Artificial Intelligence in business: How can it be applied?

Reading Time: 9 minutes

Companies across industries are loaded with information, so it’s challenging to gain insights from the deluge of knowledge into actionable intelligence. That’s where the applications of artificial intelligence in business comes into the picture. This intelligence drives major and minor industries by providing them with the leverage to experiment with new concepts that cater to what the market wants. Gartner, an MNC famous for its “Magic Quadrants” in numerous sectors ranks firms on different metrics and provides a thorough analysis of the present scenario across tech and non-tech industries. Gartner predicts that 40% of all new enterprise applications will be supported by computing technologies. In such a situation, the data science market is certain to witness tremendous growth within the years to come.

There are several areas where Artificial Intelligence in business is being applied: personalised apps, security applications or more popular applications in data analytics within recent times. The opportunities are endless given that businesses and institutions are capturing an increasing amount of data across their processes, due to the proliferation of data across the globe.

What are the different types of analytics?

There are four major applications of data analytics, which can be classified as follows:

1) Prediction: Predictive analytics, as the name suggests, uses machine learning algorithms to sift through the data obtained to predict potential outcomes. Predicting the price of a stock in the stock market based on the growth, profits, news etc, are interesting examples.

2) Prescription: Given a set of conditions, the algorithms give the best set of decisions that can be taken. The efficacy of these algorithms depends on the quality of the data obtained. Popular apps that offer medical advice on the basis of the patient’s inputs is a relevant example. The outcome of these apps is a list of possible illnesses based on the symptoms, with recommendations and precautions to avoid the illness becoming worse. 

3) Diagnosis: Identification of the problem at hand is crucial for many companies. In the instance of profit margins spiralling down for a company, they’ll need a thorough analysis of their operations, sales and marketing data to understand the factors that are influencing the decline so that the company can take steps to correct it.

4) Description: Describing the current market scenario, for instance, is an example of the same. Sentiment analysis gives information about the tonality and image of a company in the market. Finding the factors that lead to the right positioning and marketing is done through descriptive algorithms. For example, ThoughtSpot, a company working in the space of data analytics is working on providing descriptive solutions to marketing bottlenecks that emerging companies face. Identifying the key areas of improvement and providing actionable solutions for the same describe descriptive analytics. 

Check out the main differences between data science, machine learning and artificial intelligence.

How will AI affect business outcomes?

Artificial Intelligence business outcomes

The impact of Artificial Intelligence in business has immense potential, but there’s still a long way to go. Traditional business analytics requires high IT involvement as businesses generate a large number of data points. It is tough for a common man to navigate through a large number of data points. With AI-driven analytics, businesses can automate data preparation and reveal hidden patterns through sensible data discovery and interactive exploration. These technologies are enabling businesses to foresee outcomes, thus letting them act at the right instant when the opportunity strikes. Gartner predicts that language generation and AI capabilities will be a standard feature in 90% of contemporary analytics platforms by 2020 in which a minimum of 50% of the analytics queries will be generated or automated using the existing technologies within the same time-frame.

Instances of AI applications in business

There are various emerging companies that offer data analytics and AI services for a nominal amount. They enhance the company’s business prospects by identifying key areas of action. The user interface and the natural language capabilities of these systems improve the ease with which managers in companies can leverage large amounts of data. 

As stated before, the scope of Artificial Intelligence in business has the potential to be vastly impactful. Results obtained are simplified and additional personalised insights and necessary set of actions and recommendations are captured. Users utilize the services by searching through keywords in the form of queries. For example, a query that reads, “which product has the least profits?” has keywords product, least profits in it. Using these keywords, the algorithms search automatically through the databases and output the results in a visually appealing way. Moreover, autonomous capabilities can now deliver results that the algorithm thinks is going to be useful, based on the user’s interaction with the software. Thus, businesses are leveraging AI tools and techniques, significantly machine-learning algorithms to generate actionable insights. 

How far has machine learning come?

In development for several years, machine-learning algorithms will “learn” over time through the increasing volume of data input, developing the flexibility to detect hidden patterns and generate predictions. Machine learning is associated with the trial and error-driven sub-field of AI with roots in applied mathematical modelling. When we try to intellectually code a real-time system, the complexity with which the world works is way beyond the level that we as humans can conceptualize and convert into a logical set of statements. Therefore, it permits systems to find out and improve following prolonged exposure to information while not being programmed explicitly. Models are “trained” from meticulously crafted ready to use datasets that learn from inputs with expected outputs. 

For instance, traditional code development excels at capturing rules we can categorize explicitly— say, calculating the cost of the premium for insurance. Consider predicting the ideal and most profitable cost of the premium from customers through which the customers attract the most number of consumers and derive the highest profit. The solution to such large-scale problems is beyond the knowledge obtained solely from human experience.  When many parameters are present the complexity of the problem increases multifold, and therefore machine-driven analytics is the smart way to make judgments. By inferring significant relations directly from the data, machine-learning techniques usually attain human or above-human performances on new examples not seen throughout the training period, effectively working out a solution for hand-coded rules. Machine learning permits firms to use code to deal with issues that traditional coding techniques couldn’t. 

“Machine learning isn’t some obscure magic that happens with data scientists in a backroom, it’s pervasive in how we use and consume data,” says Rich Clayton, VP of product strategy for Oracle Analytics. “Instead of looking at just enough data to support a hypothesis, machine learning does inductive discovery— it looks at what the data is telling me, even things I didn’t think about”. The increase in the use of machine learning across verticals is becoming increasingly pervasive and an integral part of the digital transformation. India is a major example of an efficient and smooth implementation of digital technologies in every aspect of bureaucracy. 

