Mediaboss Marketing

AI has become intertwined with every aspect of our lives. Each one of us is currently using this technology in one form or the other. From personal digital assistants like Siri, google assistant, Alexa, to self-driving cars, its being used very widely.

The use is increasing on a daily basis in fast growing sectors such as healthcare, finance, e-commerce, and manufacturing.

Also, businesses like Swiggy and Zomato, which have invested heavily in AI over the past couple of years, have witnessed the power of technology to both sustain and increase growth. This has steered the discussion towards AIs potential for other companies in India.

According to a report by Accenture, its expected that AI has the potential to make up 15% of Indias current gross value in 2035 or US$957 bn.

In the coming years, AI will transform the way we live and work.

With increasing demand for AI technology, investor interest in AI stocks has also increased.

Heres the list of top Indian companies working on AI in the Indian stock market.

1. Coforge

Coforge is an IT services company providing end-to-end software solutions and services.

It is among the top-20 Indian software exporters.

The company was formerly known as NIIT Technologies and was incorporated in April 2003.

It provides AI-based digital business assistants, deep learning, machine learning, multi-currency, multi-lingual, multi-channel experience, image recognition, robotic process automation (RPA), natural language processing (NLP), and workflow automation.

In the past, the company has made a few acquisitions to increase revenue and enhance geographical and customer presence.

In April 2021, Coforge completed its strategic investment in SLK Global Solutions. SLK Global has deep domain expertise in the banking and insurance segments in North America. It enjoys multiple long-standing and scalable relationships with marquee clients with strong growth potential.

Over the span of five years, the company has given a return of 1,202%. Currently, shares of Coforge are trading at 5,136 per share.

2. Happiest Minds Technologies

Happiest Minds is an IT consulting and services firm that was founded in 2011.

The company works on disruptive technologies such as artificial intelligence, cloud, internet of things (IoT), blockchain, robotics/drones, virtual reality, and other services.

Artificial intelligence is used by the firm for language processing, picture analytics, video analytics, and upcoming technologies such as AR and VR.

In addition, the company assists organisations in using robots using AI, leading to time and cost savings.

In September 2020, the firm was listed on the stock exchange. Its one of the most popular Indian artificial intelligence stocks.

Ashok Soota the executive chairman of the company is the main promoter and was earlier founding Chairman & MD of Mindtree. Prior to Mindtree, he led Wipros IT business for fifteen years.

Since its listing, the company has managed to give a return of 290.8%. Happiest Minds shares are trading at 1,445 on the BSE.

3. Saksoft

Saksoft is a leading provider of information management solutions to successful companies around the world.

The company is a mid-sized IT company and provides end-to-end business solutions that leverage technology and enables their clients to enhance business performance.

It mainly focuses on getting transformations through efficiency, productivity, enhanced customer decisions, and service innovations by increasing the combination of AI and automation.

Saksoft gives a boost to digital transformation and applies intelligent automation to solve major business problems with the assistance of modern technology like IoT, AI, machine learning, and automation.

The company has delivered good profit growth of 20.1% compound annual growth rate (CAGR) over last 5 years. Saksoft shares are trading at 913 on the BSE.

4. Tata Elxsi

Founded in 1989, Tata Elxsi is a part of the Tata Group and performs in the midcap range in the stock market.

Today Tata Elxsi is one of the leading providers of design and technology services in various industries. These include automotive, broadcasting, communication, healthcare, and transportation.

When it comes to AI, the company has had success in various fields like self-driving cars, video analytics solutions etc.

Tata Elxsi Artificial Intelligence Centre of Excellence addresses the increasing demand for intelligent systems. It allows its customers to use cloud-based integrated data analytics frameworks that feature patent-pending technology to get actionable insights and outstanding returns.

On the financial front, the company has performed well over the last few quarters. It has had a compounded profit growth of 19% for the last 5 years.

In the past five years, stock has provided 535% return compared to Nifty IT that returned 95% returns to the investors.

5. Persistent Systems

Persistent Systems offers a secure and scalable mobile networking capability based on its cutting-edge Wave Relay MANET technology.

Persistents products provide a total solution consisting of voice, video, and situational awareness to mobile users with no reliance on fixed infrastructure.

Also, the company has developed machine learning and AI solutions that help companies at every stage of their AI and machine learning development.

It uses AI to help companies improve and scale their operations, prioritise cases, and designs platform architecture.

Financially the company has performed well. It has achieved a compounded profit growth of 10% and sales growth of 13% over the last five years.

