future of data and ai

In March 2023, we had the pleasure of hosting the first edition of the Future of Data and AI conference – an incredible tech extravaganza that drew over 10,000 attendees, featured 30+ industry experts as speakers, and offered 20 engaging panels and tutorials led by the talented team at Data Science Dojo. 

Our virtual conference spanned two days and provided an extensive range of high-level learning and training opportunities. Attendees had access to a diverse selection of activities such as panel discussions, AMA (Ask Me Anything) sessions, workshops, and tutorials. 

Future of Data and AI conference featured several of the most current and pertinent topics within the realm of AI & data science, such as generative AI, vector similarity, and semantic search, federated machine learning, storytelling with data, reproducible data science workflows, natural language processing, machine learning ops, as well as tutorials on Python, SQL, and Docker.

In case you were unable to attend the Future of Data and AI conference, we’ve compiled a list of all the tutorials and panel discussions for you to peruse and discover the innovative advancements presented at the Future of Data & AI conference. 

Panel Discussions

On Day 1 of the Future of Data and AI conference, the agenda centered around engaging in panel discussions. Experts from the field gathered to discuss and deliberate on various topics related to data and AI, sharing their insights with the attendees.

1. Data Storytelling in Action:

This panel will discuss the importance of data visualization in storytelling in different industries, different visualization tools, tips on improving one’s visualization skills, personal experiences, breakthroughs, pressures, and frustrations as well as successes and failures.

Explore, analyze, and visualize data with our Introduction to Power BI training & make data-driven decisions.  

2. Pediatric Moonshot:

This panel discussion will give an overview of the BevelCloud’s decentralized, in-the-building, edge cloud service, and its application to pediatric medicine.

3. Navigating the MLOps Landscape:

This panel is a must-watch for anyone looking to advance their understanding of MLOps and gain practical ideas for their projects. In this panel, we will discuss how MLOps can help overcome challenges in operationalizing machine learning models, such as version control, deployment, and monitoring. Additionally, how ML Ops is particularly helpful for large-scale systems like ad auctions, where high data volume and velocity can pose unique challenges.

4. AMA – Begin a Career in Data Science:

In this AMA session, we will cover the essentials of starting a career in data science. We will discuss the key skills, resources, and strategies needed to break into data science and give advice on how to stand out from the competition. We will also cover the most common mistakes made when starting out in data science and how to avoid them. Finally, we will discuss potential job opportunities, the best ways to apply for them, and what to expect during the interview process.

 Want to get started with your career in data science? Check out our award-winning Data Science Bootcamp that can navigate your way.

5. Vector Similarity Search:

With this panel discussion learn how you can incorporate vector search into your own applications to harness deep learning insights at scale. 

 6. Generative AI:

This discussion is an in-depth exploration of the topic of Generative AI, delving into the latest advancements and trends in the industry. The panelists explore the ways in which generative AI is being used to drive innovation and efficiency in these areas and discuss the potential implications of these technologies on the workforce and the economy.

Tutorials 

Day 2 of the Future of Data and AI conference focused on providing tutorials on several trending technology topics, along with our distinguished speakers sharing their valuable insights.

1. Building Enterprise-Grade Q&A Chatbots with Azure OpenAI:

In this tutorial, we explore the features of Azure OpenAI and demonstrate how to further improve the platform by fine-tuning some of its models. Take advantage of this opportunity to learn how to harness the power of deep learning for improved customer support at scale.

2. Introduction to Python for Data Science:

This lecture introduces the tools and libraries used in Python for data science and engineering. It covers basic concepts such as data processing, feature engineering, data visualization, modeling, and model evaluation. With this lecture, participants will better understand end-to-end data science and engineering with a real-world case study.

Want to dive deep into Python? Check out our Introduction to Python for Data Science training – a perfect way to get started.  

3. Reproducible Data Science Workflows Using Docker:

Watch this session to learn how Docker can help you achieve that and more! Learn the basics of Docker, including creating and running containers, working with images, automating image building using Dockerfile, and managing containers on your local machine and in production.

4. Distributed System Design for Data Engineering:

This talk will provide an overview of distributed system design principles and their applications in data engineering. We will discuss the challenges and considerations that come with building and maintaining large-scale data systems and how to overcome these challenges by using distributed system design.

5. Delighting South Asian Fashion Customers:

In this talk, our presenter will discuss how his company is utilizing AI to enhance the fashion consumer experience for millions of users and businesses. He will demonstrate how LAAM is using AI to improve product understanding and tagging for the catalog, creating personalized feeds, optimizing search results, utilizing generative AI to develop new designs, and predicting production and inventory needs.

