AI for the Enterprise
Wednesday, October 26, 2022
PRO TALK (AI): ML Drift Monitoring : What to Observe, How to Analyze & When to Act
Deploying a new ML model in production successfully is a great achievement, but also is the beginning of a persistent challenge to keep them performing at expected levels. Models in product will drift and decay, and the value provided by them to the business will drop. ML drift monitoring is a challenging tasks, from identifying the right data to collect, the right metrics to compute, the right trends to analyze and the right actions to take. This session will explore the process of model drift monitoring, from model instrumentation to determining the next-best-action. Real life challenges will be explored and best practices and recommendations will be discussed.
KEYNOTE (AI): LivePerson -- Building a Mental Model Around Conversational AI: Why We Need to Teach How to Interact with Bots
Use of conversational AI across retail, finance, healthcare, and other industries is on the rise. Whether they recognize it or not, today’s consumers are rapidly shifting their mindset — they are ready for, and even demand, a new type of interaction with brands centered around messaging: Indeed, new research shows that over 3 quarters (78%) of consumers want the ability to message with businesses and 83% would browse or buy products in messaging conversations.
Perhaps most importantly, consumers are suddenly, radically more open to automated conversations now than ever before: Positive sentiment towards chatbots nearly doubled in 2021 (61%) vs in 2020 (31%).
Despite new capabilities that make chatting with a conversational AI bot more like having a conversation with a human, there isn’t yet a prevailing mental model for what conversational AI is that will help people get the most out of their interactions with them. Simply put, people aren’t sure how to talk to bots. On the one hand, some people treat it like a search engine, typing in short commands; while others treat it like another human, telling long-winded stories and burying what question or issue it is they really are trying to address.
Similar to when search engines were first invented and people had to figure out how to effectively use them, many people may not know how to maximize the efficiency of a bot conversation. Tech companies can and must take the lead on that instruction to enable correct use of their products and to help users get the most benefit out of them.
During this session, Joe Bradley will offer guidance on how companies can help users find the middle ground of these two scenarios. How they can begin creating a playbook for cultivating best practices and interacting with conversational AI.
There are many questions around how companies should teach people to interact with conversational AI and how they can make this form of communication most successful that are just now being explored – How can we be sensitive to the fact that different people will respond to conversational AI in different ways? How can we help people learn and get the most out of this new type of interaction? Not only do these questions intersect with machine learning but they also involve psychology and sociology.
While few people have the time (or interest) in diving deep on how to best interact with conversational AI, bot builders can begin to offer clues and guidance on how to engage with conversational AI bots effectively. Having previously worked on data science and e-commerce projects at Amazon and Nike and advising brands like David’s Bridal and Virgin Atlantic at LivePerson on how to build their bot strategies, Joe Bradley will share his learnings on how to build a mental model around conversational AI that gets the most out of this increasingly used form of interaction.
PRO TALK (AI): Data Ecosystem a Stepping Stone for Decarbonization of Operation Industry
Climate change is possibly one of the most complex and challenging issues on earth. On the other hand, manufacturing companies often find themselves in the crosswind of it. Oil and gas, mining, chemical, cement, energy, and utility sectors are responsible for more than 50% of the industrial GHG emissions. The changes they are bringing into their operations are not enough to address the issue. New initiatives for carbon abetment are not showing any visible improvement in reducing GHG levels in the environment.
In this session, we will analyze how data ecosystems such as LiDAR, remote-sensing data, IT, and OT data pertinent to these manufacturing companies can help them to track/measure, trace and mitigate excess emission issues for their operations. We will also explore how advanced AI techniques such as deep learning, and reinforcement learning techniques can be used effectively to find an optimal solution for the above-mentioned problem/s with real-life examples.
Thursday, October 27, 2022
KEYNOTE (AI): Iterate.ai -- AI Will Fuel 2023’s Innovation Explosion – What Can You Do Now?
2023 is the inflection point when a matured $98 billion AI market defines a truly new age of innovation for enterprises across industries. The convergence of several maturing technologies all now steering toward 2023 ubiquity – including 5G, IoT, blockchain, and low-code software platforms – will enable AI technologies to fast-track innovation to a degree that enterprises haven’t yet seen and enable wholly new customer experiences. Enterprises proficient with AI going into 2023 will wield a decisive competitive advantage; what do they need to be doing now?
Enterprises have just a one-year head start to prepare for the explosion in innovation that demonstrably more matured AI, combined with several other advances, will unlock. This talk offers attendees a crucial opportunity to understand the coming AI-led transformation, why 2023 is pivotal, and how to take steps now that position their businesses at the leading edge of these uniquely profound market changes.
