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How is AI Shaping the Indian Job Market?

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How is AI Shaping the Indian Job Market?

Currently, startups focusing on Artificial Intelligence, Machine Learning and Deep Learning are on the rise in India. According to a recent report by AI Task Force, there are 750 startups in India that are actively working to build a robust AI ecosystem in India. Initiatives to promote AI by Indian government include establishment of NITI Aayog, the policy think tank of India, and Digital India, which is a campaign to improve technological infrastructure of the country.

65% of participants of a PwC survey believed that AI will have a grave impact on the employment scenario of India. Interestingly, the majority of participants of this survey were of the opinion that AI will allow employees to do more value-added tasks as it will take up all the daily mundane tasks.

Deep Learning and AI using Python

Job market outlook:

‘’We expect a 60 per cent increase in demand for AI and machine learning specialists in 2018’’, said BN Thammaiah, Managing Director, Kelly Services India. Belong, a Bengaluru-based outbound hiring firm startup, shares the same view, stating that the demand for AI professionals has risen by leaps and bounds due to the widespread adoption of AI and automation technologies across companies. Consulting industry leader, Accenture, expects AI to add $957 to India’s GDP by 2035.

Jump in demand:

Only 4 percent of AI professionals have work experience in core domains, like deep learning and neural networks.

For every 1000 jobs in the field of Deep learning, there are approximately 530 professionals available. Similarly, for every 1000 jobs in the field of Neuro-linguistic Programming (NLP), there are only 710 professionals available.

The lack of core data science disciplines in engineering institutes across the country is responsible for the disparity between demand and supply of AI professionals. Only a few selected institutes, like IITs and IISc, have ML programs in their curriculum. The active AI researchers in India are a meager 386 in number.

AI hotspots in India:

AI-work hubs in India are Bengaluru, New Delhi and Mumbai. IBM, Microsoft, Flipkart and Amazon are carrying out good research work in AI. Companies like Adobe, Accenture, Amazon, JP Morgan, SAP, L&T Infotech, Nvidia, Intel and Wipro are actively hiring AI professionals. The main sectors fostering AI employment are e-commerce, banking and finance. Kamal Karanath, Co-founder of Xpheno, a recruitment company, said that there would be a huge demand for AI engineers in these sectors in the next 5 years. AI-powered technology boosts efficiency and security of Indian banking and financial sector.

India Inc is endeavoring to upskill workers in subjects like machine learning, cloud computing and big data. In efforts to nurture talent and obtain solutions from vertical focused AI startups, which are developing innovative technologies, enterprises have set up many accelerator programs. Flipkart is developing AI products that will boost their business growth.

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A peek into the future of AI:

The Indian government intends to establish research institutes and Centres of Excellence that foster training and skilling in fields like AI, robotics, big data analysis and internet of things. Top engineering schools, like IITs, IIITs and IISc are collaborating with industries to bridge the gap in AI talent, provide targeted solutions and steer growth of the AI industry. Government of India is framing numerous policies to promote industry-academic partnerships.

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How Conversational AI and Chatbots are Revolutionizing Indian banking Industry

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Thanks to the advancements in AI and ML, bank work can now be done with the click of a phone button! Innovations in the field of customer services form an important part of the technology overhaul. The banking sector is making hefty investments on AI technology to simplify user experience and enhance overall performance of financial institutes.

Let’s take a look at how conversational AI and chatbots are revolutionizing the Indian banking industry.

  • Keya by Kotak Mahindra Bank

Keya is the first AI-powered chatbot in Indian banking sector. It is incorporated in Kotak’s phone-banking helpline to improve its long-established interactive voice response (IVR) system.

‘’Voice commands form a significant share of search online. In addition, the nature of the call is changing with customers using voice as an escalation channel. Keya is an intelligent voicebot developed keeping in mind the customers’ changing preference for voice over text. It is built on a technology that understands a customer’s query and steers the conversation to provide a quick and relevant response”, says Puneet Kapoor, Senior Executive Vice President, Kotak Mahindra Bank.