In the last five years, the stock gave returns of 462%. Currently, shares of Persistent Systems are trading at 3,479 per share.

Apart from the above, heres the list of more AI-based stocks to watch out for in India.

View Full Image

In conclusion

Today, AI is a crucial tool for many businesses and the market for the technology is growing quickly in India.

From online shopping to the data used for scholastic tasks, AI has become an integral part of human life.

Also, many Indian start-ups are expanding and developing AI solutions in education, health, financial services, and other fields.

For the last few years, it has been attracting numerous companies to adapt to the trend, driving investments towards them, due to its increasing demand in the present and future.

Investing in digital technologies can create huge revenue in the coming years.

If youre thinking about buying artificial intelligence stocks, you should look out for companies that are focused on AI businesses in India with excellent technical and business fundamentals, minimal debt, and are available at attractive valuations.

Happy Investing!

(This article is syndicated from Equitymaster.com)

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Five Indian companies that are leading the AI race - Mint

Cybercriminals are using artificial intelligence (AI) to evolve the sophistication of attacks at a rapid pace. In response, an increasing number of organisations are also adopting the technology as part of their cybersecurity strategies. According to research conducted in Mimecasts State of Email Security Report 2021, 39 per cent of organisations are utilising AI to bolster their email defences.

Although were still in the early phases of these technologies and their application to cybersecurity, this is a rising trend. Businesses using advanced technologies such as AI and layered email defences, while also regularly training their employees in attack-resistant behaviours, will be in the best possible position to sidestep future attacks and recover quickly.

Mimecast is integrating AI capabilities to help halt some of cybersecuritys most pervasive threats. Take the use of tracking pixels in emails, for example, which both BBC and ZDNet have called endemic. Spy trackers embedded in emails have become ubiquitous often by marketers but also, increasingly, by cybercriminals looking to gather information to weaponise highly targeted business email compromise attacks.

Mimecasts CyberGraph uses machine learning, a subset of AI, to block these hard-to-detect email threats, thus limiting reconnaissance and mitigating human error. CyberGraph disarms embedded trackers and uses machine learning and identity graph technologies to detect anomalous malicious behaviour. Because the AI is continually learning, it requires no configuration, thus lessening the burden on IT teams and reducing the likelihood of unsafe misconfiguration. Plus, as an add-on to Mimecast Email Security, CyberGraph offers differentiated capability integrated into an existing secure email gateway, streamlining your email security strategy.

AI is here, and here to stay. Although its use is not a silver bullet, theres a strong case for it in the future of cybersecurity. Mimecast CyberGraph combines with many other layers of protection. It embeds colour-coded warning banners in emails to highlight detected risks, and it solicits user feedback. This feedback strengthens the machine learning model and can update banners across all similar emails to highlight the new risk levels.

As more cyber resilience strategies begin to adopt AI, it will be vital that people and technology continue to inform one another to provide agile protection against ever-evolving threat landscapes. Innovations such as CyberGraph provide evidence that AI has a promising value proposition in cybersecurity.

Duncan Mills is the senior product marketing manager at Mimecast

This article was originally published in the Summer2021 issue of The Record. To get future issues delivered directly to your inbox, sign up for a free subscription.

Excerpt from:
Artificial intelligence is the future of cybersecurity - Technology Record

Back view of a senior professor talking on a class to large group of students.

AI is becoming strategic for many companies across the world.The technology can be transformative for just about any part of a business.

But AI is not easy to implement.Even top-notch companies have challenges and failures.

So what can be done?Well, one strategy is to provide AI education to the workforce.

If more people are AI literate and can start to participate and contribute to the process, more problemsboth big and smallacross the organization can be tackled, said David Sweenor, who is the Senior Director of Product Marketing at Alteryx.We call this the Democratization of AI and Analytics. A team of 100, 1,000, or 5,000 working on different problems in their areas of expertise certainly will have a bigger impact than if left in the hands of a few.

Just look at Levi Strauss & Co.Last year the company implemented a full portfolio of enterprise training programsfor all employees at all levelsfocused on data and AI for business applications.For example, there is the Machine Learning Bootcamp, which is an eight-week program for learning Python coding, neural networks and machine learningwith an emphasis on real-world scenarios.

Our goal is to democratize this skill set and embed data scientists and machine learning practitioners throughout the organization, said Louis DeCesari, who is the Global Head of Data, Analytics, and AI at Levi Strauss & Co.In order to achieve our vision of becoming the worlds best digital apparel company, we need to integrate digital into all areas of the enterprise.