Increasing adoption of AI and ML by businesses

artificial intelligence business adoption

Over the past few years, there has been an increase in the adoption of Artificial Intelligence in business. Deloitte expects giant and medium-sized businesses to “intensify their use of machine learning” in 2020. The number of experiments and pilot programs using machine learning algorithms can double, and this accelerated adoption is going to be a tipping point for the entire global economy. For example, Flipkart has come up with its range of products ranging from washing machines to air conditioners. The objective of this venture is to create products which have the best features from products across multiple companies product lines. Hence, providing it in one single product enhances the customer experience. How did they get the data in the first place? It is through customer feedback and reviews published on their website.

In retail, firms are investing in machine learning and autonomous analytics to derive user insights that equip retailers to deliver targeted and contextually relevant offers and client experiences. Many financial companies are investing in machine learning in a variety of host of applications from algorithmic stock trading and customer service chatbots, to real-time fraud prevention. Health-care and biotech firms are driving innovation by coming up with new drugs, new therapies and new ways to automate complex surgeries through the involvement of robotics. 

Digital transformation needs the employment of both adaptive and autonomous analytics. This implies using machine learning to power the business analytics chain, which starts with discovery, moves to the organization and augmenting of information, then to analysis, modelling, and eventually prediction. ML capabilities have traditionally been out of reach, due to price, availability of talent, and out-of-date infrastructures. To drive large scale digital transformation, enterprises need an optimized technology stack— from hardware and storage to efficient and intelligent algorithms and applications that leverage machine learning techniques—all designed to run analytics and AI applications seamlessly in any setting. Trendy analytics platforms infused with machine learning and natural language processing eliminate the requirement for a lot of of the up-front technical work, automating information preparation and facilitating sensible information discovery via interactive visual exploration. With cloud-based intelligence, autonomous analytics and the consequent advancement of ML is comparatively inexpensive and also allows high-capacity in terms of processing power, storage, networking, and new memory technologies. Businesses are already experiencing a huge leap within the rate of change in the quality offered by these data-driven insights.

What technologies are propping up the rise of AI and ML?

The role of IoT and 5G is going to be huge, as the process of data collection is going to grow both in terms of speed and size. 5G is being prepared to be ML ready and thus high bandwidth interfaces allow the handling of large ML model execution and thus lead to real-time analytics accessible from the simplest of an internet-connected edge device.

Companies with more than 100 years’ worth of data which is well documented in the form of papers, can utilize the services that autonomous analytics systems provide. It is ideal for non-technical industries to invest in such services as there is no up-front cost, and neither do they have to employ anyone to do the same. The existing employees can use these interfaces and come up with insights. This is what the data revolution looks like. An oil company decided to adopt a cloud-based, autonomous analytics system. The company aggregated its diverse data sources and obtained insights in weeks, doing so otherwise would have taken years— if employees had even thought of asking various questions. 

Another example is of Tesla Motors. The self-driving cars project wing under the company has inserted several cameras in each car, and thus have a huge dataset built. What is the relevance of the size of a dataset? The size matters, especially because, the probability of a larger dataset capturing all the corner cases will be higher when compared to a smaller one. The corner cases, in the scenario of a self-driving car, can be a crucial one, especially because of the consequences that such applications shall have. The idea of safety, feasibility, and scalability are the key bottlenecks all these platforms need to consider and master. 

Investment optimization is a key aspect these platforms concentrate on. The objective is to maximize profits and thus, all companies need to be future-ready, that is, focus on their scaling capabilities in terms of the advances in the field. 

How can companies leverage AI?

Artificial Intelligence companies use

There’s no one-size-fits-all approach for applying artificial intelligence in business, but here is a normative approach:

  • Formulate a holistic and long-term data strategy, not just a tactical road map for disparate projects. Doing so requires an assessment of business objectives along with a clear definition of success criteria and the data maturity of the enterprise. Evaluate such variables as existing legacy applications, workload placement, agility and time to market, security, compliance, capacity, legal/regulatory requirements, workforce skills, and maturity of offerings from cloud solutions providers.
  • Identify use cases with a high return on investment (RoI) that are tied to business goals and objectives, which will help demonstrate initial proof points and benefits. An important part of this exercise is to identify the foundational data that will drive such use cases.
  • Capture key lessons learned and make them available for future endeavours. 
  • Allocate enough resources for change management, which is perhaps the most difficult aspect of supporting a data-driven culture. The prescriptive actions might be counterintuitive and thus making data governance and lineage a top priority are critical to take decisive actions.
  • Depending on platforms that provide analytical services can only get us so far. The remaining work that needs to be done might need specialized requirements and thus investing in data science research and having a Research and Development team would help the growth of the company. 

Challenges in AI adoption for companies

Data storehouses and a disjointed approach to analytics have created sub-optimal conditions, thus preventing organizations from fully harnessing the power of intelligent insights. Says Oracle’s VP Rich Clayton, “If we continue with the same analytical processes and technology with big data, we won’t be able to reap the full potential.” The path to digital transformation success requires a massive update in underlying infrastructure tightly coupled with a well-defined strategy for data, analytics, and AI. The three are intrinsically linked. To enable new applications that can run both at the edge (such as devices and sensors) and in the cloud, or to run workloads seamlessly across the edge, in the cloud, and on-premises, the need to democratize insights will be greater than ever. 

Therefore, democratizing access to actionable information has become the new business imperative for organizations that aspire to win in the digital economy. Those enterprises with a vision for intelligent, autonomous analytics will experience a significant increase in the variety of problems that can be tackled, moving beyond decision making that drives efficiency and productivity to an innovation agenda tuned to competitive advantage.

Digital Marketing for Startups: Why It’s Critical

Reading Time: 7 minutes

In the digital age, marketing your products and services on the internet is an established necessity. In the early stages, startups must promote brand awareness to land clients, but most startups stumble when trying to define essential details such as the main target, or the actual purpose of using social networks. This article focuses on why digital marketing for startups is critical.