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  • Bank of Baroda chatbot

Akhil Handa, Head of Fintech Initiatives, Bank of Baroda said that their chatbot will manage product-related queries. He believes that the services of the chatbot will result in better customer satisfaction, speedy responses and cost minimization.

  • Citi Union Bank’s Lakshmi Bot

Lakshmi, India’s first humanoid banker is a responsive robot powered by AI. It can converse with customers on more than 125 topics, including balance, interest rates and transactional history.

  • IBM Watson by SBI

Digital platforms of SBI, like SBI inTouch, are utilizing AI-powered bots, such as IBM Watson, to enhance customer experience. SBI stated that modern times will witness the coexistence of men and machines in banks.

  • AI-driven digital initiatives by YES Bank in partnership with Payjo

Payjo is a top AI Banking platform based out of Silicon Valley in California. YES Bank has partnered with Payjo to launch YES Pay Bot, its first Bot using AI, which improves already popular wallet services. The YES Pay wallet service is trusted by more than half-a-million customers.

  • YES TAG chatbot

YES TAG chatbot has been launched by YES Bank and enables transactions through 5 messaging apps. Customers can carry out a wide range of activities, such as check balance, FD details, status of cheque, transfer money, etc. It is currently used in Android and will soon be available on Apple App Store.

  • Digibank

Asia’s largest bank, DBS Bank, has developed Digibank, which is India’s first mobile bank that is ‘chatbot staffed’. It provides real-time solution to banking related issues. This chatbot employs a trained AI platform, called KAI, which is a product of New York startup- Kasisto.

  • Axis Bank launches intelligent chatbot in association with Active.ai

Axis Bank facilitates smart banking with the launch of a chatbot that employs conversational interface to offer interactive mobile banking solutions. This intelligent chatbot was developed in association with Singapore based AI company- Active AI.

  • HDFC Bank launches OnChat in partnership with Niki.ai

To enable smooth ecommerce and banking transactions, HDFC in partnership with Niki.ai has launched a conversational chatbot, called OnChat. It is available on Facebook messenger even to people who aren’t HDFC customers. Users can recharge phone, book cabs and pay utility bills through this chatbot.

  • EVA by HDFC Bank

EVA is exclusively for the customers of HDFC Bank. It is an electronic virtual assistant developed in partnership with Senseforth, an AI startup based in Bengaluru.

  • mPower by YES Bank

mPower is a chatbot for loan products that has been developed by YES Bank in association with Gupshup-a leading bot company. It assists customers on a variety of loan related topics like personal loans, car loans and loan against securities.

In the future, there will be three kinds of bots- speech-based bot, textbots and video chatbots. Conversational bots work in harmony with human employees to enrich customer experience.

Thus, AI-powered technology is the way forward. To be industry-ready in this AI-era, enroll for the Machine Learning course in Gurgaon at Dexlab Analytics. It is a premier Analytics training institute in Delhi.

 

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How Machine Learning and Sensors Help Detect Cyber Threats within Power Distribution Networks

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Machine learning in marriage with cutting edge sensor technology helps alpha geeks detect and assess cyber-physical attacks across power-distribution channels.

How Machine Learning and Sensors Help Detect Cyber Threats within Power Distribution Networks

Today, losing power and imagining a life without technology sounds unreal. It’s more than just an inconvenience. The truth is we rely on electricity much more than we even do realize. Even though you are not a techie or someone belonging from the IT domain, you still stay dependent on electricity and power. They have become the BASICS.

For this reason or more, power companies have initiated a ‘deep dependency’ concept, Smart Grid – it’s an effective and powerful power-distribution structure. It’s originally a power-line internet that harbors exceptional capabilities within.

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Machine Learning and Sensors to Ensure Security to Power Grids

A team of eminent researchers are toiling rigorously to integrate machine-learning algorithms, cybersecurity methodology and commercially-available power-system sensor technology into a security monitoring and analysis framework to support power grids.

The team is at present working on the framework’s architecture for detection of cyber-physical attacks on any power-distribution network. “To do this they are using micro-Phasor Measurement Units (µPMUs) to capture information about the physical state of the power distribution grid,” explains Kathy Kincade, Lawrence Berkeley National Laboratory. “They then combine this data with SCADA (Supervisory Control and Data Acquisition) information to provide real-time feedback about system performance.”

