AI for The Enterprise
Tuesday, October 26, 2021
PRO TALK (AI): How Artificial Intelligence Will Redefine the Leadership in the Software Management IndusJoin on Hopin
I do believe that today’s business leaders will need to radically change the way they lead and manage teams within their organizations as AI affects all business sectors. Successful leadership will not be driven by the same fundamental ‘human’ traits and characteristics we see in influential, successful leaders today anymore. Leaders in the Artificial Intelligence age need to be more open and willing to learn and seek input and knowledge from everyone within the hierarchy of the organization, regardless of their role. Effective and wise leaders in the age of Artificial Intelligence already recognize that some of the most valuable contributions and ideas for AI implementation may come from employees with much less experience than themselves. Leaders need to also create and foster a strong culture of innovation within their teams and be ready to respond to any technological opportunities and threats as they appear. Being able to effectively communicate the opinions of the team members to relevant stakeholders and being flexible enough to quickly adapt, as required, in this new ‘as yet unwritten’ era of commerce, should be perceived as key strengths that will certainly improve their commercial decision making. In this session, I will engage the audience in new ideas about the human traits and characteristics of successful leadership in the era of AI and Machine learning.
Wednesday, October 27, 2021
FEATURED TALK: (AI): Responsible AI into Practice - Deliver Trust in Artificial Intelligence SolutionJoin on Hopin
AI has been a key driver in innovation in every industry Organizations have ramped up their effort on leveraging AI to gain a competitive advantage. However, AI solution comes with its own challenges and risk, particularly in regulated industries. There have been numerous instances when AI introduced bias. Organizations must use a balanced approach to accelerating the adoption of AI and prioritize AI governance to ensure trust in the AI system. While AI regulation landscape is still evolving, now is the time for organizations to start taking steps to understand and mitigate AI risks. Responsible AI framework provides guidelines around AI governance for building fair, transparent, ethical, and accountable AI solutions. In this session you will learn about how organizations can follow Responsible AI guidelines and operationalize trust in AI solutions by incorporating AI governance throughout the AI/ML life cycle.
A hands-on deep dive on using Apache Pulsar Apache NiFi with Apache MXNet, OpenVino, TensorFlow Lite, and other Deep Learning Libraries on the actual edge devices including Raspberry Pi with Movidius 2, Google Coral TPU and NVidia Jetson Nano. We run deep learning models on the edge devices and send images, capture real-time GPS and sensor data. With our low coding IoT applications providing easy edge routing, transformation, data acquisition and alerting before we decide what data to stream real-time to our data space. These edge applications classify images and sensor readings real-time at the edge and then send Deep Learning results to Apache NiFi for transformation, parsing, enrichment, querying, filtering and merging data to various data stores. https://www.datainmotion.dev/2019/08/updating-machine-learning-models-at.html
Application of AI within image Processing for defect detection and classification which brings significant reduction in time and resources for the manufacturer - use case in semiconductor mask manufacturing
Through an innovative project, reducing CO2 emissions and all other air pollution induced by the mobility in cities by 30% by deploying a solution for a real-time automatic emission-based road traffic micro-regulation, we managed to use the best of AI technologies. Indeed, AI is the key enabler addressing the complexity of real-time analysis of mobility in crossroads and local air pollution with the trend predictions that leads to recommendation to how to regulate road traffic to decrease air pollution and apply these recommendations directly to traffic lights. Using embedded AI at local camera level was instrumental to allow detecting the different road users (vehicle, public transportation, pedestrian, cyclist…) in real-time, while respecting privacy and GDPR, in order to apply mobility strategies for the optimal mobility management with minimum pollution impact. This last part is combining two AI engines with 5 models. This project, [AI]Roads, is an European awarded project and the outcome is tested in some major cities in EU. Beyond technical challenge, we will share some key advantages of combining AI and embedded AI, which might become the mainstream for some applications, and how we offered a scalable solution to a complex problem: the automatic and best trade-off between air pollution and mobility.
As AI continues to transform our world, people are becoming more accustomed to conversing with voice assistants and chatbots to accomplish an increasing number of tasks - naturally, that includes search. Think about how fast people speak versus how they type. 41% of adults, and 55% of teens, use voice search daily.
Because intelligent assistants are able to decipher natural language, people are using voice search far more conversationally than typed search. Today’s natural language processing technologies are enabling rapid and continuous improvement to the speed and accuracy that intelligent assistants process user queries and deliver results, making voice search a better user experience..
