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AI Took My Job! Ken Jennings’ name is vaguely familiar to people, but why? Because his profound knowledge on all things trivial led to him being the unbeatable champion of a TV game show called Jeopardy! It also put him in the gunsights of IBM. They spent thousands of hours, invested millions of dollars, all just to build a machine named WATSON that could defeat him playing that TV-derived game. See how Ken deals with the consequences of coming out on the losing end of a battle with a computer that took his job – a fate which, according to this Oxford study , is likely to affect 47% of people over the next 20 years… Working Together? Synergy, says Garry Kasparov, is essential to the way humans and machines operate. As the World Chess Master for over a decade, he too became a target for IBM. They spent thousands of hours and millions of dollars to beat him at his own game. Kasparov doesn’t fret about being beaten by a machine. He worries that he might not be beaten by a machine; that human beings will choose to cower in their caves rather than work to create something greater than themselves. Machines can calculate, he says, and humans can understand; machines have instructions, and humans have purpose. When you add AI’s sheer speed to our desire to learn, it is an almost unbeatable combination. We’re better together. The question, of course, is whether Kasparov is right that there is some special, rather mysterious faculty of human “understanding” that machines cannot outperform, which many experts have come to doubt and reject... In Control Based on the real risk and virtual long-term certainty that AI will surpass humans in all domains of understanding, Sam Harris recommends a much more cautious approach. It is inevitable that we will advance our technology in all fields, and that includes AI itself. Imagine trying to get all of humanity to give up research, or to stop improving some aspect of their current lives, and you quickly see that it is infeasible. Sustained technological advancement, Harris argues, must eventually lead to superintelligent systems. He has no definite solution to the momentous risk/opportunity management question he poses, except that more of our best scientific resources should be allocated to it. We likely only have one chance to get it right… Part of the Solution (Literally)? Whereas Sam Harris hypothesizes that a safer way to create superintelligent AIs would be to incorporate them in our own biological brains, Ray Kurzweil suggests that we create a thinking-complex in the Cloud, and insert nanobots into our brains that are capable of linking to it upon need. We have 300 million “thinking modules” in our neocortex (the part of our brain most strongly associated with our abstract intelligence). Imagine if you could connect to a billion more in order to solve a problem. You may not remember the name of the two lead actors in the 1989 movie that you loved called “When Harry Met Sally.” Your memory isn’t perfect, and so you pull out your smart device and add a few thousand more computers to the task via Google’s search engine. Suddenly you are reminded that it was Meg Ryan and Billy Crystal, which opens a mental floodgate, and you recall that Carrie Fisher and Bruno Kirby were also in it, and that triggers even more memories. Being able to connect instantly to a Synthetic Neocortex would make it possible to solve immensely complex problems with relative ease. Seeing the Possibilities Fei-Fei Li talks about how AI will advance once it has significant vision capability. She and her team built a massive picture database (called ImageNet) and trained an AI to recognize images through constant exposure to millions of images that were labelled and described. Over the years the AI has been taught to analyse pictures more deeply, and learned to construct accurate sentences such as “This picture is of a large airplane sitting on a runway” when it “sees” that picture. Something a child can do automatically is often very difficult to teach to a computer. (Cf. Moravec’s Paradox , stating that contrary to initial assumptions in robotics research, some high-level reasoning requires very little computation, whereas low-level sensorimotor skills require enormous amounts of computational resources.) You have probably used the ImageNet technology with TinEye, Google Image, Root About, or Karma Decay, among many others where you can upload an image and then have its source identified, or its contents described in text, or read aloud to help people with limited vision understand images. Once AIs can accurately identify what they see, they provide us with a whole new set of insights. They can think about a million times faster than us and find patterns that escape our visual-cum-cognitive notice. Morality & the Machine: Aligning AI with Our Values The area of AI content-learning that may ultimately determine our fate the most is human morality. Despite substantial disagreement, our values as human beings are fairly universal and very close in the huge space of all possible values. Machine intelligences will have none of them by default – consider the possibility of “paperclip maximizing” superintelligences. It is up to us to ensure that, in time for the AI take-offs that are likely to happen this century, we find a reliable way of teaching the machines to value and pursue the things we value; to understand us and to seek the things we ourselves seek. In order for an AI’s decisions to be safe and beneficial, they have to pass (directly or indirectly) through the human filter. Philosopher Nick Bostrom proposes to make this problem a research priority for humanity. His TED Talk makes us edgy about the AI possibilities. But it also stresses that the AI alignment task, while momentous and enormously challenging, is not hopeless.
