our tools and analyses, no matter how powerful or clever, will always be restricted by how they’re wielded – and who wields them.

In this talk, I’ll outline the three technological changes that are driving this quiet transformation of data science. I’ll show how any company, regardless of stage or resources, can and should take advantage of these changes. I’ll discuss the problem technology hasn’t yet solved – how to put data science in the rooms where decisions are made. Finally, I’ll survey the opportunities we have to address this challenge…more details

Business Talk | Finance | Intermediate

Astute investors have shifted their attention to explore the information content in unstructured data sets to differentiate their source of alpha. In this presentation, we will explore a number of sentiment- and behavioral-based signals using the content from earnings call transcripts via NLP that have historically demonstrated stock selection power in the U.S. market…more details

Business Talk | Cross Industry | Begginer-Intermediate-Advanced

As data scientists, we invest much of our time on the business problem, the data, the statistics, the algorithm and the model. But we can’t afford to overlook one very important component: the customer! A great AI/ML model with a poorly designed user experience is ultimately is going to fail. The world’s best data products are born from a perfect blend of data science and an amazing user experience.

Design thinking is a methodology for creative problem solving developed at the Stanford University d.school. The methodology is used by world class design firms like IDEO and many of the world’s leading brands like Apple, Google, Samsung and GE.

Michael Radwin, VP of Data Science at Intuit, will offer a recipe for how to apply design thinking to the development of AI/ML products. Your team will learn how to get deep customer empathy & fall in love with the customer’s problem (not the team’s solution). Next, you will learn to go go broad to go narrow, focusing on what matters most to customers. Finally, learn how to get customers involved in the development process by running rapid experiments and quick prototypes.

These lessons of blending data science & design thinking can be applied to products that leverage supervised and unsupervised machine learning models, as well as “old-school” AI expert systems.

Case study examples will include:
Mint users lose $250 million in overdraft fees every year. Using the data from Mint’s 10 million users, we applied a machine learning algorithm that predicts if you are likely, within three days, to have an overdraft. Mint alerts you in time, with helpful suggestions on how to avoid the exorbitant Non-Sufficient-Funds fee.
Business or personal? Mobile mileage tracking for QuickBooks Self-Employed: ML model + UX = automatic categorization of individual trips easy to accurately rack up potential tax deductions.
Americans spend 7 billion hours every year filing taxes. TurboTax’s Tax Knowledge Engine, which uses advanced AI to translate the 80,000+ pages of US tax requirements and instructions into a software oracle that can explain computations to DIY tax filers so that they have greater confidence in the calculations in their returns…more details

Business Talk | Cross Industry | Intermediate-Advanced

The proliferation of sensor technologies has resulted in more connected machines than ever before. This change is resulting in huge quantities of sensor data becoming available for analysis. Machine learning algorithms have resulted in a mixed track record of success with these data sources. This talk will give an overview of the state of machine learning as applied to IoT and industrial equipment. It will discuss some of the challenges with current approaches, exciting theoretical advancements and some “lessons learned” from the field.

Specifically:
What do we mean by IoT?
What is failure prediction and prognostics?
What is the value of IoT?
Differences between physics based approaches to IoT and data-driven approaches to IoT
What are the challenges from applying data-driven approaches to IoT?
How can recent advances in machine learning help with the unique challenges of IoT?
Real-case study that illustrates the application of deep-learning, gradient boosting, transfer learning and other machine learning techniques for IoT applications
What are the opportunities for future enhancements and exciting research in this area?

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Business Talk | Cross Industry | Beginner-Intermediate

Globally, demand for AI skills continues to outpace supply resulting in an arms race to hire and retain scarce talent. In the last 18 months alone, the number of open positions on LinkedIn increased by 2x for AI roles and 10x for ML Engineer roles. In response, companies are adapting by looking internally to build proprietary talent pipelines through skills development and retraining programs. This presentation will share insight, best practices, and lessons learned from company leaders who have built consensus for and ultimately launched AI skills development programs at growing startups as well as large Fortune 500 organizations…more details

Business Talk | Cross Industry | Beginner-Intermediate-Advanced

The amounts of data in digital investigations are ever increasing and new approaches are needed for finding the relevant items amongst the noise. For too long, the focus on digital investigation software has been on parsing and extracting any possible piece of data and displaying it to the user. But, with the increasing amount of data, the focus needs to be on showing only the most relevant items.

Machine learning techniques can help identify which items the user should see first and therefore save them time. This talk will outline how these techniques can be used to rank documents, executables, and other files found during a digital investigation…more details

Business Talk | Cross Industry | Beginner-Intermediate-Advanced

Have you ever been asked to create a model or derive insights out of data that is inaccurate, missing, or unreliable? As data experts, we know that no matter how good our model is, the results will be unreliable if the source data is unreliable. As the saying goes– Junk in, junk out.

This talk will explore the approaches that Wayfair’s Business Intelligence Team has used to improve the quality of source data, and in turn increase the accuracy of reporting, models, and insights.

Yard Arrival Date – Wayfair has increased data capture from <10% to > 99% for Yard Arrival Date. Yard Arrival date is the date that an SPO (Stock Purchase Order) arrives at our warehouse yard. Knowing the exact date that an order arrived in the yard is essential to creating predictive models that will allow us to predict when future orders will arrive. We found that the capturing Yard Arrival required staff to perform extra work that they did not see the value in. There also was no accountability – no one was checking if the staff was entering the data, and it didn’t impact their work, so there was no incentive to do this seemingly needless work. By explaining the usefulness of this data, working with the warehouse staff to improve the data capture process, and creating a public dashboard that drove accountability, we drove Yard Arrival Date entry compliance from less than 10% to over 99%.

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Business Talk | Cross Industry | Beginner-Intermediate-Advanced

How does customer experience/digital marketing know what customers are saying to our human chat, bot chat, survey, or social? Why are they not satisfied or not moving to the next action? The first step is to deeply analyze customer conversations. A new generation of AI technology makes this possible, extracting the ideas contained in text to summarize, organize, and display for analysis…more details