Granted, corporate training programs can easily become a waste.This is especially the case when there is not enough buy-in at the senior levels of management.

It is also important to have a training program that is more than just a bunch of lectures.You need to have outcomes-based training, said Kathleen Featheringham, who is the Director of Artificial Intelligence Strategy at Booz Allen.Focus on how AI can be used to push forward the mission of the organization, not just training for the sake of learning about AI. Also, there should be roles-based training.There is no one-size-fits-all approach to training, and different personas within an organization will have different training needs.

AI training can definitely be daunting because of the many topics and the complex concepts.In fact, it might be better to start with basic topics.

A statistics course can be very helpful, said Wilson Pang, who is the Chief Technology Officer at Appen.This will help employees understand how to interpret data and how to make sense of data. It will equip the company to make data driven decisions.

There also should be coverage of how AI can go off the rails.There needs to be training on ethics, said Aswini Thota, who is a Principal Data Scientist at Bose Corporation.Bad and biased data only exacerbate the issues with AI systems.

For the most part, effective AI is a team sport.So it should really involve everyone in an organization.

The acceleration of AI adoption is inescapablemost of us experience AI on a daily basis whether we realize it or not, said Alex Spinelli, who is the Chief Technology Officer at LivePerson.The more companies educate employees about AI, the more opportunities theyll provide to help them stay up-to-date as the economy increasingly depends on AI-inflected roles. At the same time, nurturing a workforce thats ahead of the curve when it comes to understanding and managing AI will be invaluable to driving the companys overall efficiency and productivity.

Tom (@ttaulli) is an advisor/board member to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction, The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems and Implementing AI Systems: Transform Your Business in 6 Steps. He also has developed various online courses, such as for the COBOL.

Read more from the original source:
Artificial Intelligence: Should You Teach It To Your Employees? - Forbes

As soon as Tom Smith got his hands on Codex a new artificial intelligence technology that writes its own computer programs he gave it a job interview.

He asked if it could tackle the coding challenges that programmers often face when interviewing for big-money jobs at Silicon Valley companies like Google and Facebook. Could it write a program that replaces all the spaces in a sentence with dashes? Even better, could it write one that identifies invalid ZIP codes?

It did both instantly, before completing several other tasks. These are problems that would be tough for a lot of humans to solve, myself included, and it would type out the response in two seconds, said Mr. Smith, a seasoned programmer who oversees an A.I. start-up called Gado Images. It was spooky to watch.

Codex seemed like a technology that would soon replace human workers. As Mr. Smith continued testing the system, he realized that its skills extended well beyond a knack for answering canned interview questions. It could even translate from one programming language to another.

Yet after several weeks working with this new technology, Mr. Smith believes it poses no threat to professional coders. In fact, like many other experts, he sees it as a tool that will end up boosting human productivity. It may even help a whole new generation of people learn the art of computers, by showing them how to write simple pieces of code, almost like a personal tutor.

This is a tool that can make a coders life a lot easier, Mr. Smith said.

About four years ago, researchers at labs like OpenAI started designing neural networks that analyzed enormous amounts of prose, including thousands of digital books, Wikipedia articles and all sorts of other text posted to the internet.

By pinpointing patterns in all that text, the networks learned to predict the next word in a sequence. When someone typed a few words into these universal language models, they could complete the thought with entire paragraphs. In this way, one system an OpenAI creation called GPT-3 could write its own Twitter posts, speeches, poetry and news articles.

Much to the surprise of even the researchers who built the system, it could even write its own computer programs, though they were short and simple. Apparently, it had learned from an untold number of programs posted to the internet. So OpenAI went a step further, training a new system Codex on an enormous array of both prose and code.

The result is a system that understands both prose and code to a point. You can ask, in plain English, for snow falling on a black background, and it will give you code that creates a virtual snowstorm. If you ask for a blue bouncing ball, it will give you that, too.

You can tell it to do something, and it will do it, said Ania Kubow, another programmer who has used the technology.

Codex can generate programs in 12 computer languages and even translate between them. But it often makes mistakes, and though its skills are impressive, it cant reason like a human. It can recognize or mimic what it has seen in the past, but it is not nimble enough to think on its own.

Sometimes, the programs generated by Codex do not run. Or they contain security flaws. Or they come nowhere close to what you want them to do. OpenAI estimates that Codex produces the right code 37 percent of the time.

When Mr. Smith used the system as part of a beta test program this summer, the code it produced was impressive. But sometimes, it worked only if he made a tiny change, like tweaking a command to suit his particular software setup or adding a digital code needed for access to the internet service it was trying to query.

