The benefits which MNCs are getting from AI/ML and the enhancement of AI provided to their products and make them the top notch companies of this generation.
What is Artificial intelligence ❓
Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.
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.
ARTIFICIAL INTELLIGENCE EXAMPLES💡
- Smart assistants (like Siri and Alexa)
- Disease mapping and prediction tools
- Manufacturing and drone robots
- Optimized, personalized healthcare treatment recommendations
- Conversational bots for marketing and customer service
- Robot-advisors for stock trading
- Spam filters on email
- Social media monitoring tools for dangerous content or false news
- Song or TV show recommendations from Spotify and Netflix.
The benefits which MNCs are getting from AI/ML💡
1. Routematic:
One of the most innovative technology enabled transportation brand in India, Routematic is aimed at providing a simplified, robust and cost-effective transportation system for corporate employees. The company offers a combination of 2 services - Routematic Fleet Service and Transport Automation Software. As a SaaS product, it offers automation software complimentary to its fleet, which helps clients plan, optimize and monitor their employee transport operations.
2. Aurelius
We have been instrumental in developing consultative in-sourcing solutions which can enable organizations to streamline their operational procedures and business models. The company develops and customizes training programs focusing on the application side of technology. These programs are delivered Real-Time and Virtual with cloud-hosted labs based on the diverse requirements of companies.
3. Roadcast
A GPS-based real-time asset tracking, management and monitoring platform that allows businesses, which deal in logistics, transport and home delivery services, to track shipments/vehicles in real-time and tabulates data such as distance, time and routes. The platform helps in Live Location Tracking, Task Management, Attendance Management, Extensive Reporting, Customizable platform, and is suitable for any type of business.
4. Intuition
This Bengaluru-based startup which develops point-of-sale (POS) and billing systems using AI and ML, has collaborated with Lantern Pharma, a biopharmaceutical company which uses precision oncology to treat cancer and its related diseases. With cancer being harder to detect and treat at initial stages, Lantern aims to alleviate this problem using its advanced genomics and AI for improved drug development. Intuition Systems will work with Lantern's team to help with AI, big data, cloud services and infrastructure to support drug development and biomarker identification.
5. Customer Success Box
A B2B SaaS customer success platform backed with $1 million in venture funding, Customer Success Box is aimed at delivering proactive customer success. As the startup believes, customer churn is the biggest blocker of growth and it's like trying to fill up a leaky bucket. Such businesses cannot continue to operate with the old reactive support model, this is where CSB comes into the picture.
Example:
How AI and data strategy helps BNY Mellon in the transformation journey.
Bhargavi Nuvvula, Head-Corporate Technology, BNY Mellon, in a conversation with ETCIO, talks about the technology initiatives that helped BNY Mellon meet business needs. Also, Nuvvula explains how AI and DataOps is integrated into the roadmap for real time benefits and continuous improvement.
Data simplification and AI help in digital transformation. In our finance and procurement groups, we deal a lot with invoice processing and contract reviews where we have possibilities to apply Intelligent Character Recognition (ICR), basic robotic process automation and machine learning capabilities. We have started embracing these next gen digital transformation techniques six months back and we will start seeing major transformation in the coming months.
AI and ML are really the cornerstones of most of our systems featuring financial intelligence. They are being used in regulatory data, predictive analytics for risk assessments, to identify intraday peak liquidity demands, to detect anomalies in cash flows – to name a few. All of these AI systems involve massive data mining and continuous model improvements. We are on a continuous improvement journey here with a lot more possibilities to harness.
As far as blockchain is concerned, I believe that the true value of a technology like blockchain comes when it has wider adoption. The biggest impediment for blockchain adoption is the lack of industry-wide momentum among integrating partners in the ecosystem.
The enhancement of AI provided to their products and make them the top notch companies of this generation💡
1. Yelp — Image Curation at Scale
Few things compare to trying out a new restaurant then going online to complain about it afterwards. This is among the many reasons why Yelp is so popular (and useful).
While Yelp might not seem to be a tech company at first glance, Yelp is leveraging machine learning to improve users’ experience.