Demand for professionals in data science and analytics is expected to rise significantly over the next years (cf. this study by IBM). In order to keep track of future job trends, we started the DataCareer Job Market Index (DJMI) in July 2017. We track job openings on the biggest online job board, Indeed , in the fields of data science and analytics, data engineering, business intelligence, artificial intelligence and statistics. Sign up for our newsletter if you’d like to receive a monthly summary of the results. Methods We searched for jobs on Indeed using the following search terms: Data Scientist, Data Analyst, Data Engineer, Big Data, Machine Learning, Artificial Intelligence, Business Analyst, Business Intelligence, Statistics, Quantitative. Using the jobbR  package , we downloaded the jobs into R for further processing. Since the search returns some results we'd like to omit from the job index (e.g., general Scientist or Software Engineer jobs), we filtered these jobs out. The index only counts jobs that contain at least one of the following expressions in the job title: Data, Analy, Business Intelligence, Machine Learning, Deep Learning, Artificial Intelligence, AI, Computer Vision, Neural Net, Natural Language Processing, NLP,  Hadoop, SQL, Oracle, Statisti, Quant, Bioinform. This filters almost all jobs correctly, although it will miss a few data jobs that lack these keywords in the title (e.g., some general PhD student ads). The search is performed at the end of each month, with only jobs from the last 30 days being included. Using this method, how accurate can our job market estimates be expected to be? While we are likely to underestimate the absolute number of jobs, the relative changes we record over time should provide a very accurate measure of job trends. We therefore indicate relative changes compared to the reference point of July 2017, which is set to 100 in our market index. Results In Switzerland, data job ads increased by 28% between July 2017 and January 2018. The drop in December (-13%) can likely be explained as a seasonal fluctuation, given that companies publish less job openings during the holiday season. During our reference month, July 2017, the index included 544 jobs. In Germany, job ads increased by 36% between July 2017 and January 2018, with the biggest rise occurring in January (+17%). Interestingly, we did not observe a drop in job openings during December. In July 2017 we counted a total of 2’492 jobs. In the biggest data job market, the US, jobs remained relatively stable between July and December 2017. In January 2018 we observed a 12% increase in jobs. The total number of US data jobs was 35’947 in July 2017. In the UK, jobs increased by 24% in January 2018 after a drop of 18% in December 2017, . Again, we expect this to be an effect of the holiday season (which apparently leads to either a significant decline or stagnation). In July 2017, we counted 7’633 jobs for the UK. If we consider the job counts for January 2018 and relate them to population size, we get the following number of jobs per million inhabitants: 125 for the US, 125 for the UK, 81 for Switzerland, and 41 for Germany. However, these numbers should be taken with caution, as the percentage of jobs that are published on Indeed may differ between the countries. We’re currently conducting research into open questions such as the popularity of Indeed in each country and the factor by which Indeed-based estimates underestimate the total number of jobs. For questions, comments or if you’d like to collaborate on our research, don’t hesitate to send us an Email at info@datacareer.ch.
Mobile phone data has a vast scope. Our phones track our location, record social activities by listing who we call or message, and know what we like or what we’re looking for by collecting data on our online behavior and use of apps. The recent Mobile User Demographics Challenge on Kaggle (by the Chinese platform TalkingData ) offers some insight into the volume and precision of the information available on mobile users. The data challenge asks participants to predict the age and gender of Chinese mobile users based on their phone data, particularly the apps they are using at various times and places. For another example, check out the Development Challenge by Orange : It highlights the huge potential such datasets hold, especially for developing countries, where reliable data is often a scarce resource. The winning projects use detailed mobile phone data for urban planning in Senegal. Among other things, they predict electricity demand and model several socio-demographic factors relevant to urban design. As the idea of open data spreads, an increasing number of mobile providers have started to offer free access to anonymized datasets. A brief analysis of Swisscom mobile phone data On its Open Data Porta l, Swisscom provides detailed data on the number of voice calls, the number of SMS and the data downloaded per Swiss canton in July 2017. In what follows, we’ll take a closer look at the voice calls that occur on a busy working day. The graph below plots the number of daily calls happening over the course of the month. Clear patterns emerge: First, there’s a very significant difference between working days and weekdays. Second, as July evolves and the holiday season starts, the number of calls shrinks substantially. When do people get up or go for lunch? Calling behavior provides insight into people’s daily habits. The Swisscom datasets contain information on the number of calls per hour for each day. A massive share of calls is work-related, which explains the difference between working days and weekends. On working days, the Swiss get up quite early: At 7am, almost a million calls have already taken place. Calling peaks at 10am and decreases over lunch time. The workday seems to end around 5pm – with a sharp decrease in calling activity. On weekends, phone calls start significantly later, with Sundays being particularly quiet. Pronounced regional differences Given the structure of the available dataset, a comparative analysis across cantons seems natural. Dividing the average number of daily calls by each canton’s population yields a large variation (see the map below). In the two Appenzell cantons, less than 1 call per person and day is recorded, whereas the average Ticino inhabitant makes 2.6 calls per day. Overall, calling activity is significantly higher in the peripheral French- and Italian-speaking parts of the country than in the German-speaking part. While the data provides a good general overview of the users’ behavior, regional differences may also be affected by local variation in the composition of users and market shares for certain products. In 2016, Swisscom had a share of 57.7% on the Swiss mobile market, with local market shares differing substantially. The next figure displays the calling activity on a working day in Geneva, Ticino and Zurich (calls relative to the busiest calling hour). Overall, the calling patterns run very similarly. The working day in Geneva seems to start slightly later, which translates into some lag of the lunch time and evening declines as well. Calling in Ticino seems to be focused on mornings and to level off in the afternoon. The data for Sundays reveals that Swisscom clients in Ticino take some break from calling in the afternoon. In Geneva, both Saturday and Sunday nights seem to last longer than in Zurich or Ticino. Some insights into the daily habits of Switzerland’s inhabitants can be deduced even from crude datasets like the one examined here. We’re curious what the next uploads on Swisscom’s Open Data Portal will reveal.
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