The success of the Web of Data (WOD) is based on the thorough understanding of, and agreement upon, the se- mantics of data and ontologies. But the Web of Data as a whole is complex, and inherently messy, contex- tualised, opinionated, in short: it is a market-place of ideas, rather than a database. Existing paradigms are in- appropriate for dealing with this new type of knowledge structures. The urgency of dealing with the non-standard charac- teristics of the Web of Data has been recognised, and separate initiatives try to tackle its individual manifes- tations, e.g.

Many of the most successful and widely used machine learning models are trained with the help of thousands of low-paid gig workers. Millions of people around the world earn money on platforms like Amazon Mechanical Turk, which allow companies and researchers to outsource small tasks to online crowdworkers. According to one estimate, more than a million people in the US alone earn money each month by doing work on these platforms. Around 250,000 of them earn at least three quarters of their income this way. But despite many working for some of the richest AI labs in the world, they are paid below minimum wage and given no opportunities to develop their skills.

USA TODAY is my favorite paper - how could it not be when I started reading it in high school when it first launched? So indeed, I am excited to see they are covering such an important topic in my world #AI #analytics and the risk of bias at scale - in a way non tech people can understand. This is why #diversity matters. This is why #data literacy matters.

Deep Learning and generally Machine Learning seems to have reached their limits. Indeed, these techniques are based on recognizing patterns by training with Datas (generally Big Data)… and that's the problem: on a large number of trials, Deep Learning and well-trained AI entities have a huge percentage of success… but what about on a single case? There, the AI can make big mistakes that 5 years old children would not do! So, we now realize that the Neural Network methods used in Deep Learning (in fact already very old – we "just" do now (big) improvements of more than 30 yo deep learning general techniques) – can't lead to an Artificial General Intelligence (AGI). So, what are the ways for moving to the next level?

Artificial Intelligence is changing SEO and therefore search. Shelly Kramer details how these changes are taking shape. It is another in our "Great Articles You may have missed" series. There is no shortage of topics to discuss when we're talking about artificial intelligence (AI). It's hot for a good reason: AI changes the game for the future of everything from social marketing to search.

Artificial Intelligence is changing SEO and therefore search. Shelly Kramer details how these changes are taking shape. It is another in our "Great Articles You may have missed" series. There is no shortage of topics to discuss when we're talking about artificial intelligence (AI). It's hot for a good reason: AI changes the game for the future of everything from social marketing to search.

The intention behind the query will matter more than the query itself. A key way AI is shaking up the world of search is by prioritizing query intent over query And no, I'm not just talking about misspelling a word--it's bigger picture than that. I'm talking about RankBrain, Google's machine learning system that makes up the three-legged stool that is Google's page ranking algorithm (behind relevant links and meaningful content). RankBrain harnesses the ability of AI to aggregate data--and infer from that data--to provide answers to questions that may not have even asked. It also thinks one step ahead: What will the person making this query want next?

And although some of jump is a result of continuing tech trends that have been playing out for years – consumer demand for smartphones, for example, is expected to generate a 3 percent uptick in product revenues this year – a substantial portion of the gains were attributed to new data-heavy technologies like the Amazon Alexa and Google Home digital assistants.

A lot of computational power is needed to solve deep learning problems because of the iterative nature of deep learning algorithms, their complexity as the number of layers increase, and the large volumes of data needed to train the networks. The dynamic nature of deep learning methods – their ability to continuously improve and adapt to changes in the underlying information pattern – presents a great opportunity to introduce more dynamic behavior into analytics. Greater personalization of customer analytics is one possibility. Another great opportunity is to improve accuracy and performance in applications where neural networks have been used for a long time. Through better algorithms and more computing power, we can add greater depth.

Artificial intelligence (AI) takes the power of computing systems to a different level. It is amazing to even think that a computing system can emulate human beings. There are many fantastic examples of AI in various areas of our lives. That said, computing systems are still considered limited in their capabilities because they cannot think creatively like human beings. While AI can process and analyze complex data, it still does not have much prowess in areas that involve abstract, nonlinear and creative thinking.