6. Unlock the Power of Embeddings with Vector Search:

This talk will include a high-level overview of embeddings and discuss best practices around embedding generation and usage, build two systems; semantic text search and reverse image search, and see how we can put our application into production using Milvus – the world’s most popular open-source vector database.

7. Deep Learning with KNIME:

This tutorial will provide theoretical and practical introductions to three deep learning topics using the KNIME Analytics Platform’s Keras Integration; first, how to configure and train an LSTM network for language generation; we’ll have some fun with this and generate fresh rap songs! Second, how to use GANs to generate artificial images, and third, how to use Neural Styling to upgrade your headshot or profile picture!

8. Large Language Models for Real-world Applications:

This talk provides a gentle and highly visual overview of some of the main intuitions and real-world applications of large language models. It assumes no prior knowledge of language processing and aims to bring viewers up to date with the fundamental intuitions and applications of large language models.  

9. Building a Semantic Search Engine on Hugging Face:

Perfect for data scientists, engineers, and developers, this tutorial will cover natural language processing techniques and how to implement a search algorithm that understands user intent. 

10. Getting Started with SQL Programming:

Are you starting your journey in data science? Then you’re probably already familiar with SQL, Python, and R for data analysis and machine learning. However, in real-world data science jobs, data is typically stored in a database and accessed through either a business intelligence tool or SQL. If you’re new to SQL, this beginner-friendly tutorial is for you! 

In retrospect

As we wrap up our coverage of the Future of Data and AI conference, we’re delighted to share the resounding praise it has received. Esteemed speakers and attendees alike have expressed their enthusiasm for the valuable insights and remarkable networking opportunities provided by the conference.

Stay tuned for updates and announcements about the Future of Data and AI Conference!

We would also love to hear your thoughts and ideas for the next edition. Please don’t hesitate to leave your suggestions in the comments section below. 

Artificial Intelligence (AI), Machine Learning (ML), and data science have become some of the most significant topics of discussion in today’s technological era.

In one of the speakers’ sessions on the ‘Future of Data and AI’, several experts in these fields came together to discuss the latest advancements, how they are using them in their everyday work, and the connection of AI and society.

Introduction of panelists 

The session starts with Hamza, a research science manager at Google, introducing himself and explaining how he runs a few ML models and helps build models that can predict user abuse. Hamza works in the trust and safety group within search, where they prioritize the protection of users. 

Generative AI: Trends, Ethics and Societal Impact – Watch the complete session  

The other experts introduce themselves as well. Batool, who has experience working as an AI scientist at Amazon, focused on dialogue machines and natural language understanding.

Meanwhile, Francesca, a Principal Data Scientist manager at Microsoft, leads teams of data scientists and ML scientists, working on internal problems at Microsoft. Raja, the Founder, and Chief Data Scientist at Data Science Dojo has been working in data science before it was even called data science. 

Use of Generative AI 

The conversation then shifts to the use of generative AI, which has been used in the field of data science and ML for a while. Francesca explains that there are three main categories where generative AI is used every day in her work.  

The first is generating natural language, which includes summarization, translation, and question-answering systems. The second is an image and video generation, which has applications in industries like gaming and advertising. The third is generating music, which can be used for composing, arranging, and creating personalized music. 

A deeper understanding of the current state of the field 

The experts then discuss the latest advancements in these fields. Raja emphasizes the importance of the latest advancements in deep learning, specifically transformers, in NLP tasks. He also mentions the development of large-scale language models like GPT-3, which can perform tasks like translation, summarization, and question-answering. 

Matul discusses how chatbots have evolved from rule-based systems to data-driven systems, where they can use data to train and improve their performance. This includes using natural language processing to understand and respond to user queries more effectively. 

Francesca highlights the importance of democratizing AI and making it accessible to all people, regardless of their technical background. This involves developing user-friendly tools that can be used by people without technical expertise, which can be used to address common business problems. 

Generative AI – The impact of ground-breaking generative AI technologies 

Open AI has brought about a major transformation in the field of artificial intelligence (AI), data science, and machine learning. One of the most significant contributions of open AI is its generative AI capabilities that help in generating code, images, and troubleshooting bugs. These capabilities are particularly useful for data scientists who need to deploy and operationalize their machine-learning applications. 

Generating code from one programming language to another is one of the three main categories where generative AI applications have been seeing a lot of demand. Another popular application of generative AI is in generating images, especially for use cases such as generating images from text descriptions. 