The Role of Digital Marketing

Startups are competing with millions of promotional campaigns from well – established organizations with established customer bases. Prying consumer eyes away from these large corporate entities with a yet – to – be proven product, is an uphill task. But that can be overcome with the power of marketing.
Retail startups usually have it figured out with a solid contingency in place for all future marketing needs and a plan to go-to-market. However, technology-focused startups are usually the ones that have it rough. The development cycle causes a lot of changes in the product so they really cannot begin any kind of marketing or branding until they have an MVP (Minimum Viable Product) ready.

Surveying social media trends can unveil a great deal of information about what the current market space looks like. And if it isn’t clear already,  it’s all very digital. Global online ad – spending was forecasted to rise by 4 %, however, 2019 saw a 4.7 % uptick. The average Snapchat user’s revenue generation capacity rose from $1.21 in 2018 to $1.68 in 2019. Simply put, companies are investing larger sums of money on social media and other forms of marketing in the digital arena, and they’re also reaping greater rewards.

And apps like TikTok that focus solely on video-based content, see significant benefits in attracting video ads to their platforms. TikTok’s in-app sales witnessed a 500% surge. Even retail commerce websites put out a 110% growth figures concerning the social referral traffic; the principal form of promotions performed by YouTubers and other social media influencers. “Type in the code to get a 20% discount,” is a statement you’ve heard (and probably responded to) if you’ve ever used YouTube, which of course you have.

It’s Super Cost-Effective

The biggest benefit that digital marketing can offer to budding startups especially, is that if carefully mapped out, it can be far, far cheaper than traditional methods of marketing. And considering how most startups are technology-oriented or at least typically online – facing, this becomes a no – brainer. Retail and e-commerce services, have it the best. Potential customers can be directly led to your service with a click, immediately after they see your ad. Few businesses can boast such easy conversions.

Video: The Hottest Cake

The video makes up more than 80% of all traffic on the Internet. Depending on your budget you can get very specific with who sees your ad. This year American audiences will spend more time on their phones than tune in to their television. You get a level of certainty that even a Super Bowl spot will not guarantee. If your potential biggest buyer spills their coffee during your spot, that’s $5.2 million down the drain. You can get a hundred million pairs of eyes for less than $5000 on YouTube, plus the ad cost. And while not every Ebba Olsson in Sweden is watching the game, she’s very likely going to spot your ad for the latest online music licensing software as she watches a sound mixing tutorial on YouTube.
Dunkin’ reported a 20% decrease in the cost – per – video – view ratio during its trial – run of Instagram Stories stickers and polls. And at 57%, more than half of Pinterest users have said that they are on the app while they shop, in stores and online.

The Audience

Digital marketing can be incredibly powerful if properly leveraged. It’s all about connecting with the right audience.  The brand’s presence is dictated by answering this very basic question – who is your target audience?
Once you’ve adequately measured who your target audience will be, it then becomes important to tailor your marketing practices to ensure that they are receptive. Careful market research is critical here.
As much as you believe in the world-altering potential of your idea, your customer is quite understandably going to need a little bit of nudging when it comes to parting with their hard-earned money.

Drop Some Crumbs

Cookies, you see. Someone visits your website, and you can place a non-intrusive piece of code (with the user’s permission of course) on their web browser. It is an extremely efficient way of serving advertisements based on what websites they show interest in. You simply let them pick up on the trail you lay from there and they find their house of candy.

Cookies essentially serve the function of the modern-day salesman. Using customer data, companies can acutely understand exactly what the customer wants.

User Experience

After you’ve discovered what it is that your audience wants, the next step to building your brand is cultivating the user experience. Social media is an immensely powerful tool at the disposal of marketing professionals.
In addition to increasing sales, social media creates customers that remain loyal; something no other form of branding has been able to properly deliver on. Besides the ubiquitous nature of social media, companies can cultivate an image for themselves unlike any other platform affords them the opportunity to. Sharing posts or stories is something many companies engage in. And the twitter landscape has long been overtaken by large corporate chains like Wendy’s that frequently post funny and entertaining content.
All of this works in creating a sense of what the company is in the consumer’s mind. Carefully controlling this aspect can prove to be supremely beneficial to a growing startup.

Search Engine Optimization

You’ve created your website showcasing your brand and exactly what you do. It’s stunning and minimal. Users can’t help but turn up in droves, right? Few more steps there.

Sure, your idea is unique. However, it still exists within a niche or a subset of one. And there are easily a hundred other results that existed before your website in your very niche (unless you’ve got something remarkably special going on). So, how’s the customer going to find your page?
SEO (Search Engine Optimization), not only boosts traffic, but your business/enterprise seems more legitimate. No one is going to click a link that’s on the third page of a Google search, because no is even clicking on the second page. SEO is a vast topic and merits an article of its own, so we’ll be covering that later.

Location-based Marketing

89% of marketers reported a boost in sales after utilizing location information to specifically focus their promotional material. And location-based marketing is expected to increase this year by 14%. But what is it exactly? Ever received a text about a Buy One Get One free offer that’s running on a nearby Dominos? There you go, location-based marketing.

Companies use location data from your phone and send you location-specific alerts to promotions or things you may like to purchase nearby. This is another very promising facet of digital marketing for startups to tap into.

Email marketing

Yes, emails are antiquated. Or so you’ve been led to believe. One of the best-kept secrets is that, after everything is said and done, emails are probably the best way to reach your customer/client. Any email you send to a potential or current customer falls into this category. Especially when it comes to B2B startups, email marketing can be a real boon. Finding email ID’s are incredibly easy so there is no reason not to be marketing this way. The zero-cost to the company can only be a plus.

Recently held survey results from the Demand Metric and the Data & Marketing Association (DMA), showed that the ROI on email marketing stood at a not – to – be – scoffed – at figure of 122%. Which is actually four times higher than the other most commonly used marketing channels.