Note: Kathy Kincade published a Lawrence Berkeley National Laboratory press release: Combination of Old and New Yields Novel Power Grid Cybersecurity Tool, which talks elaborately on this issue.

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The notion here is to keep a close watch on the physical behavior of the components within a particular electric grid to understand when devices are under attack, how they are manipulated weirdly. These devices act as a redundant set of measurements that offers veritable ways to monitor everything that’s going within a power distribution grid.

One of the researchers, Sean Peisert (Berkeley Labs) articulates the importance of redundant measurements permitted by implementing both µPMU and SCADA devices. He further says, “Individually it might be possible for an attacker to manipulate what is being represented by any single sensor or source of information, which could lead to damage of the power grid. This approach provides the redundancy and therefore resilience in the view that is available to grid operators.”

System redundancy comes with an additional benefit of distinguishing real attacks from false alarms by comparing µPMU measurements to what the device reports.

An Algorithm for Real-time Reporting

The proud researchers formulated an algorithm in 1954 for their machine learning endeavors. The algorithm aids software in identifying if measurements like active power, reactive power and current magnitude are normal or abnormal by discerning robust changes across the physical environment.

The Last Thoughts

Cyber attacks are becoming increasingly widespread. Every other day, you might find some headlines or tech page news surfacing out, intensifying how cyber attacks are plaguing our lives, digitally. Therefore, it’s high time to learn from the pundits how to work on the issue.

As Peisert concludes, “Using high-resolution sensors in the power-distribution grid and a set of machine-learning algorithms that we developed, in conjunction with a simple model of the distribution grid, our work can be deployed by utilities in their distribution grid to detect cyberattacks and other types of failures,” it stresses on the significance of machine learning algorithms to combat such attacks.

The original article first appeared in – https://www.techrepublic.com/article/power-grid-cybersecurity-tool-uses-machine-learning-and-sensors-to-detect-threats

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How Algorithms Shape Public Discourse and Opinions?

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The rapid evolution of today’s communication mediums has brought about a radical change in how public opinions are framed and public discourse is conducted. In general, the conventional boundary between public and personal communication has somewhat disappeared.

 
How Algorithms Shape Public Discourse and Opinions?
 

Incredible global platforms, like Google allow us all gain access to information in a blink of an eye. In order to do so, they use computer algorithms that weigh “relevance”, but sometimes the standards do not correspond to the expectation of the users.

 

Custom-fit Relevance

Algorithms function distinctly and descriptively. For an instance, the technology alters “relevance” for a user based on what links he or she has clicked in the past. But still, many users think the results are normative (‘higher’ up in Google results). In several cases, Google’s algorithms determine a massive divergence between content quality and “relevance”.

 

Not only that, owing to their encompassing database, Google and Facebook play a strong role in forming public opinions. 57% of German internet users get access to information about social affairs and politics through Google and other social networks. The researchers from Hamburg-based Hans Bredow Institute quoted in 2016, “the formation of public opinion is no longer conceivable without intermediaries, such as Google and Facebook”.

Keep Engaged

The design elements that Google and other intermediaries use are leading to a structural change in public discourse. Today, publishing is a piece of cake. Anyone can publish anything on the web, but everyone might not find an audience – for the latter, decision-making algorithms are needed. They garner the needed attention. They also determine the relevance of each content piece that goes through various social networks, like Facebook and filter the items that should be displayed for each user. In making an individual’s feed attractive, Facebook runs a detailed analysis and determines which content the user and his or her friends’ likes or prefers to hide. Both signals are important to perform a fairly straightforward analysis.

 

Moreover, Facebook deploys signals that users have no idea about, such as the amount of time they take to look into a single entry in the feed. In other areas too, algorithmic decision-making plays a crucial role, like offering help in legal matters or assessing where and when the police officers are on duty.

Diversity Rules

To guarantee a diversity of media in the public, make sure the algorithmic decision-making processes that determine relevance are diverse in the same manner. The digital discourse is supported by the robust algorithms that constantly ranks and personalize content.

