3 min read AI Research : The Need For Problem Recognition AI and machine learning have [...]
AI and machine learning have been in existence for some time. Only now are we coming towards the right richness of data to be able to make more intelligent models that can help transform raw data sets into something more valuable for both the enterprise and the customer. AI research is getting more complex day by day.
Today, most successful consumer applications are powered by AI. Tomorrow every successful business will be powered by AI. As a result, virtually every company is exploring the possibilities of AI in their product strategies – from facial recognition, to autonomous vehicles and self-learning connected products, it’s hard to imagine an industry that won’t soon be powered by AI. However, if you take a minute to look beyond the hype, AI is still in its infancy – but we are making important progress.
Last weekend, Mnubo attended AIFest, an event organized by MTL Data as part of Montreal’s world renowned Startup Fest. The event brought together more than two hundred data enthusiasts from around the Montreal community – the AI research capital of the world. Ary Bressane, Senior Data Scientist at Mnubo, took the main stage where he spoke about his experience as a data scientist, highlighting the importance of solving the right problems.
More often than not, data scientists and analysts spend so much time, money, and energy trying to solve the wrong problem that they miss the bigger picture. Many focus solely on building and marketing their AI-based technology, rather than first identifying the business value and societal opportunity, and building from there. As a result, and not surprisingly, less than 15% of projects involving big data and machine learning actually move from the proof of concept (PoC) phase to production and deployment. There is no question these professionals are excellent at identifying and evaluating solutions, however there is a capability gap when it comes to identifying and analyzing problems.
In his discussion, Ary addressed this challenge and put forth the Design Sprint methodology, originally developed by Google Ventures, and explained how Mnubo has adapted it to a one-day process to better identify and evaluate business problems before moving into production. The process involves designing, prototyping and testing
ideas with customers, and has become a critical point for Mnubo to distinguish realistic business opportunities from overhyped ideas.That being said, the hype around AI is justified. AIFest spotlighted
many exciting AI developments – from optimizing the user experience with chat bots and tools, to life altering applications in healthcare and cancer care, to addressing societal issues – education, climate change, and social development.
Montreal is the AI capital of the world, and AIFest showcased the strong startup ecosystem that is supporting this transformation. With such a strong turnout, participants discussed the idea of creating an open letter to send to the provincial government regarding the recently announced $100-million AI research cluster in Montreal. To accelerate the growth and development of business-led innovation, we are asking you to be actively engaged in creating and developing the cluster – see the open letter here and share your ideas!
A big thank you to MTL Data for hosting a great event, and to all participants for contributing exciting developments to the AI ecosystem!
Find out how Mnubo is tapping into the benefits of product data by transforming raw sensor data into actionable business insights. Read more here!
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