Influencers

Say you run a startup that offers rental equipment to filmmakers and photographers. Now you could take the old, well-trodden path and approach filmmakers, pay for branding at film festivals and art galleries, or simply put up ads on TV and billboards. Hopefully, enough filmmakers have seen your ads and you’re doing some decent business. But what if you could do all that for a tiny fraction of what you spent and be able to reach your target audience with far better results? All you have to do is find a filmmaker/photographer that is active online. Get them to do a referral code plug. Maybe you could throw in a 20% discount if you’re feeling particularly generous. This is an extremely popular form of promotion that’s driving a lot of local business to achieve outstanding growth.

Customers are more likely to engage with a brand recommended by a person that they trust. Influencers spend a lot of time building this trust, fostering a connection with their followers and building a community.

Recently the stock footage accumulation website blackbox.com saw a huge influx of users after its creator was featured on a prominent Instagram and YouTube-based influencer’s video. So many users joined that their servers crashed, and they were forced to ramp up their capacity to adjust for all the new faces.

digital marketing for startups

Marketing in the 21st Century

Most customers have developed an apathy to traditional forms of advertising. The sense of wonder and the immediate want for a product that television ads could inspire in the ‘50s is all but lost.

Management in the 21st century must be informed by the successes of management in the 20th century. The 20th century in addition to improving the plight of the worker made incredible leaps and lunges in terms of branding and marketing philosophies. And this drive to look ahead and constantly innovate, not just on the product but on the way the product is sold has been an instrumental part of what defined that era. This century supplements the last as it continues to see an increasing number of options for immense creative outflow. Adidas pioneering its Instagram checkout feature reported a 40% increase in online purchases in Q1 sales.

Startups generally function under conditions of extreme uncertainty. And most of them thrive in this uncertainty. And on their path to creating a sustainable business, startups will have to endure as a massive bunch of hiccups, pitfalls, snags, and all sorts of bruise-inducing gags are bestowed upon them. The one constant as the startups attempt to carve their path is the vision.

Vision is what drives all the marketing too. By getting online, and seeing real-time responses, startups can work on fine-tuning their strategies and optimizing their product to best suit the needs of the consumer. And gaining information from web analytics and various methods to determine online metrics is a very simple task, that’ll help you to accurately measure what works and what does not. Gone are the days when companies had to guess how many people saw their ad in the newspaper and decide on make-or-break situations based on these approximations. The sophisticated tools that are available today immediately offer metrics, such as how many viewers, and from which locations are interacting with their advertisements.

Startups must entirely bank on being more efficient in their marketing than the big dogs. This, however, gets quite challenging given their limited budget and resources. By cleverly making use of a domain that the younger generation (who constitute most startup founders) are very privy to, startups can use some of the methods mentioned above to get there. Digital marketing for startups paves a cheap and effective way to directly reach customers that you most definitely know are going to at the very least be interested in your product.

Artificial Intelligence and The Human Mind: When will they meet?

Reading Time: 7 minutes

Artificial Intelligence is the intelligence exhibited by machines as opposed to the intelligence possessed by humans. It is an umbrella term to represent technologies like cognitive computing, machine learning, image recognition, and many more. Founded in 1956 as an academic discipline, AI has come a long way over the years to reach where it is today.

The smart assistant in our smartphones — Google Assistant, Siri — are all powered by Artificial Intelligence elements. The services that the entertainment-related applications offer in the form of suggestions and recommendations, the security the banking sector guarantees, are all heavily dependant on AI. In fact, AI has evolved to such levels where there is hardly a thing in our life that is not directly or indirectly at least, remotely, related to AI.

As the domain of artificial intelligence progress at a breakneck pace, machines become ‘a little more’ than traditional machines — the ones that are smarter, more reliable, and self-healing. Modelling techniques help in identifying the best choice of crops for a given terrain; Companies employ Natural Language Processing Techniques to improve voice recognition, text recognition, and speech synthesis as they enable computers to understand and manipulate human languages.

Artificial Intelligence vs Human Intelligence

Human Intelligence is a quality of the mind that enables one to use knowledge — acquired from existence, abstract concepts, several cognitive processes, and more, to manipulate one’s environment. A machine that can, in some way ‘mimic’ this quality can make our lives much more simpler and efficient. This is where artificial intelligence comes into the picture.

Intelligence is a quality that is unique to humans. Even the most complex behaviour exhibited by an insect does not qualify to be called intelligence. Take the case with digger wasps as an example: the wasp that goes out in search of food does not re-enter its burrow without looking for intruders. This is based on the fact that a possible threat could have intruded the burrow while it was gone looking for food. But when, as part of an experiment, the food was left right in front of the burrow itself, the wasp was observed to look for threat before entering the burrow, exactly like how it would have responded in the former situation. Intelligence involves the ability to learn from experiences and adapt to changes — something which is absent here.

Developing a machine having such a complex character is not easy. It is a realm that progresses slowly with the hard work of thousands of dedicated computer scientists and programmers, and it takes decades to reach its culminate.

Artificial General Intelligence
Instead of viewing intelligence as one single entity, AI has its focus put on the learning, reasoning, language, perception, and problem-solving aspects of intelligence. The combination of mathematics, statistics, and algorithms are making us capable of such a feat.

Human intelligence has overcome physical limitations we had in the past and has transcended to a different world where such limitations do not matter. AI aims for similar outcomes. We have now reached such levels where we could think and work toward making strong Artificial Intelligence a reality. Strong AI, or Artificial General Intelligence, is the form of AI where a machine’s cognitive capacities are on par with a human being. This machine possesses consciousness, self-awareness and can communicate to us in our languages.

The possibility of such awareness is highly disputed. One of the most significant philosophical question is whether Artificial General Intelligence can be developed to respond like a human brain? Some Scientists believes that Artificial General Intelligence can never become a reality, but some others believe that we can develop it by the next century, or perhaps even before.

AI and Human Rights
The development of artificial intelligence has raised several serious questions. Some of them are regarding the impact AI has on human rights.

