What's next for AI: Top 4 innovations in machine learning – InfotechLead.com

The advancement of artificial intelligence (AI) has surpassed expectations of the past. AI is a branch of computing that deals with how a machine can display human intelligent behavior in itself.
Machine Learning in business
One AI subset, machine learning (ML), is now used in many technologies across many industries. ML uses a process where a computer model is fed numerous pieces of historical data in order to predict and classify new data.
Because ML requires a lot of processing power, resources and data, developers have come up with a myriad of tools to help themselves lighten up their workload, increase efficiency, and reduce the time it takes to ship and deploy business-ready software. One of these tools is the AI operating system cnvrg.io.
Information technology (IT) firms’ hard work has paid off because now, the world is gaining benefits from a number of ML innovations.
Machine Learning Innovations Today
Here are the top four:
Chatbots
Customer service, technical support, and inbound sales. These are the realms of the two types of chatbots. How consumers interact with them is through messenger apps, text apps and website pop-up chat boxes (e.g., the widget you can toggle, usually placed on the lower right-hand side of a brand’s webpage). The first type is a rule-based bot. It answers queries rigidly, and is very simplistic.
On the other hand, the AI chatbot is trained with thousands—sometimes millions—of text-based conversation snippets relevant to the company’s products and policies. Consumer support is more efficient and relatively more effective with AI chatbots handling the queries.
But not always. Hence, companies would set up their customer experience workflow in such a way that the bot would try to help the customer first, and have them transfer that customer to a human agent, if the problem or question is still unresolved.
Speech Recognition
Speech recognition is how computers can convert human speech into usable text. Machine learning has been used to train it using thousands or millions of hours of audio recordings. Speech recognition has been helping the deaf and hard-of-hearing community through captioning services.
Business professionals are using the technology for a number of tasks. Authors, secretaries, bloggers and certain transcription roles use it for hands-free dictation. There is no need for typing because the software does it automatically (called ‘voice writing’), and usually with minimal edits. Because of this feature, it saves a considerable amount of time.
But perhaps the most evident use of speech recognition is through smartphones and home assistants. Using these tools, people are now used to making searches by speaking to their devices (e.g., ‘find the nearest bus stop’, or ‘what’s the weather like today?’). They can also issue voice commands (e.g., ‘call Mom’, or ‘schedule a meeting at 8 pm’).
Through a smaller branch of ML called ‘deep learning’, speech recognition technology can now process human speech at about 95 percent accuracy compared to about 20 percent a few decades ago.
Image Recognition
Image recognition aims to help computers process images and give useful output based on them. ML helps to train image recognition models to accurately classify and identify different objects based on different presentations of them in pictures (or videos).
One of the meaningful uses of image recognition is the detection of certain types of cancers, especially the external ones, like melanoma (a skin cancer). One of the ambitious, perhaps, is safer driverless cars. These vehicles not only use image recognition, but also other AI components. For now, these two technologies are in the works.
Currently, people appreciate image recognition through the use of their camera phones. Fun filter apps and facial recognition in the camera (for feature enhancements during picture-taking and shooting videos) can brighten your hangout times with your friends. Make your and your friends’ faces look like zombies, fire-breathing dragons, older, younger, cartoon-like, and more. You can make your moments hilarious through AI and ML.
Recommendation Engines
Recommendation engines are ubiquitous, but people don’t usually know this. Every time they make a search, listen to a song, watch a video, or purchase a product online, they participate in that company’s gathering of user data utilized to feed recommendation engines. These are ML models responsible for the suggestions people receive on their search engines, video- and music-streaming apps, social media, and e-commerce browsers.
Recommendation systems enhance the user experience because customers see a wider selection of the products, videos, or music they like. At the same time, companies using these systems benefit from increased ROI (return on investment) due to more purchases and livelier user engagement.
What’s Next In AI:
In the near future, the speculative route for AI is toward automation. This is quite apparent with the number of patents filed by numerous tech and financial giants from some of the developed countries today. These firms are smartphone manufacturers, software development companies, financial corporations, and electronics and robotics pioneers.
Takeaway
It’s hard to say how exactly the different cultures will react to this next wave of deeper digitalization (extensive use of electronic devices in everyday living). AI and the computing industry are active areas of research (and change). One hopeful outcome is that as the IT sector and related industries come up with new and exciting tech, the rest of the world will study it, learn from it, and adapt.
Baburajan Kizhakedath


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