Machine learning (ML) extracts meaningful insights from raw data to quickly solve complex, data-rich business problems. ML algorithms learn from the data iteratively and allow computers to find different types of hidden insights without being explicitly programmed to do so. ML is evolving at such a rapid rate and is mainly being driven by new computing technologies.
Machine learning in business helps in enhancing business scalability and improving business operations for companies across the globe. Artificial intelligence tools and numerous ML algorithms have gained tremendous popularity in the business analytics community. Factors such as growing volumes, easy availability of data, cheaper and faster computational processing, and affordable data storage have led to a massive machine learning boom. Therefore, organizations can now benefit by understanding how businesses can use machine learning and implement the same in their own processes.
ML helps in extracting meaningful information from a huge set of raw data. If implemented in the right manner, ML can serve as a solution to a variety of business complexities problems, and predict complex customer behaviors.
What is Machine Learning :
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
What is the difference between AI and machine learning?
Machine learning may have enjoyed enormous success of late, but it is just one method for achieving artificial intelligence.
At the birth of the field of AI in the 1950s, AI was defined as any machine capable of performing a task that would typically require human intelligence.
AI systems will generally demonstrate at least some of the following traits: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.
Alongside machine learning, there are various other approaches used to build AI systems, including evolutionary computation, where algorithms undergo random mutations and combinations between generations in an attempt to “evolve” optimal solutions, and expert systems, where computers are programmed with rules that allow them to mimic the behavior of a human expert in a specific domain, for example an autopilot system flying a plane.
Why is machine learning so successful?
While machine learning is not a new technique, interest in the field has exploded in recent years.
This resurgence comes on the back of a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision.
What’s made these successes possible are primarily two factors, one being the vast quantities of images, speech, video and text that is accessible to researchers looking to train machine-learning systems.
But even more important is the availability of vast amounts of parallel-processing power, courtesy of modern graphics processing units (GPUs), which can be linked together into clusters to form machine-learning powerhouses.
Today anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon, Google and Microsoft.
As the use of machine-learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. An example of one of these custom chips is Google’s Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which machine-learning models built using Google’s TensorFlow software library can infer information from data, as well as the rate at which they can be trained.
These chips are not just used to train models for Google DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photo, as well as services that allow the public to build machine learning models using Google’s TensorFlow Research Cloud. The second generation of these chips was unveiled at Google’s I/O conference in May last year, with an array of these new TPUs able to train a Google machine-learning model used for translation in half the time it would take an array of the top-end GPUs, and the recently announced third-generation TPUs able to accelerate training and inference even further.
As hardware becomes increasingly specialized and machine-learning software frameworks are refined, it’s becoming increasingly common for ML tasks to be carried out on consumer-grade phones and computers, rather than in cloud datacenters. In the summer of 2018, Google took a step towards offering the same quality of automated translation on phones that are offline as is available online, by rolling out local neural machine translation for 59 languages to the Google Translate app for iOS and Android.
How Big MNC’s are getting benefitted :
If I talk about Facebook, like how they use the AI for enhancing and evolving their market.
Facebook is building its business at high speed by learning about its users and packaging their data for the benefit of advertisers. The company functions around the goal of connecting every person on the planet through Facebook-owned tech products and services (such as Whatsapp, Instagram, Oculus and more) within 100 years.
Facebook has evolved as a platform enabling conversation and communication between people as a highly valuable source of knowing their lifestyle, interests, behavior patterns, and taste inside and out. What do individual users like? What don’t they like? This data — voluntarily provide but messily structured — can be utilized for profit at an exorbitant value.
That’s where AI comes in. AI enables machines to learn to clarify data, all by themselves. The simplest example of this would be AI image analysis identifying a dog, without telling that machine what a dog looks like. This begins to give structure to unstructured data.
A brilliant tool used by Facebook is called Deeptext, which deciphers the meaning of the content posted to find the relative meaning. Facebook then generates leads with this tool by directing people to advertisers based on the conversations they are having. It offers user-related shopping links to connect chats and posts to potential interests.
Another great feature of the Facebook is that ,it map the people of the whole world using AI.Now it covers almost all the Africa and in the future it is believed to be covering the whole with that .
It also helps in preventing the suicide among the people which is severe case .Facebook do it using ai like it learn the pattern and the post and conversation of the people and In this way it help in prevent the suicide.
It also detect the bad content like nudity ,terrorism ,harsh comment.In this case ,facebook immediately take action and close that particular account.
Proposals are accepted on a rolling basis and are evaluated “on their scientific merit,” Bellard says, in addition to their innovativeness and scalability. Selected applicants receive compute credits for Microsoft’s Azure AI Platform in increments of $10,000, $15,000, or $20,000, depending on their project’s scope and needs, and additional funds to cover costs related to collecting or labeling data, refining models, or other engineering-related work. They also gain access to Microsoft engineers, who work with them to accelerate development and incorporate their innovations into “platform-level” partner services. To that end, InnerVoice combines avatars with written text, pictures, and video to create experiences that help learners identify the connections between speech and language. Its videos abstract concepts, while the avatars label what’s happening using facial expressions and emotional tone of voice, and users practice conversations with the avatars — a superhero, drawing, or photograph of a loved one — and learn words by taking pictures that machine learning algorithms detect and label.
IBM is hard at work trying to disentangle the concepts behind artificial intelligence (AI) to clients, explaining to them how the technology makes decisions. Eighty-two percent of C-suite executives it researched said they wanted to use AI but were concerned about unconscious bias and the skills needed. It’s offering AI for a range of services and has implemented it itself in areas such as recruitment where it’s used to make sure there is no bias in how job descriptions are written, according to IBM Senior Vice President and Chief Marketing Officer Michelle Peluso.
“Technology can help to make sure there’s not bias in promotions and the like and so (there is) this grounded belief at IBM that inclusion is part of our ‘brand state’,” she told CNBC’s “Marketing Media Money.”
There are several ways marketers can best use AI, Peluso said. The first is in getting to know customers. “It allows us to understand more about our customers. We can analyze tone. We can listen in on chat bots, we can analyze personality and social (media), so we have the ability to develop a richer understanding of our customers,” she said. AI is also being used in how businesses interact with their customers, allowing chat bots to answer customer service queries