This advancement will reshape customer service and support as well as information search over the next five years.
In this talk, Alex Farr will explain how easy it is for any business user to build and deploy an intelligent chatbot including highlighting several use cases where it has improved customer experience and accessibility, including:
Starting with ML tutorials seems easy. But how do you scale your ML models from detecting cats and dogs to a full scale business ML model?
The quest for a practical AI solution for automated ECG diagnostic is motivated by desire to reduce the human and financial resources required for patient monitoring and to enable more ubiquitous remote outpatient monitoring. Today, Deep Neural Networks (DNN) are considered the building blocks of all AI solutions. Yet, DNNs are not widely adopted in hospitals for automatic diagnostic for the following reasons. First, doctors do not have the time nor the desire to be “mechanical Turks” who label row-by-row millions of ECG patient records for model training. Second, doctors do not trust black box diagnostic predictions as humans need reasons to support deliberate actions. Third, “the right-to-know” regulation included in GDPR requires organizations to provide to stakeholders explanations for any automatic decision making. We overcomes these challenges with an innovative, patent-pending variant of the Neural Networks. The LNN by Trendalyze in cooperation with LaTrobe University (Australia) and St. Ekaterina University Hospital (Bulgaria). It achieved the best performance of 100% within-patient accuracy in recognizing atrial fibrillation in 12-lead ECG recordings and showed robustness with respect to the wide variations of ECG patterns among different patients.
AI teams invest a lot of rigor in defining new project guidelines. But the same is not true for killing existing projects. In the absence of clear guidelines, teams let infeasible projects drag on for months. By streamlining the process to fail fast on infeasible projects, teams can significantly increase their overall success with AI initiatives. This talk covers how to fail fast on AI projects. AI projects have a lot more unknowns compared to traditional software projects: availability of right datasets, model training to meet required accuracy threshold, fairness and robustness of recommendations in production, and many more.In order to fail fast, we manage AI initiatives as a conversion funnel analogous to marketing and sales funnels. Projects start at the top of the five-stage funnel and can drop off at any stage, either to be temporarily put on ice or permanently suspended and added to the AI graveyard. Each stage of the AI funnel defines a clear set of unknowns to be validated with a list of time-bound success criteria. In the talk, we cover details of the 5-stage funnel and experiences building a fail-fast culture where the AI graveyard is celebrated!
The Agile Metrics are important to track the health of your projects. They help in tracking the project progress. There are other advanced metrics equally important, like Customer Satisfaction, Employee Satisfaction, and Innovation? Tracking these statistics many times is not easy and straightforward.Did you ever think of applying AI (Artificial Intelligence) to measure these and come up with actionable evidence? The AI-powered with NLP (Natural language Processing) and statistical models not just help in getting a good project insight, it can also help in course corrections, and increase the rate of project success. It can help companies to understand their core strengths, weaknesses, and how to position themselves in the market.Rohit will talk and demonstrate how you can digitally transform your Agile Program Management with AI and NLP. How it enables organizations to take proactive measures that not only make projects successful but also help companies stay competitive and thrive in the market.
One of the main issues with ML and DL deployment is finding the right way to train and operationalize the model within the company. Serverless approach for deep learning provides simple, scalable, affordable yet reliable architecture. The challenge of this approach is to keep in mind certain limitations in CPU, GPU and RAM, and organize training and inference of your model. My presentation will show how to utilize services like Amazon SageMaker, AWS Batch, AWS Fargate, AWS Lambda, AWS Step Functions and SageMaker Pipelines to organize deep learning workflows. My talk will be beneficial for machine learning engineers and platform engineers.
Thursday, October 28, 2021
OPEN TALK (AI): Making the World Smaller with NLP: Using AI to Link Data and Make it Easier for Machines (and Humans) to UnderstandJoin on Hopin
Linked Data and the Semantic Web have come a long way in helping to achieve a world that is more understandable to computers, but unstructured data can still be especially challenging when trying to extract concepts and metadata into standardized concepts. In this presentation, you will learn about the background of Linked Data (JSON-LD in particular) and how natural language processing can be used to help take advantage of this increasingly important effort. From more easily enhancing the SEO of a website, to making your application more interoperable, natural language processing can make your projects better understood by humans and machines alike.