 

To instill transparency to algorithmic decision-making methodologies, follow the steps below:

 

  • Back external researchers, and open platforms and their impacts
  • Support diversity among algorithms
  • Develop and maintain a strong code of ethics among developers
  • Educate users about the importance of the mechanisms used to influence public opinions

 

Coupled with strong industry self-regulation and legislative measures, a true and impartial notion of social and political influences on algorithmic ranking is established, which carries the potential to discover and combat dangers early on.

 

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How VC Firms Are Using Machine Learning to Make Robust Investment Decisions

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How VC Firms Are Using Machine Learning to Make Robust Investment Decisions

Venture capital companies find it hard to pool in interesting investment options – the task is laborious and travel-intensive. But, thanks to machine learning and predictive analytics – they have now started to transform the entire procedure of how an investor builds up a portfolio altogether.

Considering the power of AI’s utility in determining the most fabulous startup investments, InReach Ventures co-founder Roberto Bonanzinga has decided to invest $7 million on respective software that deploys machine learning to identify significant European startups to invest capital into. Following its footsteps, several other VC firms have started doing this, already just to thrive in.

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Rightfully so, AI is an incredible tool that is capable enough to filter out all the unnecessary noise and pull up VCs with potential candidates for sound investment. This makes it easier for entrepreneurs to hit the optimal level of funding and appeal to strong VCs.

AI: An Investment Ally

According to a Social Science Research Network Study, there lies an inherent risk with investing on newbie entrepreneurs, and just only 18% tastes success on their feats. Brand new business owners are ambiguous, they need some scrutiny before investment – for that, AI framework is armed with the required tools and information – it can internalize data to easily derive at conclusions and fasten a success rate to a company on the basis of past industry performance, revenue growth, profit ratios and market size.

As a result, entrepreneurs can tweak their pitches and alter company profiles to better tally with AI, and this how they can start:

Get Deeper

Who doesn’t dream of owning a company that’s a market leader?! However, raising such adequate amount of capital becomes the real challenge. The challenge intensifies when budding entrepreneurs need to attract funds.

For such minority-fronted startups, Alice, a formidable AI platform uses data to decide which businesses are worth funding. Entrepreneurs should implement AI platforms, like Alice to take a deeper look into the key metrics to get a larger picture how their startups are staking up to their tailing rivals who received funding and how well they are functioning.

Tracking Investor Trends Helps

Age-old methods of tracking investment trends are things from the past, because AI and machine learning is changing the entire ball-game. A Berlin-based VC firm Fly Venture plans to target European startups in the seed stage and pre-Series A startups and finally closed its first fund at $41 million. It aims to use machine learning to generate deal flow. This type of technology helps entrepreneurs meet the right investors at right time. After keeping a close eye on the market, it’s about time to utilize the AI-sought information to make sure your company is line with what investors are seeking in a veritable startup partner. This will bear more fruits and less frustration.

Never stop evolving

The best thing about AI is that it never stops improving. Constantly, machine learning is on the move – it analyzes information 24/7 so that entrepreneurs gain access to non-stop updates to tweak their businesses, while pitching for investors.

In a nutshell, to have better insights and cleaner access to data, entrepreneurs need to harness the relentless power of AI. The technology isn’t eating away our jobs, instead its bringing a new change in the data-inspired environment. And if you are already working with it, you’ll understand how it’s reshaping and guiding venture capital to startups that AI finds worthwhile.

To grasp emerging trends, newer solutions, robust techniques and real-life case studies, take up Machine Learning Using Python courses from DexLab Analytics. Their Machine Learning Training Gurgaon simply gives an out of the world experience, thus need to be tried on.

 

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How Machine Learning Coupled With Data Science Improves Retail Scenario (Part II)

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This blog is a continuation of the previous blog that talked about how data science is improving retail – through cutting edge machine learning training models. For sure, retail and ecommerce churns out a humongous amount of data, but with no proper tool of analysis, the vast pool of data lies unutilized, unearthed and you end up knowing what you already know.

How Machine Learning Coupled With Data Science Improves Retail Scenario (Part II)

In this blog, we will delve into a few common uses of data science in retail – which demands absolute attention, before we start automating procedures.

