Innovation Alphabet
Machine Learning
In a nutshell
Machine learning is a subcategory of artificial intelligence. It is a process that uses mathematical models of data to help computers develop the ability to learn automatically, make data-driven predictions, and improve their performance through experience. And we users, through our networked activities and released information, are the ones who dress ourselves up as more or less knowledgeable teachers.


A clarification
AI, Machine Learning, Deep Learning
Those three term are very popular nowadays in enterprise IT, and sometimes (mistakenly) used as synonyms. Artificial Intelligence, though, is the biggest container referring to the simulation of human intelligence by machines. Among AI technologies there are machine learning and deep learning, which, in turn, is a form of machine learning through which computers process large amounts of data and create knowledge from examples. In short, Deep Learning is that subset containing, for example, cell phones that recognize faces, videos that recognize dialogue, and cars that recognize obstacles.
Application Fields
• Search engines: If we search for one or more keywords on Google and related browsers, there appear lists of results, the so-called Search Engine Results Pages (SERP). They are the product of algorithms that – as a consequence of analyzing patterns and structures in the data – give as output information that is considered to be consistent with the search performed.
• Educational scope: Technology makes it possible to create customized online training courses (e-learning). Data improve educational offerings because they can adapt them to the user’s needs in real time.
• Safety: Some solutions can recognize pedestrians and traffic signs, or support a driver. Cars are able to detect obstacles, traffic lights, signs and any impediments that may be encountered along the way. The data collected is recycled to teach smart cars how to “behave” on the road from time to time.
Industries
• Machine learning in the pharmaceutical industry
Machine learning used in the medical field enables an initial drug screening to predict the success of different components based on biological factors. A strategy that helps evaluate and increase the success rates of new drugs, reducing operating costs over the long run. Novartis has already adopted the technology to have computers predict (on the basis of previous operations) the constituents that researchers will need to use to successfully make desired drugs, increasing their speed to market.
• Machine learning in navigator apps
DeepMind is the name of the London-based artificial intelligence lab owned by Google’s parent company Alphabet. Scientists have developed a new algorithm to compare real-time road conditions with traffic histories to ensure an increasingly accurate estimation of arrival time. The information is collected anonymously from connected devices, combined with useful information such as speed limits. But DeepMind has enabled more detailed analysis. One of its tests showed that prediction errors decreased by more than 51 percent compared to previous traffic apps.

• Machine learning in image processing
Yelp’s algorithms help company staff collect, classify, and label images more efficiently. The software uses image processing techniques to identify the color, texture, and shape of food. Deep learning recognizes the constituent elements of an image or video by training machines to process, analyze and understand content. It can recognize whether a photo depicts a pizza or if a restaurant has outdoor seating.
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• Machine learning in support of e-commerce
Machine learning algorithms used by Amazon recommend the right product for visitors’ still latent needs and generate 55 percent of e-commerce sales. Moreover, the information captured by such algorithms helps Amazon predict product demand, thus facilitating the inventory according to seasonality and trends.
• Machine learning in support of customer experience
Through smart algorithms, Netflix is able to learn about users’ preferences in advance and then offer TV and movie content more in line with unconsciously expressed tastes. The data inspires those in charge to make logical and effective choices. Otherwise, it would not be possible to offer recommendations on what to watch based on the user’s odds of liking.
• Machine learning in support of marketing
Albert is a robot powered by machine learning and artificial intelligence that aims to revolutionize digital advertising. It can be applied to various marketing channels, including social media and email. The software can predict consumer conversion rates and bring concrete benefits: for example, Harley-Davidson increased its sales by 40 percent because Albert identified and targeted potential buyers that the company didn’t even know it had.