Since images are almost as vital to Yelp as user reviews themselves, it should come as little surprise that Yelp is always trying to improve how it handles image processing.
This is why Yelp turned to machine learning a couple of years ago when it first implemented its picture classification technology. Yelp’s machine learning algorithms help the company’s human staff to compile, categorize, and label images more efficiently — no small feat when you’re dealing with tens of millions of photos.
2. Baidu — The Future of Voice Search
Google isn’t the only search giant that’s branching out into machine learning. Chinese search engine Baidu is also investing heavily in the applications of AI.
A simplified five-step diagram illustrating the key stages of
a natural language processing system
One of the most interesting (and disconcerting) developments at Baidu’s R&D lab is what the company calls Deep Voice, a deep neural network that can generate entirely synthetic human voices that are very difficult to distinguish from genuine human speech. The network can “learn” the unique subtleties in the cadence, accent, pronunciation and pitch to create eerily accurate recreations of speakers’ voices.
Far from an idle experiment, Deep Voice 2 — the latest iteration of the Deep Voice technology — promises to have a lasting impact on natural language processing, the underlying technology behind voice search and voice pattern recognition systems. This could have major implications for voice search applications, as well as dozens of other potential uses, such as real-time translation and biometric security.
3.Starbucks
With more than 90 million transactions a week in 25000 stores globally, Starbucks uses Machine Learning and big data analytics to help direct marketing, business decisions, and sales. By launching its mobile application and reward program they collected and analyzed their customer’s buying habits. The users themselves have created the data by defining where, what, and when they buy coffee.
Starbucks gathers this information about their customer’s buying habits. So that even when the customer visits an offline store their system is able to identify their preferences through their smartphone. In addition to this, the app can also suggest new treats that might go with the drinks they ordered.
All this is powered by Starbuck’s Digital Flywheel Program. It is a cloud-based Artificial Intelligence engine that recommends food and drinks options to the customers who are not aware but want to try something new.
The technology is so sophisticated that the recommendations will change according to the weather on that particular day, or if it is a holiday or a weekday, or at what location you are.
4.Amazon
AI & ML are changing every aspect of Amazon’s Business, from its products, warehouse, to its Echo smart speaker.
Since its earliest days Amazon has used Machine Learning to come up with product recommendations based on the products that the users have already liked. The technology behind those systems has been updated every now and then to make its functionality much better.
These days these recommendations have become more dynamics all thanks to ML.
Another Amazon powered product is Alexa, voice assistant, she gives Amazon Web Service users access to cloud based tools, allowing shoppers to grab items and walk immediately out of Amazon Go stores, guide robots carrying products directly to the fulfillment center, and a lot more.
One of the major reasons why Amazon has risen to a near trillion dollar company is because of its Amazon Web Services, which a cloud storage and server provider. AWS has become a cloud storage standard for a lot of companies and this most because these companies want access to the same technology that powers Amazon’s Alexa, Amazon Prime Videos, and Amazon.com.
AWS is being used across a lot of industries — retail, fashion, entertainment, real estate, health care, and more. Their customers have a variety of AI competence. Some of them are experts having a PhD in Machine Learning, and some of them are developers. Amazon has tailored its Machine Learning and Artificial Intelligence services to match the needs of their customers.
5.American Express
Data Volumes at American Express was not only increasing, but also was changing a lot. More and more of their customers were doing businesses via mobile phones. American Express has seen with the access to big data and ML they can develop models that can help them learn about customer behavior, fraud case detection, new customer acquisition, and recommendation for better customer experience.
In terms of fraud detection American Express have started keeping excellent track record, including their online business transaction using Machine Learning. To do this Machine Learning requires a lot of data sources that will include card membership, merchant information, and spending details.
For new customer acquisition, Amex has up their online presence and website model. Lately they only used to receive leads from their Email Campaigns. But as of now, their online engagement has risen by 40%.
One of the most favorite parts of using AI & ML is recommendations. When an Amex user gives permission to track their data, ML can monitor their history and give out recommendations accordingly.