International Conferences and high-level committees at different parts of the world are ensuring that the development of AI will only benefit humankind and will not compromise on human rights. Also, companies like Google and Microsoft have already published ethical principles regarding AI.

Since the development of Artificial General Intelligence is not going to happen any time soon, there is no urgency for governments and concerned authority to address the issue and find a solution immediately. Also, with this level of development, it is not possible to predict what impact AI will have on ethics and human rights. Hopefully, with efficient frameworks on AI and ethical guidelines, the advancements in the field of artificial intelligence can be directed only in the direction of progress of humanity. It will begin a new era with enhanced living standards.

AGI tests

AGI Tests helps gauge the level of complexity and efficacy of an AGI model. Some famous examples:

· The Turing Test: a machine and a human interact with another human. This human cannot see the device or the other person. After an interaction with both, the human has to identify the robot. If the person fails, then the robot wins the test.

· The Employment test: a robot has to perform an important job. If the robot works as good as its human counterparts or better, it wins the test.
· The Mirror Test: This test determines whether the subject possesses self-recognition. The robot has to distinguish between an object and its mirror image.

· The Furniture Test: A machine has to unpack and then assemble a flat packed furniture item correctly after reading all the instructions provided.

· The Coffee Making Test: A robot has to enter a home and make coffee all by itself by finding out all the necessary ingredients provided to it. The robot passes the test if it makes the coffee.

· The College Student Test: the robot has to attend a college and graduate. It will attend the class with humans and will appear for the same exams that the rest of the class attends.

How Far Have We Come?

As time progressed and as technology developed, artificial intelligence also transcended its vision. In the past, computer scientists wanted to create machines equipped with AI to perform independent tasks for us. Their success has made scientists think of developing devices that are capable of performing tasks with us. The modern world has seen a lot of progress since this transformation — from performing tasks for us to perform tasks with us.

In some of the developed countries, AI-enabled robots perform primary health check-ups after engaging in a dialogue with the patients. AI-enabled robots which can detect the progress of tumours, create medicinal drugs, etc. find their space in the medical field. There are robots that can help take care of the elderly and design treatment methods.

These are some of the functions that a traditional machine can not do. What is unique to AI-enabled machines is that they make use of machine learning algorithms which lets them access information, interpret it and offer a valid, genuine, and well defined final result.

A Vision for Humanity and the challenges

AI-enabled products offer logical solutions to users. The algorithm that runs the product ensures this. Once they evolve to understand and interpret human text and speech, the quality of the machine and its result will enhance. But such a process is not easy as computing models which can integrate natural and visual processing are necessary for it to be a reality. This is a real hurdle that stands in the path of developing humanoids as understanding and interpreting human language is a complex phenomenon and is very difficult for AI-powered machines as our words change meaning with context.

Another hurdle that stands in the path of such a marvel is the fact that even though medical science has progressed so much, we still do not have a concrete idea about how the human mind or human intelligence functions. We do not know the various elements that constitute our intelligence or how these constituents are interdependent. We first need to have a better understanding of how our mind and intelligence work in order to build machines that in some way mimic these and thereby interact with us as one among us.

Computational neuroscience is a field of study which makes use of Mathematics to learn the human brain by creating theoretical models of our brain. Studies in such areas have revealed to us details about our nervous system, which was previously unknown. The new information available has improved our understanding of the processing capabilities of our nervous system and thus, have given us a chance to improve on artificial intelligence with the help of this information. Drawing inspiration from the human nervous system, Artificial Neural Network, ANN, was born to mimic the problem-solving capabilities of the human brain.

As opposed to functioning with programmed and task-oriented rules or algorithms, ANN systems operate by learning how to perform actions by referring to examples and by learning from previous experiences (do not infer ‘learn’ in the literal sense). Consider the example below:

An ANN System is trained to identify a particular image, say cars, by exposing it to images that are previously labelled manually as ‘cars’ and ‘not cars’. Now it is programmed to choose only the image that contains cars and leaves the rest. This result is saved for later references. Here the program did not feed the system with the details of the car in order to make it identify the image of a car when presented to it. The system can automatically find similar characteristics from the car and can recognize them as pertaining to cars. Thus the system learns how to identify cars from a given set of images; if any.

This technology is used in image recognition. Facebook’s image recognition system, DeepFace, was developed with the help of this technology. DeepFace was trained to recognize human faces with the help of millions of images that were uploaded.

The Future

The limitations that strong AI has today that stops it from attaining its full potential cannot exist for a long time. Exactly how technology brought our world to such heights, it will certain transcend further. With global giants like Google and IBM working on AI, the day cannot be quite far.

The future generation will live in a world where humans and humanoids coexist — with humanoids existing to serve human beings. Those will be the days where human to machine interactions and machine to machine interactions are the same as human to human interactions. Life in such an era will be far more advanced and superior than the best of what we can imagine.

Over 75% of customers availing in one way or another, the services of an AI-powered system. Also, over 80% of global business organizations believe in the ability of artificial intelligence to offer them a competitive advantage. It won’t take much time for the science fiction of yesterdays to become an absolute reality. The global AI market will grow to $60 billion, which is over 25 times its number in 2016.

Acceptance Among People

But keeping all the business facts and statistics aside, AI is making significant progress in the direction where its interactions with its human users are also improving. With developments in the field, especially in the field of speech synthesis and speech analysis, the gap between the user and his/her virtual assistant has narrowed. People have started relying on their chatbots. They have also been trusting the medical diagnosis given by the robots. We call a customer care facility with the expectation that a robot will respond to us; we log into websites to get assistance from a chatbot.

What the human mind has achieved will one day welcome the world of robots too. There will come a day where human intelligence and AI will work together for one shared goal — to make our lives better.

If you are looking for ways to further dive into Artificial Intelligence, you can take a look at our PG Program in Artificial Intelligence and Machine Learning.