Attendees of this presentation will come away with a clear picture of how AI will transform enterprise innovation, the advantages available to those that prepare appropriately, and how to accelerate AI strategies within their organizations. IDC predicts that once AI hits scale, AI-powered businesses will respond to customers and competitors 50% faster than competitors. Powered by tiny powerful AI chips – 50 can now fit on the head of a penny – products and sensors with localized edge-processing capabilities will do their own thinking. Countless AI interactions will contribute data in real-time, enabling new product experiences, rapid iteration of software solutions using low-code drag-and-drop development, IoT-powered backend and supply chain efficiency, and blockchain-secured digital identities and privacy. Ultimately, enterprises that take steps to become AI-ready today will command greater customer satisfaction and success tomorrow.
OPEN TALK (AI): Operationalizing AI with a Shift from Research to Product Orientation
Many AI programs fail to deliver sustained value despite great research, due to insufficient operational tools, processes and practices. These days, more and more data science teams are going through a major shift, from research orientation, to product orientation. Key factors to successfully transition to a product-oriented approach to AI include empowering data scientists to take end to end accountability for model performance, and going beyond the model - gaining a granular understanding of the behavior of the entire AI-driven process. In this talk, Yotam will discuss the importance of empowering data science teams to successfully make the transition from research oriented to product oriented.
Wednesday, November 2, 2022
[#VIRTUAL] PRO TALK (AI): ML Drift Monitoring : What to Observe, How to Analyze & When to Act
Deploying a new ML model in production successfully is a great achievement, but also is the beginning of a persistent challenge to keep them performing at expected levels. Models in product will drift and decay, and the value provided by them to the business will drop. ML drift monitoring is a challenging tasks, from identifying the right data to collect, the right metrics to compute, the right trends to analyze and the right actions to take. This session will explore the process of model drift monitoring, from model instrumentation to determining the next-best-action. Real life challenges will be explored and best practices and recommendations will be discussed.
[#VIRTUAL] KEYNOTE (AI): LivePerson -- Building a Mental Model Around Conversational AI: Why We Need to Teach How to Interact with Bots
Use of conversational AI across retail, finance, healthcare, and other industries is on the rise. Whether they recognize it or not, today’s consumers are rapidly shifting their mindset — they are ready for, and even demand, a new type of interaction with brands centered around messaging: Indeed, new research shows that over 3 quarters (78%) of consumers want the ability to message with businesses and 83% would browse or buy products in messaging conversations.
Perhaps most importantly, consumers are suddenly, radically more open to automated conversations now than ever before: Positive sentiment towards chatbots nearly doubled in 2021 (61%) vs in 2020 (31%).
Despite new capabilities that make chatting with a conversational AI bot more like having a conversation with a human, there isn’t yet a prevailing mental model for what conversational AI is that will help people get the most out of their interactions with them. Simply put, people aren’t sure how to talk to bots. On the one hand, some people treat it like a search engine, typing in short commands; while others treat it like another human, telling long-winded stories and burying what question or issue it is they really are trying to address.
Similar to when search engines were first invented and people had to figure out how to effectively use them, many people may not know how to maximize the efficiency of a bot conversation. Tech companies can and must take the lead on that instruction to enable correct use of their products and to help users get the most benefit out of them.
During this session, Joe Bradley will offer guidance on how companies can help users find the middle ground of these two scenarios. How they can begin creating a playbook for cultivating best practices and interacting with conversational AI.
There are many questions around how companies should teach people to interact with conversational AI and how they can make this form of communication most successful that are just now being explored – How can we be sensitive to the fact that different people will respond to conversational AI in different ways? How can we help people learn and get the most out of this new type of interaction? Not only do these questions intersect with machine learning but they also involve psychology and sociology.
While few people have the time (or interest) in diving deep on how to best interact with conversational AI, bot builders can begin to offer clues and guidance on how to engage with conversational AI bots effectively. Having previously worked on data science and e-commerce projects at Amazon and Nike and advising brands like David’s Bridal and Virgin Atlantic at LivePerson on how to build their bot strategies, Joe Bradley will share his learnings on how to build a mental model around conversational AI that gets the most out of this increasingly used form of interaction.
[#VIRTUAL] PRO TALK (AI): Data Ecosystem a Stepping Stone for Decarbonization of Operation Industry
Climate change is possibly one of the most complex and challenging issues on earth. On the other hand, manufacturing companies often find themselves in the crosswind of it. Oil and gas, mining, chemical, cement, energy, and utility sectors are responsible for more than 50% of the industrial GHG emissions. The changes they are bringing into their operations are not enough to address the issue. New initiatives for carbon abetment are not showing any visible improvement in reducing GHG levels in the environment.
In this session, we will analyze how data ecosystems such as LiDAR, remote-sensing data, IT, and OT data pertinent to these manufacturing companies can help them to track/measure, trace and mitigate excess emission issues for their operations. We will also explore how advanced AI techniques such as deep learning, and reinforcement learning techniques can be used effectively to find an optimal solution for the above-mentioned problem/s with real-life examples.
