PREDICTING THE FUTURE ISN’T MAGIC, IT’S ARTIFICIAL INTELLIGENCE | PART-3
Is machine learning right for you?
There’s been a lot of buzzing in the news about what machining learning is and what it’s not, and there’s a lot of misunderstandings that people get about what it really is. What it really is is a very particular set of algorithms that solve very specific problems. Machine learning is only targeted towards specific types of problems such as classifications, or regressions, recommendations, those kinds of things.
The subset is a lot miniature than what a lot of people think, but a lot of it gets mistaken, based upon a lot of the things you see in the news, such as like self-driving cars, and how machines are producing scripts for movies and things like that.
Machine learning is directed for a major growth spurt. Machines can’t learn everything regarding business or its customers. But companies like Apple, Spotify and Alibaba are urging that boundary back further and further. Now, with machine learning advancing disruptive innovation easier than ever before expect to see new startups agitating the existing market leaders.
Do AI technology tackle specific business problems
Technology is the most crucial element of a successful AI deployment strategy, as it helps to put the various pieces together and ensure successful implementation of automated systems and processes. Comprehensive AI tools and solutions that can handle end-to-end enterprise processes are always a better option for businesses as compared solutions that are dedicated to very specific business functions. Moreover, such solutions are not only more efficient in the long-term, but also eliminate the need for building a huge team to develop and deploy different solutions whenever the need arises.
Furthermore, enterprise AI solutions and platforms are also easily scalable, allowing managers and leaders to tackle specific business problems across the enterprise and derive maximum value out of it for the entire organization. Consequently, this can be the key to gaining a lasting competitive edge in their respective sectors and markets.
How AI helps to achieve business
The factor driving the success of AI implementation in enterprises is a business context, which enables an AI solution to perform in a manner and deliver results that help achieve a larger business goal. Let us look closely at the elements of AI strategy to see how they can help businesses achieve this in some companies:
Retail giant Target found that machine learning can be used to predict not only purchase performance but also pregnancy. In fact, Target’s model is so accurate that it can reliably guess which trimester a pregnant woman is in based on what she’s bought. After a father found through Target’s determined improvements that his 16-year-old daughter was pregnant, Target actually had to dial its lead back by mixing in less specific ads.
Most companies’ promotions are handled by the seasons or holidays. Snow shovels spread on sale in July, sunscreen in June. But consumers go for seasons in their own lives, too. The critical time to sell someone a car, for example, is right later she just bought one. It might be the best time, despite, to market car insurance to that person. Machine learning can pick up on those rhythms, helping companies prescribe their products to customers when the timing is just right.
When someone posts a photo on Twitter, she/he craves people to see it. But if the thumbnail is not fair, nobody is going to click on it. Twitter resembles to have solved this problem by using neural networks. In a scalable, cost-effective way, the social media firm is using machine learning to crop utilizers photos into compelling, low-resolution show images. The outcome is fewer thumbnails of doorknobs and more of the unusual signs just above them.
Apple freshly filed a patent that, in non-technical terms, indicates that it’s prioritizing cross-device personalization. In the expected future, for example, a user’s Apple Watch might recommend an iTunes playlist to match his heartbeat goal in another app.
500 million people shop with Chinese local giant Alibaba. Each of those customers goes through a separate and different journey, from searching to buying. How does Alibaba follow and tailor each of those 500 million journeys? With machine learning, of course.
Alibaba’s practical storefronts are customized for each shopper. Search results set up ideal products. Ali Xiaomi, a conversational chat-bot, manages most spoken and written customer service requests. Where chat-bots is an AI-enabled software performs automated interactions with humans in terms of addressing queries and other requests on digital platforms like apps and websites. Every part of Alibaba’s business was created for the shopper involving with it, and everything the shopper gets teaches the machine more nearly what the shopper wants.
Analysis of the professional networking site has shown that India is the third best-placed economy after the US and China to crack AI jobs and careers with high penetration of AI awareness. AI is a big part of investments in estimated by private players alone in 2016.
The development of technologies may have been expected, but the manner in which they have been disrupted the number of industries in different ways is unprecedented. These technologies will only develop further, the prospect of which is immensely exciting. Among the more exciting opportunities, one can expect in 2019 is the rising application of AI in healthcare and smart infrastructure in urban planning and development.