'60 Leaders' is an initiative that brings together diverse views from global thought leaders on a series of topics – from innovation and artificial intelligence to social media and democracy. It is created and distributed on principles of open collaboration and knowledge sharing. Created by many, offered to all.
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'60 Leaders on Artificial Intelligence' brings together unique insights on the topic of Artificial Intelligence - from the latest technical advances to ethical concerns and risks for humanity. The book is organized into 17 chapters - each addressing one question through multiple answers reflecting a variety of backgrounds and standpoints. Learn how AI is changing how businesses operate, how products are built, and how companies should adapt to the new reality. Understand the risks of AI and what should be done to protect individuals and humanity. View the leaders.
'60 Leaders on Innovation' is the book that brings together unique insights and ‘practical wisdom’ on innovation. The book is organized into 22 chapters, each presenting one question and multiple answers from 60 global leaders. Learn how innovative companies operate and how to adopt effective innovation strategies. Understand how innovation and experimentation methods blend with agile product development. Get insights from the experts on the role of the C-Suite for innovation. Discover ways that Innovation can help humanity solve big problems like climate change.
What is the academic curriculum for a successful career in AI?
Defining the multitude of ‘data professional’ roles has been a challenge over the last decade. For instance, the definition of Data Scientist has mutated terribly; many data scientists tend to use ML in order to best understand the data with which they work, but some major tech companies have also simply rebranded their traditional analyst roles as ‘data science’ roles in order to attract talent.
Regardless of job titles, a high-functioning data team, focused on complex problems, needs several contributors with a diverse set of skills to be successful. This includes:
- Data Engineering. An AI/data team will need data, of course, and professionals focused on managing large volumes of data for processing and preparation. These professionals, often called Data Engineers, need a moderate (though not extensive) background as software developers and must have a strong background in the principles of parallel processing with map-reduce concepts and the latest technologies for implementing them. Data Engineers will also have a strong background in mathematical literacy and must be fluent in the kinds of mathematical transformations applied to their data.
- Data/ML Science. Scientists/applied researchers work with the data to draw conclusions (especially predictions). The highest functioning scientists in this role will use the best tools at our disposal and so often (but not essentially) use ML. Ideally, an ML scientist will focus most of their time hypothesizing what kind of techniques (ML models) will detect desired patterns or generate successful predictive results, run experiments to test these hypotheses, and then conduct extensive analysis to understand if their hypotheses were validated (and in what ways). The best Data/ML scientists will have extensive (graduate level) ML training in addition to mathematical literacy, especially in the area of linear algebra, and ideally functional analysis. They will need decent coding ability but need not be software engineers. They will also require a background in statistical modeling, at minimum having a strong background in probability theory and hypothesis testing.
- ML Engineering. To make the team most efficient, I often like to have a dedicated group of ML engineers. An ML engineer’s job is to develop centralized code for the scientists to use to eliminate practical software challenges reproducing boilerplate code so that they can focus on understanding their experiments rather than implementing, debugging, and ensuring that their results have parity with prior work. ML engineers will fundamentally be software engineers but will have a working understanding of (though perhaps not graduate-level training in) the machine learning and mathematical literacy expected of scientists.