As I looked around some of the artificial intelligence (AI) related acquisitions in last few years, I got convinced, at least, on the fact that majority of large companies such as Google, Facebook, IBM are seeing various different artificial intelligence topics to play a key role in impacting end users’ life (and hence, developers) in the future. With that as the background, it gave me good enough reasons to start doing research in relation with following:
- As students, what topics of artificial intelligence should they take up as one of their computer science courses while selecting under-graduate & post graduate courses?
- As developers/programmers, what topics of artificial intelligence should they start focusing upon in terms learning basic fundamentals?
- As entrepreneurs, which areas of artificial intelligence should they consider for their next startup?
- As investors (primarily, angel/seed), which areas of artificial intelligence should they consider for doing investment?
The article presents an analysis in different fields of artificial intelligence in relation with following:
- AI topics that are currently researched in top 20 universities in US and,
- AI topics which is seeing major acquisitions
AI Research in Top 20 US Universities: An Analysis
Following is a plot representing the AI topics against their occurrences in top 20 US universities:
From above plot, one could rank AI topics in following order of occurrence that could also act as some sort of guideline for students to select their courses appropriately:
- Machine learning
- Natural language processing (NLP) that includes related topics such as computation linguistics
- Knowledge representation, discovery, retrieval
- Human-computer interaction
- Reasoning with uncertainty
- Complex systems
Following represents these AI topics vis-a-vis some of the universities where they are taught:
|Machine Learning||University of Chicago,Duke University,University of Pennsylvania, JHU, WUSTL, Cornell, Vanderbilt University, Rice|
|NLP, Computational Linguistics||University of Pennsylvania, JHU, Brown University, Cornell|
|Robotics||Duke University,MIT, University of Pennsylvania, JHU, Cornell|
|Knowledge Representation||Cornell,Vanderbilt University|
|Vision||Duke University, University of Pennsylvania, JHU, WUSTL,Cornell|
|Human-Computer Interaction||Duke University, WUSTL|
Following represents overview on different acquisitions that happened in some of the above mentioned AI topics:
As mentioned on Wikipedia page, Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.
Machine learning has seen many acquisitions in last year or so. Some of them are following:
- Google acquired DeepMind in $500+ Million: Read the details on the acquisition on Techcrunch page. The acquisition reportedly strengthened Google portfolio in the area of “Deep Learning“.
- Yahoo acquired LookFlow: As reported by techcrunch, the acquisition was related to the field of “Deep Learning“.
Following have been some of the acquisitions in the area of Robotics:
- Google acquired Boston Dynamics: As reported by Techcrunch, it was Google’s eighth acquisitions in the area of Robotics. This only confirms of the fact how committed is Google to make a mark in the field of Robotics.
Vision represents some of the following topics of research:
- Recognition, image recognition, image databases
- Machine vision
- Motion, stereo and segmentation
This field has seen many a acquisitions in the recent times, some of which are following:
- Facebook acquired Face.com and later codenamed the project as DeepFace.
Top 5 AI Topics For Developers/Entrepreneurs Focus
Following are top 5 topics in the field of artificial intelligence that could be considered by developers and entrepreneurs for their time & money:
- Machine learning
- Natural Language Processing
- Knowledge representation, discovery and retrieval
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