Whether it's reports of a new and wondrous technological accomplishment or of the danger we face in a future filled with unbridled machines, artificial intelligence (AI) has recently been receiving a great deal of attention. If you want to understand what the fuss is all about, Max Tegmark's original, accessible, and provocative Life 3.0: Being Human in the Age of Artificial Intelligence would be a great place to start. Tegmark successfully gives clarity to the many faces of AI, creating a highly readable book that complements The Second Machine Age's economic perspective on the near-term implications of recent accomplishments in AI and the more detailed analysis of how we might get from where we are today to AGI and even the superhuman AI in Superintelligence. Would intelligent machines that design and build new iterations of themselves represent a new form of "life" (the "Life 3.0" Tegmark refers to in the book's title)?
Perhaps no advancement over the past decade has had more of an impact on paid advertising, especially with Google AdWords and Facebook PPC than Artificial Intelligence (AI). In other words, it is the gathering of information on the personal habits of the potential customer so that paid advertising can be better directed at the right audience. By not only addressing your actions, it also considers your vulnerabilities which means that paid advertising in places like Google AdWords and Facebook PPC becomes more potent in its effects. This is not to say that paid advertising has replaced organic SEO in terms of marketing strategy, but there is no doubt that it has offered a positive impact which makes Google AdWords and Facebook PPC more effective in reaching the desired customer base.
Having a vision that grand is a huge gamble -- one that can only succeed if it's backed up by a solid business model, has a clear market that it targets, and is built on a clear foundation of targets and KPIs. While the solution is a highly technical, research-heavy one, it can only generate real value if it aligns with what our business team needs, how our photography team works, and how our product team wants to translate those needs and workflows. While significant statistical jumps in accuracy still require larger algorithmic improvements, a lot of gains can be made by iterative work (training with larger data sets, for example). You fix your precision, optimize for recall to solve search, fix your recall, optimize for precision for keywording individual photos -- it's a vicious cycle.
In a market that is primarily dominated by four major companies – Google, Microsoft, Amazon, and IBM – AI could possibly disrupt the current dynamic. The current AI-cloud landscape can essentially be categorized into two groups: AI cloud services and cloud machine learning platforms. Example of AI cloud services involve technologies such as Microsoft Cognitive Services, Google Cloud Vision, and IBM Watson. Azure Machine Learning and AWS Machine Learning are examples of cloud machine learning platforms.
The company has been developing the technology for the past year and has been testing it on the streets of Pittsburgh. The National Highway Traffic Safety Administration is currently investigating that accident (see "Tesla Crash Will Shape the Future of Automated Cars"). RajunathanRajkumar, a professor at CMU who is collaborating with General Motors on automated vehicle technology, says the Pittsburgh experiment will raise public awareness about how driverless systems work. But Rajkumar cautions that both the Singapore and Pittsburgh trials may highlight the remaining challenges for automated vehicles.
On March 8th Google showed how it applies artificial intelligence software to enable robots to pick unknown objects. Fanuc, a Japanese giant in industrial robots, announced its plan to connect 400,000 installed machines by the end of 2016. Another company Brain Corp., which until a few months ago only built software, recently launched an intelligent cleaning robot to try to cash in the value created through their software. Delivering turn key solutions to the end customer, means mastering hardware design, inventory management, warranties and customer support.
Deep learning is a somewhat new approach to machine learning and artificial intelligence that has caught fire over the past few years thanks to companies such as [company]Google[/company], [company]Facebook[/company], [company]Microsoft[/company] and Baidu, and a handful of prominent researchers (some of whom now work for those companies). The field draws a lot of comparisons to the workings of the human brain because deep learning systems use artificial neural network algorithms, although "inspired by the brain" might be a more accurate description than "modeled after the brain." Essentially, the stacks of neural networks that comprise deep learning models are very good at recognizing patterns and features of the data they're trained on, which has led to some huge advances in computer vision, speech recognition, text analysis, machine listening and even video-game playing in the past few years. You can learn more about the field at our Structure Data conference later this month, which includes deep learning and artificial intelligence experts from Facebook, Microsoft, Yahoo, Enlitic and other companies.