[#VIRTUAL] OPEN TALK (AI): It’s an AI Product Manager’s Job to Help an Organization Succeed with Predictive Machine Learning
In short, AI is a lifecycle that requires the integration of data, machine learning models, and the software around it. It covers everything from scoping and designing to building and testing all the way through to deployment — and eventually requires frequent monitoring. Product managers need to ensure that data scientists are delivering results in efficient ways so business counterparts can understand, interpret, and use it to learn from. This includes everything from the definition of the problem, the coverage and quality of the data set and its analysis, to the presentation of results and the follow-up.
[#VIRTUAL] OPEN TALK (AI): Building Enterprise Grade Accessible Applications Using Microsoft Azure AI
Microsoft's CEO Satya Nadella has said: "Human Language is the new UI layer, bots are like new application". As more and more bots are getting popular in homes and enterprises, the demand for custom bots is increasing at rapid space.
Azure Communication Services is a cloud-based communications service that lets you add voice, video, chat, and telephony to your apps.
The Microsoft Bot framework is a comprehensive open-source offering that we can use to build and deploy high-quality bots.
Microsoft Cognitive Services let you build apps with powerful algorithms to see, hear, speak, understand and interpret our needs using natural methods of communication, with just a few lines of code. Easily add intelligent features – such as emotion and sentiment detection, vision and speech recognition, language understanding, knowledge, and search – into your app, across devices and platforms such as iOS, Android, and Windows, keep improving and are easy to set up.
In this demo-driven session, we will cover how to build the enterprise-grade intelligent bots in using Microsoft Bot Framework, Cognitive Services, and Azure Communication Services and deploy in Microsoft Teams and other platforms like SharePoint, Public-Facing Web Sites, etc
Thursday, November 3, 2022
[#VIRTUAL] KEYNOTE (AI): Iterate.ai - AI Will Fuel 2023’s Innovation Explosion – What Can You Do Now?
2023 is the inflection point when a matured $98 billion AI market defines a truly new age of innovation for enterprises across industries. The convergence of several maturing technologies all now steering toward 2023 ubiquity – including 5G, IoT, blockchain, and low-code software platforms – will enable AI technologies to fast-track innovation to a degree that enterprises haven’t yet seen and enable wholly new customer experiences. Enterprises proficient with AI going into 2023 will wield a decisive competitive advantage; what do they need to be doing now?
Enterprises have just a one-year head start to prepare for the explosion in innovation that demonstrably more matured AI, combined with several other advances, will unlock. This talk offers attendees a crucial opportunity to understand the coming AI-led transformation, why 2023 is pivotal, and how to take steps now that position their businesses at the leading edge of these uniquely profound market changes.
Attendees of this presentation will come away with a clear picture of how AI will transform enterprise innovation, the advantages available to those that prepare appropriately, and how to accelerate AI strategies within their organizations. IDC predicts that once AI hits scale, AI-powered businesses will respond to customers and competitors 50% faster than competitors. Powered by tiny powerful AI chips – 50 can now fit on the head of a penny – products and sensors with localized edge-processing capabilities will do their own thinking. Countless AI interactions will contribute data in real-time, enabling new product experiences, rapid iteration of software solutions using low-code drag-and-drop development, IoT-powered backend and supply chain efficiency, and blockchain-secured digital identities and privacy. Ultimately, enterprises that take steps to become AI-ready today will command greater customer satisfaction and success tomorrow.
[#VIRTUAL] PRO TALK (AI): Leveraging Automated Machine Learning to Enable Anyone to Develop Machine Learning Solutions
Nowadays, several business owners know that leveraging Artificial Intelligence capabilities, on their systems and applications, can enable their businesses to achieve better results. But building Artificial Intelligence solutions may be a time-consuming and complex process, so consequently, some of these people give up of building such solutions, since they or their team do not have the expertise and capacity required, or sometimes they end-up paying to third-party companies to build these solutions and as a consequence, they end-up doing a significant investment on building these solutions. Azure Automated Machile Learning is the solution to enable anyone to build the Artificial Intelligence and Machine Learning solutions at low cost and with the best quality possible.
[#VIRTUAL] OPEN TALK (AI): Operationalizing AI with a Shift from Research to Product Orientation
Many AI programs fail to deliver sustained value despite great research, due to insufficient operational tools, processes and practices. These days, more and more data science teams are going through a major shift, from research orientation, to product orientation. Key factors to successfully transition to a product-oriented approach to AI include empowering data scientists to take end to end accountability for model performance, and going beyond the model - gaining a granular understanding of the behavior of the entire AI-driven process. In this talk, Yotam will discuss the importance of empowering data science teams to successfully make the transition from research oriented to product oriented.