For data scientists like the speaker, who work mostly in the AI, data science, and machine learning space, most of their work is done on the cloud. With open AI, data scientists can now access pre-trained generative AI models and customize them with their data. They can also use built-in tools to detect and mitigate any biases or unfair dynamics that may exist in their applications. 

Open AI has made accessing these tools easier through the open AI studio, where one can build AI models and deploy them faster. The speaker has found this to be a privileged situation and has been using generative AI for various communication purposes such as spot-checking, rephrasing, and creating snippets for social media posts. 

Human intelligence in conjunction with AI 

While AI has brought about a significant change in the field of content creation, the speaker warns against relying solely on AI. Human intelligence should be used in conjunction with AI to create the best results. AI is just another tool that should be used with caution, as a few wrong jumps can take you in the wrong direction. 

The other speakers in the panel discussion also shared their experiences with generative AI. One of them is writing a book that covers popular machine learning algorithms using fiction. While, until a few years back, his biggest concern was hiring graphic designers and concept artists, now, with generative AI, he can create his book’s graphics on his own. 

Generative AI’s impact on creative work  

Generative AI is impacting creative work and work in general in many ways. In creative industries, such as marketing, graphic design, animation, and content creation, generative AI is a valuable tool that allows for faster and more efficient production of high-quality content. It can also democratize access to expensive resources like models for photo shoots, making them more accessible to smaller designers. 

In other industries, such as manufacturing, healthcare, and energy, generative AI can also be used to improve efficiency and productivity. For example, it can be used to design new products, optimize manufacturing processes, and analyze medical images. 

Overall, generative AI has the potential to impact work across many different industries, and its adoption is likely to continue to grow as more businesses discover its benefits. While it may not eliminate jobs, it will likely change the nature of work in many industries, requiring workers to learn new skills to work effectively with these tools. 

Read about 12 must-have AI tools to revolutionize your work 

Francesca, emphasizes the importance of considering the ethical implications of working with AI systems, not just generative AI. She has a checklist of principles that she follows, such as fairness, reliability, safety, privacy and security, inclusiveness, accountability, and transparency, which are industry standards developed by tech companies. While principles are essential to keep in mind, Francesco also suggests that tools such as interpretML and fair AI can be leveraged to understand the impact of data on predictions and results better.  

OpenAI and generative AI have many benefits, such as improving content quality, variety, and personalization. However, to ensure these benefits follow ethical principles, the model life cycle, which starts with data collection, pre-processing, model building, and tuning model parameters and ends with prediction and interpretation, must involve humans in all stages.

Generative AI in healthcare and energy

AI in healthcare

There are many exciting ways that generative AI is being used to tackle important problems in the fields of healthcare and energy. One area where generative AI is being used in healthcare is in the creation of medical images such as X-rays and MRIs. With the help of generative AI, researchers can generate high-quality medical images that can help in the diagnosis and treatment of various medical conditions. 

It is also being used to develop new drugs and treatments. With the help of deep learning algorithms, researchers can analyze large amounts of medical data to identify new drug candidates and develop personalized treatment plans for patients. 

In the field of energy, generative AI is being used to optimize energy systems and reduce energy consumption. For example, AI models can be trained to predict energy usage patterns and adjust energy supply, accordingly, reducing waste and increasing efficiency. 

Another area where generative AI is being used is in the creation of virtual environments for training purposes. With the help of generative AI, researchers can create realistic virtual environments that can be used to train individuals in various fields such as medicine, engineering, and military training. This can help to reduce the risk of accidents and injuries during training and improve overall safety. 

Generative AI and government regulations

Overall, the role of the government in regulating the use of generative AI to create content is a highly debated topic. Some believe that the government should intervene to prevent monopolies from happening and to fund open-source projects to democratize data. Others argue that too much regulation could stifle innovation and competition.  

It is essential to strike a balance between promoting innovation and protecting consumers’ interests. Legislation and regulations could be created to define what constitutes fair use and set standards for the ethical use of AI, such as the AI bill of rights. Ultimately, governments will act following the general culture and society’s values in their region, making laws that align with what is considered acceptable. 

Closing of the session – Generative AI and society

In conclusion, AI, ML, and data science have become vital to our daily lives, with advancements in these fields impacting various industries. With the continuous development of new technology, it is essential to keep up to date with the latest trends and advancements to stay competitive in the industry. The experts in the session provided valuable insights into the latest advancements and how they are using them in their everyday work. As these fields continue to evolve, it will be exciting to see what new advancements will come next.