Free | AI & Big Data Technologies

An analyst will give an overview of the current status of industry markets & where innovations are disrupting the landscape  

AI is becoming more prevalent for applications like smart medical devices, robotics, and financial trading, where predictions are made in near real-time. However, there are many complexities associated with managing high-frequency data, the AI modelling approach, and integrating the models in a streaming architecture.

This talk will focus on building a system to address these challenges through:

• Using a stream processing framework that incorporates time-windowing and manages out-of-order data with Apache Kafka
• Synchronizing data and managing mathematical assumptions with signal processing techniques
• Incorporating time requirements when choosing and implementing AI models
• Updating and caching the models in the system

During the session, we will walk through the process of performing signal processing and time-alignment and designing and deploying a machine learning algorithm for streaming data.

A Presentation which highlights potential avenues for growth capital and funding for fast growth tech companies. In this world of ever growing financial products this presentation takes a high level view of giving Founders and C-Suite individuals some insight on what may be available and the best option for them

This session will look at the work of the Academy of Robotics in addressing the challenge of understanding the many shades of grey in autonomous navigation on unmarked roads. William will explain how they have approached the challenge of seeing not just the markings that are actually present on the roads, but also the markings that should be there. How designing for a single-minded focus on last-mile delivery has in fact opened up a variety of new industrial use-cases including building predictive models for road surface analysis and hazard prevention.

The presentation provides an overview of artificial intelligence used at Wirecard. The audience will gain insight into traditional payment methods, customer analytics, and digital marketing use cases.

In the days where we have autonomous cars, drones, and automated medical diagnostics, we want to learn more about how to interpret the decisions made by the machine learning models. Having such information we are able to debug the models and retrain it in the most efficient way.
This talk is dedicated to managers, developers and data scientists that want to learn how to interpret the decisions made by machine learning models. We explain the difference between white and black box models, the taxonomy of explainable models and approaches to XAI. Knowing XAI methods is especially useful in any regulated company.

We go through the basic methods like the regression methods, decision trees, ensemble methods, and end with more complex methods based on neural networks. In each example, we use a different data set for each example. Finally, we show how to use model agnostic methods to interpret it and the complexity of the interpretability of many neural networks.

Artificial Intelligence is on the verge of transforming man-controlled spacecraft into cognitive machines that will soon drastically change the way we used to do space exploration. For decades, the European Space Agency has been actively researching and making use of Artificial Intelligence to allow us to go further and provide our spacecraft and robots with the capacity to adapt under unpredictable environments.

In this talk we will discuss the impact of some of the key Artificial Intelligence-based technologies used by the European Space Agency in Space Communications and will give an overview of the use of AI in Space Mission Operations, Earth Observation, Space Exploration and Space Science.

Beyond the hype, what can AI, Big Data, NLP, and Machine Learning actually deliver? From a data science perspective, the evidence shows it can generate orthogonal alpha across all major asset classes. In other words, Big Data works. In this presentation, Ashish Sharma, Managing Director at RavenPack, shows how clients are finding ways of using its Platform to enhance returns, reduce risk and increase operational efficiency

The National Innovation Centre for Data (NICD) addresses the market gap in data science skills. NICD provides practical expertise to organisations working with data, exploring business questions in creative and entrepreneurial ways. NICD has worked with the likes of the NHS BSA, Procter & Gamble and others to deliver an immediate return on investment through agile processes.

There are many advantages to working alongside NICD at Newcastle Helix – Join Steve in this session to find out more.

The field of AI Safety has provided technology for explaining AI predictions, as well as quantifying how much any given prediction should be trusted. In this talk, Ilya will introduce some of that technology, and show how it can be used to improve the way AI predictions are consumed to enhance their impact.

For Industry 4.0 to work, being able to connect to industrial machinery and collect data is a crucial enabler. Once that’s sorted, being able to transform all that Big Data into meaningful actionable insights could be the key differentiator for your business and the 3X, 5X, …, 10X multiplier that you might have been looking for. Industrial AI makes it possible.

During this presentation, we would be shedding light upon

• Key challenges around Industry 4.0
• How to drive customer value by using AI in an industrial setting
• Making AI work with Big Data
• How Smartia makes Industry 4.0 easy for you

Everyone talks about artificial intelligence revolutionizing the world as we know it. Yet all we seem to see are fairly basic bots: an Alexa that only understands half of what you say and computer vision applications that can’t tell basic things apart .

Why is this?

To answer this question, let’s look at what makes machine learning different from software. The latter is—in essence—a set of very clever rules, that exist independent of the inputs, written on top of assemblers and compilers. None of these features exist for ML: What matters is the training data, and the quality of how it is structured and labeled. The labelling of the data is the programming, and right now it is the single biggest bottleneck to making AI deliver on its promise.

Machine Learning and AI are beginning to show value across multiple industries for those organisations actively deploying them at scale. However, many are often trapped carrying out Proof of Concept projects, experimenting and developing models with teams struggling to implement solutions and reach production.

During this talk from The Data Analysis Bureau, we’ll explore the value of breaking the PoC cycle and how to retain the services in demand and deploy them through a development pipeline to reduce the costs of innovation. We’ll address how you move between R&D, get out of the PoC loop, the criteria, tools and timescales should you apply, and how you assess value to achieve rapid deployment. We’ll share our lessons and a case study from working with academia and industry to move machine learning and deep learning models from R&D into production.