One potential purpose of explanations is to build the trust between the user and the AI application. This can be achieved by providing the user with understanding of the scope of automated decision-making, and the reasons that led to a particular decision. Further, during this talk, a framework will be presented to suggest two recommended approaches for developing the explain-ability with highlighting some of the possible challenges.
She has developed various intelligent system projects in education and health.
Further, she is a member of the steering group of Women Leading in AI WLiAI organisation working as Researcher in data ethics focusing on the legal and ethical aspects of AI and automated decision system. Apache Spark itself. Previously he was a Big Data Delivery Lead at Optum UnitedHealth Group , where he and his teams used to deal with projects in the PI fraud, waste and abuse, claims processing and the healthcare space. He shares his tech thoughts via his blog and DZone where he is Golden Member. Natural Language Processing offers a variety of techniques to get insight from and generate text data.
Going beyond simple representations and taking advantage of Deep Learning and RNNs, the models can use document context to perform more accurately. With the help of libraries like TensorFlow, building neural networks and applying NLP is now available to the wider audience.
In this talk, Barbara will make the introduction to NLP concepts and deep learning architectures. The audience will be walked through two labs: sentiment analysis and text generation. After this session, the audience will have a good understanding of the deep learning concepts when it comes to NLP. The attendees will create a classifying model that takes advantage of the document context using TensorFlow library and scale their solutions using Google Cloud.
Barbara is a Machine Learning Engineer with strong software development background. While working with a variety of different companies, she gained experience in building diverse software systems. This experience brought her focus to the Data Science and Big Data field.
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She believes in the importance of the data and metrics when growing a successful business. Alongside collaborating around data architectures, Barbara still enjoys programming activities. Currently speaking at conferences in-between working in London.
She tweets at BasiaFusinska and you can follow her blog. Machine learning uses algorithms to train software through specific examples and progressive improvements based on expected outcome. However, traditional data structures can fail to detect behavior without the contextual information because they lack the strongest predictors of behavior - relationships. Just as humans require contextual information to make better decisions, so do machine-learning algorithms. Combining ML processing with a graph data structure can help fill in the missing contextual information and improve our predictions.
In this session, Mark will show what graph has to offer and show an example applying link prediction analysis to estimate how likely academic authors are to collaborate with new co-authors in the future. You will see how to fine-tune the elements you measure and understand the results for decisions or further adjustments.
Learn how to exploit the power of connected data to improve prediction analysis! Mark helps users embrace graphs and Neo4j, building sophisticated solutions to challenging data problems. Mark is a co-author of the book 'Graph Algorithms: Practical Examples in Apache Spark and Neo4j', due to be released in early , and writes about his experiences of being a graphista on a popular blog at markhneedham. He tweets at markhneedham.
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You will learn what are meta reinforcement learning algorithms, how they achieve State-of-the-Art performance in multiple machine learning tasks and their potential applications. His research focuses on applying human-centered learning science theories on Deep Reinforcement Learning algorithms. Ali is also running a charity named Renaissance which operates number of charity schools for the disadvantaged communities in Pakistan.
His schools focus on quality education where technology is thoroughly integrated in classrooms and day-to-day operations.
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He has also launched a number of AI-based technology products in Pakistan, disrupting the Agriculture sector through Qurbani App and Education sector through Naseem Education. This talk is neither about big data, nor about AI. It's about artisanal, handcrafted data that poses a real challenge for anyone trying to analyze it: healthcare data. In theory, applying AI to healthcare sounds like the perfect match - look at real world data generated by patients, apply AI, learn from trends, and improve healthcare outcomes based on those learnings.
Systems like IBM Watson make us believe that the problem is already solved, but in reality, real world healthcare data and its applications suffer from problems not encountered in other domains, which poses huge challenges for any kind of analytical applications. In this talk, Samantha will look at the landscape of messy and patchy healthcare data, understand the difficulties of drawing reasonable conclusions from the data, and discuss the challenges of changing user behavior in healthcare. After spending several years in Academia, Samantha became an early employee at Flatiron Health, a New York City healthcare technology startup.
Within only five years, she watched the organization grow from two dozen people to almost employees and go through an acquisition by a large, multinational company. Nowadays companies are convinced that Data Science will help them to be more competitive in the market. Laura will explain the main challenges companies are facing in the Data Science adoption and how the use of automated ML and democratisation is helping them to success.
She is really committed to grow the STEM community and tech learning experiences for women and girls. Nowadays, she is in the UK helping other companies with their Data Science strategy, at the same time that she is contributing to community with workshops, talks, panels, etc. The CAP theorem is widely known for distributed systems, but it's not the only tradeoff you should be aware of. Fast: Results are real-time or near real-time instead of batch oriented Accurate: Answers are exact and don't have a margin of error.
Big: You require horizontal scaling and need to distribute your data. While Fast and Big are relatively easy to understand, Accurate is a bit harder to picture. Or how to trade some speed or the distribution for more accuracy. When a new idea is a big one, it causes massive effects on the way we build systems and the kinds of problems we can solve. Relational databases helped liberate information retrieval from the mainframe. The web made every computer user on earth a potential client of a single system.
Machine Learning has exposed us to broad vistas of new recognition problems that we have begun to solve in impressive ways. When big ideas meet and reinforce one another constructively, we see even larger effects. Event streaming has already caused a quiet revolution in the traditional ETL space, and is helping microservices architectures deliver on their yet-elusive promises of decoupling and evolvability. When event streaming and machine learning meet, the well-known problems of production ML systems find the same kind of solutions. Machine learning and event streaming can deliver all kinds of value to the world separately, but the ML community stands to grow by adopting real-time retraining using platforms like Apache Kafka.
Tim is a teacher, author, and technology leader with Confluent, where he serves as the Senior Director of Developer Experience. He can frequently be found at speaking at conferences in the United States and all over the world. He tweets as tlberglund , blogs very occasionally at timberglund. CodeNode is the UK's largest venue dedicated to Technology events and was designed to provide a space for Skills Matter's community of software professionals to come together and enjoy meetups, conferences, training and networking events.
With fantastic transport links and located in the heart of London's Tech City, Codenode welcomes thousands of engineers each year, who come together to learn and share skills, exoerience and collaborate on projects. CodeNode features six dedicated event spaces, a large break-out area, complete with fully-licensed bar, reliable wifi, plenty of power sockets, and 6 dedicated event and collaboration spaces.
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The Call for Papers is now closed! The Infiniteconf Diversity Scholarship Plan is now closed, thank you to all those who applied, and please feel free to apply again next year. Thank you for coming to Infiniteconf We hope you had as much fun attending as we did organising it.