Product Recommendations

In a traditional shop setup, retailers would have consultants who would understand customer requirements and use their own judgments to help them find a suitable product. In an online scenario, this would be based on past performance and revenue generation and the sole aim would be to pitch the highest selling product. Not only historical purchases and recent online activity, but consumer’s social media and online sharing would give more idea about their interests, preferences and designer they like.

Sometimes, we also come across ghost clients – clients about whom we have no information, in fact, we don’t even know from where they are browsing. In this case, your recommendations would be based on intuition and might not be 100% accurate.  The deal becomes trickier here. On the other hand, there are clients about whom who know everything – and thus tailor our offerings.

Product Assortment

No wonder, we have to keep products to satisfy our niche customers, but on a wider scale, we have to introspect what stuffs to keep in stock. A proper analysis of our product demands and the kinds of products our clients swear by, we can ascertain what items to restock again and again. Also, we can take a cue or inspiration from our vying competitors, as they are a good source of information for a perfect assortment of products you want to include. A full account of their inventory will enlighten you about a few blind spots you had, and devise how to correct them before it’s too late. 

Pricing

The people will pay whatever rates the market supports. The price of the product is still subject to change, depending on the country of origin, taste and preferences and market scenario. But these are more of a supply side changes, so what about the demand side? Interested customers are keen to buy products even at varying prices, but the products should be truly good enough. The scale also plays an important role in deciding prices. The best pricing decisions take into account data regarding weather, day of purchase, several economic factors, location and more.

Customized branding/marketing

It is mostly curated for large retailers. For example, how about some doing some routine advertising – it applies to both digital and offline branding, though much easier for digital. A monthly newsletter carrying all the needful information about discounts, new product launch and promotions always will keep your customers’ updated about everything that’s going around the company. But, make sure they have some sort of personal touch – personalized marketing helps!

Summary

While the sky is the limit for data science, the blog above sheds light on the benefits of data science and the true impact of having trust on data. After all, it is of no use to keep data and not take advantage of it!

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How Machine Learning Coupled With Data Science Improves Retail Scenario (Part 1)

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The mammoth growth in ecommerce signifies an entire paradigm shift in retail sector. Figures say, ecommerce accounts for $2 trillion dollars in sales and more. Though traversing through both the offline and online market seems a rather challenging task, but when we finally concentrate on each customer and their purchasing manner, it feels easier to break up the analysis into a few different paths.

How Machine Learning Coupled With Data Science Improves Retail Scenario (Part 1)

In this blog, we will take into account a few interesting ways, in which data science increases your sales, online and offline, alike. But before that, understand whom are you selling your products? Hoarding information about your clients is crucial, and of course there are many ways to do this.  Amazon is one of the biggest examples of this. They predict future purchases of customers, based on the past behavior. Companies lose valuable customers if they don’t look at the data with a wider scope and search for insights. But Amazon is definitely not one of them, and their technique is clearly working for them with over $2 billion profits made last year.

The Mechanism Behind

At Amazon, products are shipped even before customers have ordered them. This means, when the products are shipped, there’s no one to receive them. But, does it really matter! The main logic behind such steps is that once the products are taken out of the warehouse and transported to a particular area, they can easily be marketed to other dealers at discounted rates or kept inside the final hub. This is more like a logistic marvel than an ecommerce miracle – but it definitely makes us believe in the concept of forward thinking to lead the change.

 

amazon-distribution

 

The working principle in here is the most innovative concept of machine learning that helps in predicting future client behavior pattern. It works on data to train a formidable model. Training is a notable process of pouring data into the model so that it can employ statistical weights to automatically identify future purchase trends. For example, Mr. A purchases a new item every two or three weeks, so it’s expected that he will make a purchase within that time limit. For this, we don’t have to use data, but just divide it into train and test data. However, this is a very simple example – in reality these trends are juxtaposed with other millions of clients to differentiate clients into numerous cohorts that overlap and vary. Machine learning techniques are used in a plethora of different use cases, like product recommendations, churn predictions, logistics planning and automatic personalized marketing. We will discuss deeply about them in our next blog section.

