If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This could help you in building your first project! Be it a fresher or an experienced professional in data science, doing voluntary projects always adds to one's candidature. My sole reason behind writing this article is to get your started with recommendation systems so that you can build one. If you struggle to get open data, write to me in comments. Recommendation engines are nothing but an automated form of a "shop counter guy".
Suddenly, artificial intelligence (AI) is everywhere. For decades, the dream of creating machines that can think and learn like humans seemed like it would be perpetually out of reach, but now artificial intelligence is embedded in the phones we carry everywhere, the websites we use every day and, in some cases, even in the appliances we use around our homes. The market researchers at IDC have predicted that companies will spend $12.5 billion on cognitive and AI systems in 2017, 59.3% more than they spent last year. And by 2020, total AI revenues could top $46 billion. In many cases, AI has crept into our lives and our work without us realizing it.
Demand for data has been surging over the past few years. Companies are rushing to adopt in-house data warehouses and business analytics software, and are reaching for public and private databases in search of data to kick-start their artificial intelligence/machine learning (AI/ML) strategies. Due to the growing demand, good data is becoming a valuable commodity, like oil in the 20th century, and companies are beginning to compete for the most lucrative reserves. In order to understand why data is important for your business, you must first understand the five reasons if gives you a competitive advantage. Until very recently, companies did not realize that they were sitting on a goldmine of data and did not know what to do with it.
In the previous article, I talked about how one can make use of the internet in a highly productive manner (for those who haven't read the article, can take a look here) which can help increase knowledge and open doors for different opportunities as well. Today, I'll be discussing how startups are making use of artificial intelligence and how they are turning their products and services smarter in order to serve the consumers in a better way. The youth and the coming generations are extremely motivated and inclined towards the idea of starting a startup and pursuing the dream of becoming an entrepreneur. Though this dream comes true only with tons of determination and dedication towards the work you do. Many startups have been made in the recent years and each of them offering a unique product or service is making news in the industry.
For the past few years, the travel industry has been exploring innovative ways to utilize artificial intelligence (AI), in an effort to unlock the promise of more efficient communications and greater customer service between travelers and service provides. So far, most of that potential has remained largely untapped, despite significant advances in both travel and AI sectors. WayBlazer however, is building an extremely powerful travel recommendation engine, and it's doing it with a little help from AI. WayBlazer's Travel Graph uses artificial intelligence to learn about tens of millions of travel products and thousands of global destinations. It ingests and extracts useful from descriptions, reviews, blogs, images, and videos to develop a frame of travel intelligence that's used to power the most relevant recommendations for today's travelers. By using machine learning models, their travel graph gets smarter with every user search.
Be it driving up revenue-through customer discovery, analytics, driving down costs–through increased efficiency, cost reduction, or improving customer satisfaction-through a better user, and customer service experience. AI is even helping to predict customer behavior, providing advice to customer service agents on how best to solve a particular issue. Early detection of diseases: Using deep learning, companies like IBM and Google have been able to make breakthroughs around early detection of diseases such as cancer, cardiovascular disease, and diabetes with greater accuracy. An example would be targeting shoppers with a recommendation of shopping items or helping medical discoveries by studying user specific data such as behavioral patterns for eating, exercising, sleeping etc.
Another team matched their score on the test dataset, but the winning team scored best on the "hidden dataset" that Netflix used to score contestants' entries. Recommendation systems are now seen in nearly all online stores – products are recommended to customers that are based on a variety of filtering algorithms: Collaborative Filtering - CF (How similar are this customer's tastes and interests to other customers who also viewed/bought this item? Consequently, successful CF depends upon a good model of the customer's preferences, CBF depends upon a model of the store's content or products (e.g., a topic model), and CxBF depends upon a model of the customer's context (e.g., situational analytics). That was successful data science, successful predictive analytics, and successful knowledge discovery from data, proving once again that knowledge is power, especially in big data marketing!
Knowing how to write high quality software -- the days of one team writing throwaway models and another team implementing them in production are slowly coming to an end. With programming languages like Python and R and their packages making it easy to work with data and models, it is reasonable to expect a data scientist or machine learning engineer to attain a high level of programming proficiency and understand the basics of system design. Knowing how to write high quality software -- the days of one team writing throwaway models and another team implementing them in production are slowly coming to an end. With programming languages like Python and R and their packages making it easy to work with data and models, it is reasonable to expect a data scientist or machine learning engineer to attain a high level of programming proficiency and understand the basics of system design.
Recommendation engines are data filtering tools that make use of algorithms and data to recommend the most relevant items to a particular user. According to the article Using Machine Learning on Compute Engine to Make Product Recommendations, a typical recommendation engine processes data through the following four phases: collecting, storing, analyzing, and filtering. Explicit data consists of data inputted by users, such as ratings and comments on products. We use the K-nearest algorithm, Jaccard's coefficient, Dijkstra's algorithm, and cosine similarity to better relate the data sets of people for recommending based on the rating or product.
Content based systems (CF) rely on a typical description of items over feature vectors, and then recommend novel items to users by computing some similarity metric between them and the items that the user has already rated. The content-based component of the system encompasses two matrices: the user-user and the item-item proximity matrices, both obtained from applying the relevant distance metric over a set of features that characterize users and items, respectively. The CF component of the system, relies on the typical user-user and item-item similarity matrices computed from the known, past user-item ratings, providing for a memory component of the recommender. The Jaccard distance, on the other hand, neglects the covariances from the rating scales altogether, preserving only the information about the extent of the shared ratings.