Learn, Network and Grow at Great Learning’s Confluence

Reading Time: 3 minutes

Our learners in online-only programs have plenty of communication opportunities in the form of weekly check-ins with mentors and online forums where they can resolve their doubts and interact with their peers. We decided to take that a step forward with Great Learning’s Confluence. 

The objective of GL Confluence was to help our online learners network with their peers, meet the Great Learning team in person, on a platform that enabled direct interaction with industry experts.

The event was held in Bengaluru, and it saw the participation of 100+ of Great Learning’s Analytics and Artificial Intelligence Program participants from all over India. Participants had gone to great lengths to attend Confluence because they wanted to utilise the opportunity to meet their peers in person and participate in the industry sessions that were panelled by industry veterans. 

Panel Discussion

Learn, Network and Grow at Great Learning's Confluence

The panel was moderated by Rounak Dholakia, GM Operations, Great Learning. 

The other panellists included: 

  • Adarsh Kumar       – Ex Leadership Team, Mu Sigma | Founder & CEO, TagBox Solution – an IoT startup
  • Ayush Agrawal      – Ex Barclays, RBS, Prudential | VP Risk Management, JPMC
  • Vasudev V             – Deputy Director, Myntra 
  • Sunil C                  – AI practitioner / Researcher | Head of Innovation at one of the largest logistics firms in the world
  • Hari Nair               – Co-Founder of Great Learning

The themes of the panel discussion were around the uses cases of analytics across different domains. They also discussed the pros and cons of working in different kinds of analytics setups, namely in-house vs service based vs product based. Participants also gained valuable insights on how to approach career transitions in the Analytics field, which we hope they would apply when they are done with their programs.

Networking Session

Great Learning Networking Session

This was followed by an ice-breaker session, where Great Learning’s participants interacted with each other. Given their mutual interest in Analytics and technology, they were able to find common ground and established both professional and personal relationships, which was very heartening to see. 

Career Mentorship Session

The career session was led by Swaroop Srinivas, who has experience guiding over 20,000 professionals in approaching transitions. His insights regarding CV building, cracking interviews helped Confluence participants understand how they can fast track their transitions.

Due to the overwhelmingly positive feedback received from participants, we plan on more such editions in different cities with increased frequency. We are grateful to honour the requests of program participants to conduct similar events in different cities, and with an increased frequency to help our program participants have enriched learning journeys that help them meet their career ambitions.

Here are a few excerpts from what our learners had to say about Confluence

“GL Confluence was entertaining and informative”

“An interesting event that was well organized. A very informative panel discussion”

“Amazing Great Confluence Rocks ! Learnt and networked a lot. Great Learning rocks !!”

“Good Confluence, should happen twice a year”

“Confluence was productive, panellists were practical, career session was outstanding”

“It was superb and very well structured. Thank you for your efforts”

“Just Need this in different cities as well”

 

Great Learning’s program made me job-ready: Aishwarya, PGP-DSE.

Reading Time: 2 minutes

Learning is a continuous process. As the trends in the industry change and evolve, upskilling will ensure you stay ahead of the curve. Read on to find out how Aishwarya, our PGP-DSE alumnus underwent a successful transition with Great Learning.

Why did you choose to learn Data Science?
After completing engineering in computer science in 2014 I moved into operations initially with the Bank of America in the banking segment and then in e-commerce operations at Amazon. After that, I felt the need to make a career transition, but for me going for higher studies was costing a lot of time and money and this became the prime reason to look for courses like the  PGP-DSE program because of its durability, the timeline, and opportunities to transition to a better role in mare 6 to 7 months. Great Learning has its own brand name which made it easy for me to decide and join. I had considered 3-4 courses like ISB, Insofe, Upgrad, but Great Learning was more affordable and relevant to me.

Did the course help your transition to Data Science?

I was not from a Data Science background, but I wanted a transition into business analytics. I knew that I would need to be strong with the concepts of statistics, Python, Machine Learning and SQL. I can say that the program has covered almost everything that is mentioned in the curriculum. Professors were really focussed on what the market wants and hence the curriculum is designed with a lot of flexibility and keeping an eye on future trends.

Did the course prepare you for interviews?

I was interviewed by recognized companies like Big Basket, HPE, TVS etc. The interview with HPE was a major turn around for me. I cleared three interview rounds and everything they asked was related to previous experience and program curriculum. So I think it was a very good learning experience for me as well. Even though I did not opt for the placement process by Great Learning, the course still made me job-ready and I was able to bag two more jobs. The One with Cisco and the other with Uber and I bagged the latter.          

Do the learning outcomes help you in your current job role?

After completing around 4 and a half months of the program, I had applied for Uber. The knowledge that I gained through the course really helped to crack the interview. The first round was the written test conducted on advanced excel, SQL, and basics of Machine learning. The second round was entirely on SQL for which I would credit Professor Girish for his sessions. The third round mainly focussed on my business acumen, domain knowledge as well as how I would improve new processes in Machine learning. I majorly work on SQL and visualisation here.

What advice would you give to the aspirants?

This program is ideal for anyone who is looking for a transition into the field of Data Science. It will make you job-ready and make you efficient enough that you can crack any interview on your own of which  I am a living example. The curriculum is very well designed and the placement support is really good.

I interviewed with many companies through Great learning: Hariprasath.

Reading Time: 2 minutes
  1. What is your academic background and work experience?

I have completed my graduation in Electrical and Electronics Engineering from PSG Tech in Coimbatore. After college, I worked for IBM as an associate software developer and worked with SQL. After this, I took a break to try competitive exams and other career options and that is when I came across this course through a friend who was already enrolled for PGP-BABI in GL and on his reference I joined DSE.

  1. Why did you choose Great Learning for Data Science?

The assurance I got from those already studying in the Great Learning regarding the course structure and placements convinced me to join the course. I joined Data science course because it is a demanding field and a very interesting topic. Additionally, the basics of machine learning covered in the course were really helpful for us to transition into many different positions in the industry.  