 

supervised-machine-learning

 

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Make Flexibility Your Bae

While working on data science, it is important to focus on flexibility – the whole structure of data warehouse will start changing once you start trying something new. At times it may seem to be amusing, but on the long run, you will come across several significant insights.

 

 

With all these on point, scoring high on retail is no more a distant dream. Data science and machine learning methods have made everything so easy, and so manageable. To give a robust push to your career in data science, take up data science online training from DexLab Analytics. Apart from data science, they also offer excellent Machine Learning Certification for all data-hungry candidates – go take a look at their course structure.

 

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IoT is Advancing but without Enough Security: What You Need to Do

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IoT is Advancing but without Enough Security: What You Need to Do

IoT is eminent. And a wide number of nerdy IT specialists are scared as hell.

A recent survey pointed that 78% of IT decision makers accepted that they are skeptical if their business lose crucial data enabled by IoT devices. Amongst them, 72% even said the speed at which IoT adoption is gaining ground is worrisome because they are yet to evolve necessary security arrangements.

As the saying goes, there’s no smoke without fire – the recent WannaCry Ransomware is the best example to point out security issues related to IoT– it infringed and crippled Bank of China’s ATM networks and washing machine networks. In another instance, a company that looked after much of the Internet’s domain name structure was brought down by using somewhat 100,000 “malicious endpoints” from IoT devices. All owing to security lapses in the adoption of IoT infrastructure.

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The notion is that IoT is still largely under-secured and poses larger than life security threats and risks as companies are trusting suave IoT devices for some way or the other, as a result jeopardizing their own operational, profitability and safety decisions.

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AI is the Answer!!

AI can come to the rescue, whenever security of IoT is the concerning matter. Proponents defend saying machine learning can easily identify usage patterns and send signals to the system, whenever abnormalities are recorded or even occur. Reason: all the IoT devices are limited in function, so it becomes easier to recognize irregularities.

Again, a combination of everything is never a bad idea. Undoubtedly, AI plays a prominent role in uplifting IoT security, but a comprehensive IoT solution would include an amalgamation of everything, like AI, government regulation and standards.

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Though the ever-so-increasing tech industry is capable of devising an effective solution, but the real deal in here is to perform everything on a breakneck timetable. At present, in between IoT security and IoT adoption, the latter is winning.

FedTechIoTSecurity1

Here’s a set of suggestions that helps in latching on with the IoT without compromising on the security part:

  • Implement an integrated approach – Anytime, more is better. The companies that are relying on IoT should seek to integrate management solutions and welcome a powerful IoT framework to boost smooth data movement and good connectivity that pulls in data into a robust analytical environment, which is albeit more sophisticated and makes room for flawless behavioral analysis, which is automated – “by integrating those components, you can be more confident that what you’ve got from a feed in an IoT environment is more statistically valid,” Chris Moyer, CTO and VP-cybersecurity at DXC said.
  • Choose the perfect IoT devices – Formidable ecosystem and having a series of companions that shows no inhibitions in the manner they share information stems out to be the right IoT devices.
  • Look forward to Edge Devices and IoT Gateways – To counter the lack of security measures, top of the line companies are using Edge Devices and IoT Gateways to bind more impregnable layers of protection between insecure equipments and the internet.
  • Go create standards – From a macro level perspective, you should ensure a 360-degree IoT security for the next few years and that is only possible if you start setting standards in your business as well as in tech from now on.

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AI-Smart Assistants: A New Tech Revolution in the Make

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2018 has begun. And this year is going to witness a mega revolution in the field of technology – the rise of AI-powered digital assistants. Striking improvements in key technologies, like natural language processing and voice recognition are making smart assistants more productive, helping us use electronic devices just by interacting with them.

 
AI-Smart Assistants: A New Tech Revolution in the Make
 

Smart voice assistants are going mainstream. From Apple’s Siri to Google’s Assistant to Samsung’s Bixby, superior digital assistants are on a quest to make our lives easier, while taking us a step closer to a world where each one of us will have our own personal, 24/7 –all-ears AI assistants to fulfill our every wish and command.

Continue reading “AI-Smart Assistants: A New Tech Revolution in the Make”

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