In the 1980s I was studying mathematics and physics, which I really enjoyed. I was concerned about my job prospects, there are not many math or science jobs outside of academia. Artificial intelligence seemed equally interesting but more practical, and I thought that it could provide me with a living wage. Little did I know that artificial intelligence was about to become an unmentionable phrase that you should not put on your resume if you wanted a paying job.
Highlights of the history of artificial intelligence
- In 1956 AI was founded.
- In 1957 Frank Rosenblatt invented Perceptron, the first generation of neural networks. It was based on the way the human brain works, and provided simple solutions to some simple problems.
- In 1958 John McCarthy invented LISP, the classic AI language. Mainstream programming languages have borrowed heavily from LISP and are only now catching up with LISP.
- In the 1960s AI got lots of defense funding. Especially military translation software translating from Russian to English.
This did not work out as planned. The machine translation did not work well enough to be usable. The defense funding dried up. The approaches that had worked well for small problems did not scale to bigger domains. Artificial intelligence fell out of favor in the 1970s.
AI advances in the 1980sWhen I started studying AI, it was in the middle of a renaissance and I was optimistic about recent advances:
- The discovery of new types of neural networks, after Perceptron networks had been discredited in an article by Marvin Minsky
- Commercial expert system were thriving
- The Japanese Fifth Generation Computer Systems project, written in the new elegant declarative Prolog language had many people in the West worried
- Advances in probability theory Bayesian Networks / Causal Network
AI in the 1990sI did not loose my patience but other people did, and AI went from the technology of the future to yesterday's news. It had become a loser that you did not want to be associated with.
During the Internet bubble when venture capital founding was abundant, I was briefly involved with an AI Internet start up company. The company did not take off; its main business was emailing discount coupons out to AOL costumers. This left me disillusioned, thinking that I just have to put on a happy face when I worked on the next web application or trading system.
AI usage today Even though AI stopped being cool, regular people are using its use it in more and more places:
- Spam filter
- Search engines use natural language processing
- Biometric, face and fingerprint detection
- OCR, check reading in ATM
- Image processing in coffee machine detecting misaligned cups
- Fraud detection
- Movie and book recommendations
- Machine translation
- Speech understanding and generation in phone menu system
Euphemistic words for AI techniques The rule seem to be that you can use AI techniques as long as you call it something else, e.g.:
- Business Intelligence
- Collective Intelligence
- Data Mining
- Information Retrieval
- Machine Learning
- Natural Language Processing
- Predictive Analytics
- Pattern Matching
AI is entering mainstream computing nowRecently I have seen signs that AI techniques are moving into mainstream computing:
- I went to a presentation for SPSS statistical modeling software, and was shocked how many people now are using data mining and machine learning techniques. I was sitting next to people working in a prison, adoption agency, marketing, disease prevention NGO.
- I started working on a data warehouse using SQL Server Analytic Services, and found that SSAS has a suite of machine learning tools.
- Functional and declarative techniques are spreading to mainstream programming languages.
Business Intelligence compared to AIBusiness Intelligence is about aggregating a company's data into an understandable format and analyzing it to provide better business decisions. BI is currently the most popular field using artificial intelligence techniques. Here are a few words about how it differs from AI:
- BI is driven by vendors instead of academia
- BI is centered around expensive software packages with a lot of marketing
- The scope is limited, e.g. find good prospective customers for your products
- Everything is living in databases or data warehouses
- BI is data driven
- Reporting is a very important component of BI
Getting a job in AII recently made a big effort to steer my career towards AI. I started an open source computer vision project, ShapeLogic and put AI back on my resume. A head hunter contacted me and asked if I had any experience in Predictive Analytics. It took me 15 minutest to convince her that Predictive Analytics and AI was close enough that she could forward my resume. I got the job, my first real AI and NLP job.
The work I am doing is not dramatically different from normal software development work. I spend less time on machine learning than on getting AJAX to work with C# ASP.NET for the web GUI; or upgrade the database ORM from ADO.NET strongly typed datasets to LINQ to SQL. However, it was very gratifying to see my program started to perform a task that had been very time consuming for the company's medical staff.
Is AI regaining respect?No, not now. There are lots of job postings for BI and data mining but barely any for artificial intelligence. AI is still not a popular word, except in video games where AI means something different. When I worked as a games developer what was called AI was just checking if your character was close to an enemy and then the enemy would start shooting in your character's direction.
After 25 long years of waiting I am very happy to see AI techniques has finally become a commodity, and I enjoy working with it even if I have to disguise this work by whatever the buzzword of the day is.