I have attended PyData once again the London PyData meet-up and I am as happy as I was the first time. The day started with some news from the organisers who listed some interesting discoveries within the python ecosystem:
Life has been very busy (but good) these last weeks both from the professional and personal life and I have neglected the blog. I will change this in the next weeks and try to come back to my usual speed. For the moment being, I will share my answer to a question that I have been asked in several occasions in the past, especially when I visit universities: “What are the skills that the industry is looking for in new developers?”:
I have been quite curious about the IBM Watson ecosystem and their set of APIs for quite a long time now, and I have finally found some time to start playing with some of its modules. The ecosystem has numerous APIs that expose functions to solve different problems such as personality detection or machine translation to cite a couple of them. In my particular case, I was more interested on the APIs provided by one of their recently acquired companies, AlchemyAPI, that provides Natural Language Processing (NLP) operations. After looking at all the possible options, I decided to investigate the following set of calls to get an idea of their accuracy and flexibility:
Artificial Intelligence (AI) has been an active field of study for more than half a century. However, some of the recent breakthroughs accomplished both by the academic community and some companies are fuelling the interest, imagination and even fear from the general public. This can be seen on the growing numbers of novels and movies portraying (usually evil) AI-driven robots or entities that want to (usually) wipe out the human race. This huge interest was perfect for an event like the one Playfair Capital organised at the magnificent Bloomberg HQ in London. We had an amazing list of speakers from academia and industry, as well as some of the main writers about Artificial Intelligence.
Word2Vec is a novel technique that produces a vector representation of documents where the meaning and relationships between words is encoded spatially. Therefore, words that are related to each other are closer on the defined feature space. Word2Vec is gaining huge traction in the machine learning community and it is definitely worth to know more about it. This blogpost will illustrate the main characteristics of this methods and it will provide an proof of concept using Clojure libraries.
The second (and last) day of the conference started with presentations from two massive companies: Philip Radley shown how BT is relying on Hadoop to achieve a lot of increase in value for their clients; and Rod Smith (from IBM) defended the position that digital innovation is nowadays driven by real time insights. He claimed that realtime is becoming a critical cornerstone and summarised the three types of data analysis process we have seen in the last years:
- Traditional: Time spent moving data around rather than analysing it.
- Big Data: Driven by contextual data, more time analysing than driving actionable insights.
- Rapid insights: Just in time quick approximations of solutions.