Pablo González (Deloitte Digital) was the first speaker to discuss the current state of art of AI. Pablo unveiled some of the main fields in which Artificial Intelligence is being applied, and he introduced some use cases as well as giving some lines about what can be achieved thanks to the tools we have. In fact, he considers that being aware of the limitations of current technologies is key: to know what can be done and what has still to be researched and developed. We will only be able to harness its full potential in business when we know its limits.
Some of the trends analyzed during the 4th edition of Madrid.AI included the possibilities of eye-tracking systems, the possibilities and limitations of Artificial Intelligence and the applications of Deep Learning in the real world. Pablo González in the picture.
Can you guess what point on the screen you are looking it? Is there any way you can tell by reading these lines? These are the questions that Samuel Muñoz posed to the auditorium, and even though it seems easy to think about it, it is much more complex to effectively achieve it. Thus, an eye tracking system is the ideal project to learn about the difficulties of applied machine learning, from gathering training data to building an acceptable final product. Samuel provided a step-by-step explanation of the process that must be followed to successfully develop this type of projects, and how to overcome the challenges and obstacles found. Because we always find obstacles, such as the trolls that intentionally look at a different point on the screen to confuse the machine; theory is one thing and practice quite a different one.
Samuel Muñoz sharing with the audience the possibilities of an eye-tracking system.
Ana de Prado (Machine Learning & Robotics Program Leader at Terminus7) and Gabriel Muñoz (Data Scientist Program Leader at Terminus 7) explained to the audience the main real applications of Deep Learning, the most promising branch of AI. In fact, DL faces many challenges in production, as Ana and Gabi stated: data quality, scalability of the solution, training and response times, validation and versioning of the models, etc. In order to face these challenges when working with DL, our experts recommended the following:
- Analyzing whether Deep Learning is the best solution for a business or not.
- Choosing the right metric for your business.
- Taking into account the time needed for every part of the project.
- Keeping in touch with the customer, as much as required.
In this post, we provide a transcription of the talk that Ana and Gabi gave (in the picture) during the 4th edition of Madrid.AI. They also reminded us about some of the most interesting benefits of AI when applied to the business, in addition to the accuracy of the results:
- Time-to-market: delivering at the right time, in a highly competitive environment. Being the first in a sector can mark the difference.
- Production ready: by focusing on the actual business workflow, you can develop a solution that is integrated into everyday decisions.
- Multi-sector: according to a research conducted by the Harvard Business Review, information technology, marketing, finance and accounting, and customer services are some of the sectors working with AI solutions, though it can be found in any company.
- Automation: a huge volume of resources can be saved thanks to automation.
- Cutting down the times: according to the analysis conducted by Business Insider, powering platforms, apps and interfaces and bots help companies by spending less time on routine administrative tasks, but also on improving strategies or, for example, to create better products.
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