In other words, Codex was truly useful only to an experienced programmer.

But it could help programmers do their everyday work a lot faster. It could help them find the basic building blocks they needed or point them toward new ideas. Using the technology, GitHub, a popular online service for programmers, now offers Copilot, a tool that suggests your next line of code, much the way autocomplete tools suggest the next word when you type texts or emails.

It is a way of getting code written without having to write as much code, said Jeremy Howard, who founded the artificial intelligence lab Fast.ai and helped create the language technology that OpenAIs work is based on. It is not always correct, but it is just close enough.

Mr. Howard and others believe Codex could also help novices learn to code. It is particularly good at generating simple programs from brief English descriptions. And it works in the other direction, too, by explaining complex code in plain English. Some, including Joel Hellermark, an entrepreneur in Sweden, are already trying to transform the system into a teaching tool.

The rest of the A.I. landscape looks similar. Robots are increasingly powerful. So are chatbots designed for online conversation. DeepMind, an A.I. lab in London, recently built a system that instantly identifies the shape of proteins in the human body, which is a key part of designing new medicines and vaccines. That task once took scientists days or even years. But those systems replace only a small part of what human experts can do.

In the few areas where new machines can instantly replace workers, they are typically in jobs the market is slow to fill. Robots, for instance, are increasingly useful inside shipping centers, which are expanding and struggling to find the workers needed to keep pace.

With his start-up, Gado Images, Mr. Smith set out to build a system that could automatically sort through the photo archives of newspapers and libraries, resurfacing forgotten images, automatically writing captions and tags and sharing the photos with other publications and businesses. But the technology could handle only part of the job.

It could sift through a vast photo archive faster than humans, identifying the kinds of images that might be useful and taking a stab at captions. But finding the best and most important photos and properly tagging them still required a seasoned archivist.

We thought these tools were going to completely remove the need for humans, but what we learned after many years was that this wasnt really possible you still needed a skilled human to review the output, Mr. Smith said. The technology gets things wrong. And it can be biased. You still need a person to review what it has done and decide what is good and what is not.

Codex extends what a machine can do, but it is another indication that the technology works best with humans at the controls.

A.I. is not playing out like anyone expected, said Greg Brockman, the chief technology officer of OpenAI. It felt like it was going to do this job and that job, and everyone was trying to figure out which one would go first. Instead, it is replacing no jobs. But it is taking away the drudge work from all of them at once.

Link:
A.I. Can Now Write Its Own Computer Code. Thats Good News for Humans. - The New York Times

The AAMC submitted a letter to the White House Office of Science and Technology Policy (OSTP) and the National Science Foundation (NSF) on Sept. 1 in response to a request for information (RFI) geared toward developing a shared, national artificial intelligence (AI) research infrastructure that is referred to as the National Artificial Intelligence Research Resource (NAIRR).

The RFI will inform the work of the NAIRR Task Force, which has been directed by Congress to develop a first-of-its-kind AI infrastructure that provides AI researchers and students across scientific disciplines with access to computational resources, high-quality data, educational tools, and user support.

In its comments, the AAMC expressed strong support for Congress prioritization of AI, which has tremendous potential to advance human health and usher in a new era of biomedicine. The AAMC also commended the aspirations of the OSTP and the NSF to develop an inclusive AI infrastructure that allows all of America's diverse AI researchers to fully participate in exploring innovative ideas for advancing AI, including communities, institutions, and regions that have been traditionally underserved.

The letter outlined strategies on how the NAIRR should reinforce principles of ethical and responsible research and development of AI. In particular, the AAMC underscored the necessity of building a NAIRR that identifies and addresses systemic inequities at the interface of AI and biomedicine, mitigates bias by promoting representative datasets and algorithms, provides users with a data management and sharing plan that promotes community engagement and transparency, and fosters a diverse AI workforce and leadership.

Given the vast amounts of data, industries, and applications that will converge with the NAIRR, the AAMC also noted the importance of a multisector approach for identifying, researching, and mitigating bias, discrimination, health inequities, and social determinants of health all components that currently preclude the formation of an equitable AI framework that benefits all communities equally.

Finally, the AAMC recommended that the NAIRR partner with diverse communities in the development of this framework, thereby culminating a diverse expertise and fostering community trust. On Aug. 18, the OSTP and the NSF extended the RFIs public comment period by one month to Oct. 1, providing further opportunity for researchers and academic institutions to respond.

Originally posted here:
AAMC Comments on National Artificial Intelligence Initiative - AAMC