  1. What were the outcomes of enrolling in this program?

The hands-on learning approach for almost all of the courses and languages like SQL, Python covered in the course helped us understand well and prepare ourselves for our interviews and career in our future. Also, we got to learn a lot from the experience of some of the expert faculties here, making the learning very enriching.

  1. How did the experience here help you in your career?

The program curriculum is designed to provide the basics of data science, statistics, machine learning and data visualization. I got several face-to-face interview offers from many companies in spite of a break in my career. Utilizing these opportunities, I interviewed with many companies like BigBasket, Cartesian Consulting etc via Great Learning. We had mock interviews, multiple CV reviews, career prep sessions, aptitude sessions etc which helped me outperform myself in all my interviews.

  1. How did you get in your current role? What were you interviewed in?

I applied at Latent View through the referral of a friend. The first round of interview was an SQL Programming test followed by two rounds of technical interviews where I was tested mostly on Python and SQL, their emphasis on business analysis, approaching the solution, guesstimates and basics of statistics and machine learning.

Any advice to aspirants who intends to take up this program?

Be passionate and self-motivated in whatever you learn here. In case of any problem, talk to Faculties and in case you are scared of maths and programming, don’t be, work on it.

I feel lucky that I got to join Great Learning: Suchitra, PGP-BABI.

Reading Time: 2 minutes

After a maternity leave, it might get challenging to get back in the corporate world. That didn’t stop Suchitra though. Her steadfast determination to pursue a successful career, along with Great Learning’s PGP-BABI put her on the path to success in an Analytics career.  

Why did you choose Analytics as a stream?

I finished my B.E. in 2012 after which I worked at Infosys for about 3 years as a Developer. Due to personal reasons, I had to take a break from my career and soon after, I realised how Data and Analytics are the booming sectors, full of opportunities and growth. With that, I started googling and after a lot of research, I wanted to take charge of my life and transition my career into Analytics.

Did you consider any other options before joining Great Learning?

I consider doing my post graduation from a reputed college that provides specialisation in Analytics but eventually couldn’t join as it was in a different city. After this, I did search for all the possible online courses in my city and came across only a handful that made sense to me. Finally, I felt Great Learning was the right choice for me, because of the brand image in the industry and stories on successful career transition for many, and because they provided quality education. Also, the prime reason to join the course was it was I had to attend only 1 weekend class per month and rest was all online, making it easy for me to manage home and studies.

What was your experience with Great Learning like?

I feel Great Learning was as a blessing to many like me who have breaks in their career. Not just it gave me quality education, a clear understanding of each subject domain but also networked me to my batch mates and expert faculties, who boosted my confidence to start again with my career; and do it much better this time. With their support, I started performing better in Statistics and Accounts where I had no prior basics and GL delivered what it promised in terms of the curriculum.

What did you like the most about Great Learning’s Analytics program?

The best that anyone can take out with this program is the Capstone project. As a team, we performed a live project. Through it, I was able to get into Radiant Global solutions as an intern first and then got confirmed as a Senior Data Analyst.

What’s your current role like?

With everything that I learnt from the course, I felt my previous role was very limited and I wanted to do more with my knowledge and rebuilt confidence. Great Learning, again supported me by organising Excelerate, where I tested the waters with many companies and finally cracked Tiger Analytics and got in as a Senior Analyst. I feel lucky that I got to join Great Learning and believe that if this wasn’t from them, I would have not got my confidence and life back on track with this ease and comfort.

Do you have any tips for other aspirants?

Learning Never Stops at Great Learning. This one-liner drove me all the way through my journey here and will drive me henceforth in life. I want to tell all, that nothing can stop you except yourself. If you want to do something in the analytics field even if you have a break, GL is the right place for you. With the right guidance, mental support and expert faculties, you can grab any opportunity on your way with your hard work and self-motivation. 

Career Support stands out at Great Learning: Alvino, PGP-DSE Alumnus

Reading Time: 2 minutes

Here’s how Alvino Aji went from a fresh college graduate to landing a job at Uber. 

Why did you decide to learn Data Science?

I have completed my Computer Science Engineering in 2018 and I had plans to go for higher studies. But I thought it would be better to have some field and market experience before I join MS. So I decided to opt for Data Science and Engineering program as one of my cousins was already enrolled in. Since it was a full-time program I relocated to Bangalore.

What did you like the most about the program?

The faculty are really good here, I mean the interactive sessions with them were amazing. The sessions were not like monotonous classes, but there were lots of hands-on sessions that made it very interesting. The curriculum is very well designed and the boot camp model of training is excellent.

How did you land a job in Uber?

Uber had conducted an online test at our Bangalore campus. After clearing the test I had two technical rounds. I was tested majorly on Python, SQL, Machine learning and SAS during my interviews with Uber. Thanks to the guidance given here I grabbed this opportunity and joined in my dream company as a fresher.

How was the career support offered by Great Learning?

I had an opportunity to get interviewed by some of the top companies. Career support here is what that stands out and makes Great Learning better apart from other reasons. I had one-on-one career mentoring session with Mr Srijit Ghatak which was extremely enriching where he mentored me and gave me tips on improving my CV and interviews.

What is your advice to the aspirants?

Even if you fail in an interview, you still have a lot of things to learn from the experience of giving the interview. Keep improving on all the things that you think you are weak at and polishing those which are your strengths, that will help you in the long run and eventually crack many opportunities on your way.

6 best options to upskill yourself in 2019

Reading Time: 6 minutes

The current state of the job market, globally and in India, is in a state of upheaval. There are new advancements in existing technologies that demand that professionals be aware of all the changes, and some technologies are completely changing the nature of certain professions. To put that in context, the World Economic Forum estimates that 65% [1] of children entering primary school now will be working in jobs that do not currently exist.  

Each working professional is now aware that they need to be constantly learning in order to stay relevant in the work-force and secure their future careers. But what they choose to learn is also very important. Nobody wants to be stuck with a pager when the entire world uses cellphones. This also applies to freshers and early-career professionals, who need to build the right skills so that they don’t waste time working in domains that may soon become obsolete.

At Great Learning, we’ve seen over 10,000 learners who have transitioned to rewarding careers in domains that we are sure would build the future. We’ve specifically chosen to design programs in these domains, after extensive research on long-term growth prospects and employment opportunities that will be made available in the future.

We’re using that data we have to list out the various options for you based on your experience and interests. Take a look.

1.Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning

The World Economic Forum has estimated that 75 million jobs will be displaced by AI by 2022. But it’s also going to add 133 million jobs in the same time frame[2]. That’s a net gain of 58 million jobs that will be available for qualified professionals. In addition to the availability of jobs, there’s a massive shortage of AI professionals in India, because 76% of companies[3] say they have trouble implementing AI due to lack of qualified employees. When this huge demand is combined with AI influencing every other domain, it’s an obvious choice for anyone wanting to work with a technology of the future. Great Learning offers 3 programs in this domain:

PGP-Artificial Intelligence & Machine Learning – A 12-month comprehensive program that will make you an AI expert.

PGP-Machine Learning – A 7-month program that covers Machine Learning exhaustively with some context about Artificial Intelligence.

Deep Learning Certificate Program – A 3-month certificate program for professionals with experience in AI and ML, to gain an in-depth understanding of Deep Learning

2.Business Analytics and Data Science

Business Analytics and Data Science

No business in the modern market place can afford to ignore Analytics right now, because they need data-driven decision making to improve their outcomes. Data Analytics is also now ubiquitous because even domains that seemed impervious to data analytics are now adopting them, such as law, defence, HR, in addition to the increased adoption by analytics-driven industries such as finance, logistics, internet companies and so on.  

This domain also suffers from a similar problem of a lack of qualified professionals, leading to over 97,000 jobs [4] being vacant in India. which can be filled after taking one of these programs:

PGP-Business Analytics & Business Intelligence – This 12-month program requires no prior technical expertise and teaches you all you need to know about Business Analytics in an industry context. This program is better suited for professionals with a few years of experience.

PGP-Data Science & Engineering – This is a program aimed at fresh graduates and early-career professionals to help them launch their careers. This is a full-time program that offers immersive learning in a classroom format.

Business Analytics Certificate Program – This is a certificate program that’s aimed at beginners to learn the basics of Business Analytics. Since this is an online program, learners will have a flexible learning experience.

3.Cloud Computing

Cloud Computing and DevOps

Cloud Computing forms the backbone of data delivery and management, as it offers great flexibility and cost-effectiveness to organisations. Cloud computing has grown over the past decade to make data accessible to everyone, and the future of cloud computing hinges on remotely delivering hardware capabilities. Cloud computing has already phased out CDs, DVDs, Hard Drives and pen drives as the preferred mode of storage, and they’re only getting started. This bodes well for people looking to get into cloud computing to further their careers, because India is poised to see over 1 million cloud jobs by 2022, according to IDC [5]. Here’s how you can upskill:

PGP-Cloud Computing – This is a 6-month online program that will teach you to become a cloud architect. The program comes with an optional Developer track certificate offered to professionals with prior programming experience. Learners can choose to build expertise in AWS, Google Cloud or Azure.

Devops Certificate ProgramThis DevOps program teaches professionals the practice of building and deploying systems in the cloud. At the end of this online program, learns will become competent DevOps engineers.

4.Cybersecurity

Cybersecurity

As more systems move online, there’s a larger requirement for keeping all information and services safe and secure. Critical operations such as banking, defence all have cutting-edge security systems, and the same degree of security is trickling down to other domains as well. In the light of recent privacy concerns, every organisation that holds any data are looking keenly to improve their security measures. This gives rise to an opportunity for professionals to upskill in cybersecurity and launch their careers. The Asia-Pacific region alone has a shortage of 2.14 million cyber security professionals[6]. This program will ensure that become well-versed in cybersecurity:

Stanford Advanced Computer Security ProgramThis 6-month online program is a program by the Stanford Centre for Professional Development, delivered by Great Learning. It features course content and online videos from Stanford faculty and helps learners transition to a career in cybersecurity.

5.Full Stack Development

Full Stack Development

As more companies look to increase their software capabilities, Full Stack Developers and in demand because they can look at a system holistically and troubleshoot any errors that may arise.  Since they are familiar with both the front-end and the back-end they’re well-suited to participate in the end-to-end process of building and managing applications. Reports have estimated a 20% growth in jobs making it an in-demand job profile in India [7].

CAP-Full Stack DevelopmentThis online program teaches all aspects of Full Stack Development and is perfectly suited for early career professionals with at least 1 year of experience, who are looking to gain relevant skills set that will set them up for career success.

6.Digital Marketing

Digital Marketing certificate program online

With increased online digital ad spending by almost all companies, the digital marketing space has seen explosive growth over the past decade. Every company needs to maintain a digital presence, which increases the demand for Digital Marketing professionals. Digital Marketing has ample growth opportunities in a wide variety of skills such as Email marketing, Social Media Management, Content Marketing, SEO/SEM and much more, so learners have the freedom to choose to specialise in the vertical that resonates the most with them, which this program will help with:

PGP – Strategic Digital MarketingThis 5-month online program covers a wide range of Digital Marketing skills, and helps learners become a well-rounded Digital Marketing professional.

The skills and domains described on this page are without a doubt the technologies that will build our future and dictate the course of human progress. Depending on your experience, and of these skills will definitely set you up for career success, provided you put in the work to learn concepts and apply them to real-world problems.

All the programs that listed here use real-world projects to instil the theory you’ve learnt. They also have industry experts who will give you useful insights into how you can apply what you’ve learnt to solve real-world problems. Career guidance is also included in the form of interview preparation, CV building, access to exclusive job opportunities and 1-to-1 mentorship.

Sources: