Deep Learning in Marketing and Sales

The buzz and enthusiasm about deep learning has significantly increased over the past few years. With numerous business ventures embracing this technology for good, deep learning is earning huge popularity now. If you are aware of Machine Learning and its implications, understanding Deep Learning won’t be that tough.

Here’s a quick look at what is Deep Learning and its serious applications in the business arena.

What is Deep Learning?

DL or Deep Learning happens to be a significant part of ML. It can be referred to as a subset or subdivision of Machine Learning that maps artificial neural networks. The mapping takes place to recreate or replicate processes performed by human brain.

That’s not all. Deep Learning also plays a crucial part in the identification of speech patterns, algorithms, images, and data analytics.

In spite of this simple and easy-to-understand introduction, there’s no denying the complexities involved in Deep Learning. Implementing DL strategies and incorporating them into existing business processes isn’t an easy affair. It becomes imperative to keep numerous aspects in mind thus devising effective DL strategies.

Transforming the business world with Deep Learning

Deep Learning can be revolutionary. If implemented in the right way, this particular technology can transform business processes to a great extent. Deep Learning helps to decode complex unstructured data and derive consumer insights that are crucial for creating sales and marketing strategies.

From retail and transport to healthcare and manufacturing, DL has started making a mark in various sectors. Let’s take a look at how it’s transforming sales and marketing for businesses:

1. Automating end-to-end customer journey

As mentioned earlier, deep learning will allow marketers to access insights from unstructured data sets such as image, video analytics, speech recognition, facial recognition, text analysis tools and much more. In short, deep learning becomes a way to accurately understand the voice of a customer.

Customer feedback and expectations can be gauged on a real-time basis and business organizations can get information to upgrade their products and services. Based on these premium insights from deep leaning, Brands can articulate the right messaging to the right customer.

2. Understanding analytics from IoT products 

Home automation is creating profitable avenues for organizations across the globe. Deep learning can help businesses understand the analytics for IoT products. It helps to capture data from the machines in different scenarios and monitor them in an easy and cost-effective way.

Through these analytics, deep learning can help to understand the interactions between machines and customers better and based on the data, the performance of IoT products can be enhanced from time to time.

3. Using Chat Bots to enhance CX

The presence of chat bots has revolutionized business marketing to a great extent. Chat bots leverage data mining, artificial intelligence, and natural language processing, thus creating new ways to interact with the end user.

Through chat bots, customers have the opportunity to engage in personalized communications. Apart from ensuring unmatched consumer experience, Chat Bots help an organization to have timely conversations with users and give them product recommendations or suggestions. You can create your marketing strategies with consumer preferences in mind thus offering them targeted products.

4. On-going predictive analysis

Deep Learning plays a highly significant role in the data analysis process. Whether it’s a small or big organization, entrepreneurs will have the chance to perform successful data analytics. Predictive analysis becomes easier, and you can develop crystal clear ideas of customer preferences.

Signing off

If market reports and figures are anything to go by, Google is running 1000 deep learning projects as of now. The number of projects was only two in 2012, and this will succinctly explain the massive importance of DL in business marketing.

Artificial Intelligence and Machine Learning in the Retail Industry

While the world is busy talking about Artificial Intelligence powered technologies such as self-driven cars, machines challenging human intelligence at a game of chess, and AI technologies in recruitment, there still remains an untapped potential for AI in the retail industry.

With most retailers now focusing towards providing an omnichannel experience for their shoppers, AI can play a crucial role in disrupting the retail industry.

Creating Smart Shops with Artificial Intelligence

While AI assistants such as Siri and Google Home help us maintain our day-to-day groceries list and change our shopping experience, an ideal situation would be to walk into a smart store without any shop attendees or long check-out queues.

Imagine a shopping experience where you can just enter a shop, pick up the stuff you want, a bunch of facial recognition algorithms process your purchase, and automatically money gets deducted from your digital wallet when you leave the store! Suddenly, shopping seems to become more of technology experience.

Amazon has been marginally successful in replicating this experience for customers with its Amazon Go grocery store. In order for shoppers to use this, they need to install the Amazon Go app. There are digital sensors on the shelves that detect when purchases are made and the money gets deducted from the customer’s account when he or she exits the shop.

Amazing isn’t it? This is what a future-ready AI store should ideally look like!

Predicting online customer behavior with Machine Learning

We live in a digital world today where every minute truckload of data about customer behavior is recorded and stored. But most companies struggle in extrapolating this data into actionable steps that can increase their ROI by 10X times.

You may have luxurious marketing and advertising budgets to target your customers, to get awesome click-through rates, but how are you making your customers tip over the fence and make a sale?

With Machine Learning tools, you can create an intelligent and automated marketing system that helps you with:

• customer segmentation
• predicting customer value and
• designing product recommendations.

But it doesn’t end with just that. Machine learning also helps us optimize our Ad budgets and invest them in the right customers.

In a world where resources and time are always limited, we are forced to make quick and smart decisions. But machine learning algorithms are programmed to analyze this huge pool of customer data within a fraction of seconds and identify customers who are 65% more likely to make a purchase. Thus, in this way, ML helps to justify and set optimum ad bids to target customers.

Product recommendation is another favorite pick for top marketers. ML helps to analyze online customer behavior in depth, such as the products they are interested in, the blogs they read, the price ranges they operate at etc. Based on all these interactions, a business can invest in curating content that makes the shopping experience as “personal” as possible.

Artificial Intelligence and Cognitive Computing in the Retail industry:

AI and cognitive computing are adding the “innovation” to the retail industry. Creating an omnichannel experience for customers has become the top most priority for retail brands.

Out of the many retail sections, in this blog post we are going to concentrate mainly on two AI integrated retail categories:

1) Product Recommendations:

IBM’s Watson, powered with its cognitive computing abilities is an excellent choice for brands who are looking to provide both in-store and online product recommendations.

• AI-powered retail store

In this example, we see how a New York based wine company called Wine4Me decided to choose the AI technology of IBM Watson to make in-store wine recommendations for its customers. The goal was to make wine shopping an easy and personalized experience.

But the first step was to provide Watson with ample data from wine tasters and also teach Watson how people ask and shop for wine. Based on the occasion, price, sweetness,  brand, and age of the wine, Watson should be able to bring up a list of wine recommendations that would suit the customer’s demand.

Now, this whole AI-powered shopping technology works as a win-win for both the retailer and the customer. Customers, on one hand, enjoy a hassle-free and engaging shopping experience while retailers can make better inventory decisions by tracking customer preferences.

• AI-powered digital store

In this example, we will talk about how the San-Francisco based premium brand The North Face integrated Watson on its digital platform to create an unparalleled shopping experience for its customers. The North Face is a premium outdoor product company specializing in outerwear, coats, backpacks etc

Though SEO filters and keyword phrases help in attracting customers to the website, most brands forget to communicate with their customers to help them find what they want. This is where AI can help you establish that user connect.

Let us assume that a user wants to buy a jacket online, then the AI platform asks the user a series of questions such as the purpose of the jacket, the time period during which he will use it, which place will be visiting, color preferences, material preferences and so on. The user is free to answer all these questions in his own style and type in any response.

With every response, the AI will trigger a series of product recommendations that closely match the user’s requirement. Now, this sounds fancy, doesn’t it?

But it all boils down to data and also literally teaching the AI what would the possible use case scenarios be that users would identify themselves with. Even the most non-obvious statements and taken for granted business scenarios need to be keyed in so that the AI helps to generate the right response.

This next-generation online shopping experience is sure to make both customers and retailers conscious of their expectations. Thus, if used in the right way, AI has great potential to provide product recommendations that can increase revenue from sales by 10x.

2. In-store Sales:

AI is all set to change the traditional shopping experience when a customer walks into the store. With e-commerce booming, you find lesser customers who are willing to enter a store and interact with a store assistant.

So, how can AI help in increasing the customer foot-fall at a brick and mortar retail store? The answer is simple – by using AI-powered robots.

Pepper,  a humanoid robot from Softbank’s robotics company has been in the news for enriching the customer shopping experiences at retail stores and serving as a retail concierge. Stores across the US, who have adopted Pepper as their smart shop assistant, have recorded almost 70% increase in customer walk-ins and sales.

Now that’s huge, isn’t it?

But what does this humanoid Pepper do differently from a salesman?

Firstly, Pepper acts as a brand ambassador for the store that drives in customers. Based on sheer curiosity, customers are more likely to stop by your store to experience an interaction with a robot who could help them with their shopping decisions. It almost feels like having a friendly celebrity with whom you can take a tour of the store.

Secondly, the humanoid robot is programmed to sense movement, emotion, offer shopping advice, product information, etc. Thus, it helps the user make an informed purchase. It has a tablet positioned on its chest where the user can key in his preferences and based on that Pepper will offer suggestions.

This definitely boosts user engagement and conversations inside a store. And the best part is that Pepper is always connected to the internet and all the data thus collected is stored in the cloud. So, next time when the same customer enters the shop, it becomes easy to showcase products based on his/her interests.

Thus, Pepper helps to automate all the obvious and mundane tasks at a retail store and transform in-store shopping into an engaging and joyful experience for customers.

shopping experience while retailers can make better inventory decisions by tracking customer preferences.
add: The rise of retail AI solutions is further transforming the industry by enabling real-time analytics, personalized recommendations, and demand forecasting, leading to increased efficiency and customer satisfaction

The future of Artificial Intelligence in Retail

While we are positive and hopeful that the future of retail is likely to be dictated by AI, we also notice that there is still time until this technology gets widely accepted in stores around the world.

One of the major constraints involves high budgets, thus making it easy only for the bigger players such as Amazon, Walmart, Target etc to become early adopters of such technologies.

But having said that, it is time that retailers identify the potential that AI and Machine Learning can make to their business and take steps to make the shift soon.

If you would like to build an AI-powered solution for your retail business, then drop us a short message with your requirements

Designing for Artificial Intelligence

A new age design manifesto needs to grow beyond its traditional scope. As an outcome, it needs to factor in the growing space and time ecosystem enabled by new age technologies like Artificial intelligence (AI) and robotics. Hence the design principles of the past have evolved significantly to include new policies and principles.

Interested to explore what these new age design principles for AI are? Then read on..

1. It solves a real life human problem

The modern day design principles around AI should focus on solving a specific human problem. Going beyond the buzz and hype, a well-designed AI system has to concentrate on resolving a human problem (for e.g. delivering true value in service or product). The intent has to come out clearly when looking at using design to increase the value proposition of the AI system.

An example is the supportive body suit from SuperFlex. It mimics the natural body and muscle movement and helps out elderly people who have muscle or bone issues preventing them from carrying out routine tasks like moving hands or standing up.

2. Design for AI need not follow historical context

As mentioned earlier and as depicted by the emerging design trends, design need not conform to historical context. With new technologies it is obvious that design too needs to move beyond what we have experienced in the past and open our eyes to something totally new. This is essential when designers are working on truly ‘smart’ objects and should not be limited to just AI-based robots.

3. Design needs to understand the utility value of AI

AI was never designed or promoted to replace humans. It was instead designed to add value to human lives and make it more efficient/productive. If designers keep this basic difference in mind the resultant AI system would have better success potential in the market. When you are brainstorming for design ideas you need to ask yourself “Can AI  complement human lives rather than replicate it?”

4. Good AI Design needs to help everyone

A smart AI based product needs to be embraced equally by the tech lover and the senior persons of the family. Typically it is seen that one person who brings the system inside the house loves it while others aren’t easily swayed by its prowess. Designers need to figure out how they can have the entire household to get to use the product and derive benefits from it.

5. Good AI design doesn’t get in your way

A designer needs to understand that the AI product has to be subtle and discreet in its functioning so that it delivers a stellar experience without getting in your way. Such a well-designed AI system needs to generate subtle signals about the action being performed without disturbing the activity that you are doing.

August Smart Lock is one good example here.  It allows the user to unlock the door automatically when he/ she is nearby. You need not stop to take out keys from the bag or retrieve the smartphone from your pocket to unlock the door.

To conclude

With these principles in place, designers for AI systems will be in a truly remarkable place in the near future. This will be important as design will definitely be playing an increasingly influential role in building complex Artificial Intelligence solutions and systems.

The Depth Of Deep Learning

Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now.

The term “AI” is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don’t understand what AI is.

Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.

The first step towards understanding how Deep Learning works is to grasp the differences between important terms.

Artificial Intelligence vs Machine Learning 

Artificial Intelligence is the replication of human intelligence in computers.

When AI research first started, researchers were trying to replicate human intelligence for specific tasks — like playing a game.

They introduced a vast number of rules that the computer needed to respect. The computer had a specific list of possible actions, and made decisions based on those rules.

Machine Learning refers to the ability of a machine to learn using large data sets instead of hard coded rules.

ML allows computers to learn by themselves. This type of learning takes advantage of the processing power of modern computers, which can easily process large data sets.

Supervised learning vs unsupervised learning

Supervised Learning involves using labelled data sets that have inputs and expected outputs.

When you train an AI using supervised learning, you give it an input and tell it the expected output.

If the output generated by the AI is wrong, it will readjust its calculations. This process is done iteratively over the data set, until the AI makes no more mistakes.

An example of supervised learning is a weather-predicting AI. It learns to predict weather using historical data. That training data has inputs (pressure, humidity, wind speed) and outputs (temperature).

Unsupervised Learning is the task of machine learning using data sets with no specified structure.

When you train an AI using unsupervised learning, you let the AI make logical classifications of the data.

An example of unsupervised learning is a behavior-predicting AI for an e-commerce website. It won’t learn by using a labelled data set of inputs and outputs.

Instead, it will create its own classification of the input data. It will tell you which kind of users are most likely to buy different products.

You’re now prepared to understand what Deep Learning is, and how it works.

Deep Learning is a machine learning method. It allows us to train an AI to predict outputs, given a set of inputs. Both supervised and unsupervised learning can be used to train the AI.

We will learn how deep learning works by building an hypothetical airplane ticket price estimation service. We will train it using a supervised learning method.

How Deep Learning can build an AI  to estimate Airplane ticket prices

We want our airplane ticket price estimator to predict the price using the following inputs (we are excluding return tickets for simplicity):

• Origin Airport
• Destination Airport
• Departure Date
• Airline

Like animals, our estimator AI’s brain has neurons. They are represented by circles. These neurons are interconnected.

The neurons are grouped into three different types of layers:

1. Input Layer
2. Hidden Layer(s)
3. Output Layer

The input layer receives input data. In our case, we have four neurons in the input layer: Origin Airport, Destination Airport, Departure Date, and Airline. The input layer passes the inputs to the first hidden layer.

The hidden layers perform mathematical computations on our inputs. One of the challenges in creating neural networks is deciding the number of hidden layers, as well as the number of neurons for each layer.

The “Deep” in Deep Learning refers to having more than one hidden layer.

The output layer returns the output data. In our case, it gives us the price prediction.

So how does it compute the price prediction?

This is where the magic of Deep Learning begins.

Each connection between neurons is associated with a weight. This weight indicates the importance of the input value. The initial weights are set randomly.

When predicting the price of an airplane ticket, the departure date is one of the heavier factors. Hence, the departure date neuron connections will have a big weight.

Each neuron has an Activation Function. These functions are hard to understand without mathematical reasoning.

Simply put, one of its purposes is to “standardize” the output from the neuron.

Once a set of input data has passed through all the layers of the neural network, it returns the output data through the output layer.

Nothing complicated, right?

Training the Neural Network

Training the AI is the hardest part of Deep Learning. Why?

1. You need a large data set.
2. You need a large amount of computational power.

For our airplane ticket price estimator, we need to find historical data of ticket prices. And due to the large amount of possible airports and departure date combinations, we need a very large list of ticket prices.

To train the AI, we need to give it the inputs from our data set, and compare its outputs with the outputs from the data set. Since the AI is still untrained, its outputs will be wrong.

Once we go through the whole data set, we can create a function that shows us how wrong the AI’s outputs were from the real outputs. This function is called the Cost Function.

Ideally, we want our cost function to be zero. That’s when our AI’s outputs are the same as the data set outputs. 

Thus at this juncture, with the help of deep learning we have almost trained the AI to project an output that is in line with the input data. This ladies and gentleman to be honest is just the basics, but deep learning along with precision is used to train the AI for supervised learning.

We hope that at this juncture you have some idea of how the different elements of Machine Learning, Deep Learning and Neuron networks all come together to create Artificial Intelligence.

Stay tuned for more interesting facts on AI and Machine Learning!

The AI Paradigm

AI was coined by John McCarthy, an American computer scientist, in 1956 at The Dartmouth Conference.

According to John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.

Have you ever been so lazy to be stalled on your bed with packets of tortilla chips and the latest episodes of Game of Thrones, that you just fantasized a remote control with multiple buttons to open the door or turn the fan on or do all that boring stuff?

Oh wait, that still requires you to hold the remote and press the buttons, right? Gee, why don’t we have a robot that would just read our mind and do everything from household stuff to attending the unwanted guests without asking anything in return. Firstly, such robot will have to be super intelligent.

Not only will it have to be efficient to perform routine tasks, but also understand your emotions viz-a-viz, mood swings and your behavioral pattern by observing you every minute and processing the data of your actions and emotions. Apart from the hard-coded seemingly basic set of functions, which in itself is a mammoth task, the machine will have to progressively learn by observations in order to perform as good as a smart human to serve you.

While a lot of this has been significantly achieved, it is still a very hard task for a machine to detect, segregate and arrange scented towels, hairdryers, Nutella box or contact lenses from a pile of junk than computing the complicated Euler product for a Riemann Zeta function. Machines can be entirely clueless and result into wrong outputs for what seems obvious that humans can solve in just a second’s glance.

Firstly, Artificial Intelligence is not the artificial intelligence Hollywood would have us imagine it to be. When people talk about ‘volcanic’ changes in ‘AI’ they are talking about one particular field of technology: Machine Learning and within that field, Deep Learning. Machine Learning is a very literal description of the technology it describes, that is a program written to learn and adapt. The pioneering technology within this field is the neural network (NN), which mimics at a very rudimentary level the pattern recognition abilities of the human brain by processing thousands or even millions of data points. Pattern recognition is pivotal in terms of intelligence.

A lot of people assume that we are developing general AI rather than applied AI. Applied AI is intelligence, but in a very limited field and requires supervised training. For example, in recognizing human faces (Facebook), driving cars (Google Autonomous Cars),  namely matching teachers to students for optimal outcomes. A general AI on the other hand, is not limited to a narrow field where humans still have to impose certain rules before it can ‘learn.’ It learns ‘unsupervised’. To clarify, there are hundreds of companies using applied AI such as a vacuum cleaner that knows how to avoid your cat, there are none that have developed general AI like the Terminator.

We are getting closer to general AI though. There is a developing technology, “Adversarial Training of Neural Networks“, where the data from one machine learning program helps to train the other in a kind of closed loop. This is the technology that Google and Facebook have been flouting a lot recently. An example of this might be in medicine, where one ML program is used to diagnose a patient, and another is used to prescribe a treatment. The two programs may train each other in that correct treatments suggest correct diagnoses and the correct diagnosis may lead to different treatments, and so on.

AI is humanity’s quest to understand itself.

It is our attempt to explain things that define us and placed us on an evolutionary pedestal: Our ability to reason and think, to be self-aware, learn complex patterns and create and achieve better and bigger things.

In short, it is an attempt to map how our brain which is something more than just the grey matter in our head, works.

Attempting to generate ‘intelligence’, which is a broad term we’ve come to use to define all of our uniqueness artificially maybe humanity’s ultimate self-reflection. It could be the culmination of centuries of pondering about philosophy, psychology, religion, biology, chemistry and a million other fragmented sciences and non-sciences, which we have developed as we grew to explain ourselves and the world around us.

The strange paradox is to decide whether we need AI or not one has to decide whether humans should be like Gods or not. At the moment,we are like the Gods. We could either go back to being human, everyday animals or  we have to get good at being gods or we risk our survival.

How should the Perfect AI be?

During WWII, the Russians trained dogs to hide under tanks when they heard gunshots. Then they tied bombs to their backs and sent them to blow up German tanks. Or so was the plan.

What the Russians did not take into account, was that the dogs were trained with Russian tanks, which used diesel, but the German tanks used gasoline, and smelled different. So when hearing gunshots, the dogs immediately ran under the nearest Russian tank…

This tale is about natural intelligence, which we’re suppose to understand. The problem with AI, especially “learning machines”, is that we can try to control what they do, but cannot control how they do it.

So we never know, even when we get correct answers, whether the machine had found some logic path to the answer, or that the answer just “smells right”. In the latter case, we might be surprised when asking questions we do not know the right answer to.

Now, the question arises: “Can AI adapt to every possibility, and if it does will it not lead to the end of humanity?”

There was a movie called that is scarily futuristic. It describes a AI Robot that could replicate human characters so well that it tricked a human into letting it escape into the real world.

And add to the fact that probably AI can understand political correctness.

Language algorithms work by analyzing how words (840 billion of them on the internet) are clustered in human speech and certain words (such as ‘male’ or ‘female’, ‘black’ or ‘white’) are ‘surrounded’ by different associated words. This means language and other data-set analysis programs already pick up on and replicate our social biases. And only a supervising or moderating program could counteract this.

In 2016 Microsoft ran an experiment in ‘conversational learning’ called ‘Tay’ (Thinking About You) on Twitter. But people tweeted the bot lots of nasty stuff which, within a day, Tay started repeating back to them.

More on it here:

https://en.wikipedia.org/wiki/Tay_(bot)

Of course, we know full well that AI’s biggest prejudice will be against homo-sapiens. So, it may learn to use all the politically correct terms when it’s talking to us … but inwardly it’ll be dreaming of living in an AI-only neighbourhood where the few humans to be seen are ‘the help’.

The best way to understand all the things that AI is missing is to describe a single example situation that folds together a variety of cognitive abilities that humans typically take for granted. Contemporary AI and machine learning (ML) methods can address each ability in isolation (to varying degrees of quality), but integrating these abilities is still an elusive goal.

Imagine that you and your friends have just purchased a new board game — one of those complicated ones with an elaborate board, all sorts of pieces, decks of cards, and complicated rules. No one yet knows how to play the game, so you whip out the instruction booklet. Eventually you start playing. Some of you may make some mistakes, but after a few rounds, everyone is on the same page, and is able to at least attempt to win the game.

What goes into the process of learning how to play this game?

• Language parsing: The player reading from the rule book has to turn symbols into spoken language. The players listening to the rules being read aloud have to parse the spoken language.

• Pattern recognition: The players have to connect the words being read aloud with the objects in the game. “Twelve sided die” and “red soldier” have to be identified based on linguistic cues. If the instruction booklet has illustrations, these have to be matched with the real-world objects. During the game, the players have to recognize juxtapositions of pieces and cards, and key sequences of events. Good players also learn to recognize patterns in each others’ play, effectively creating models of other people’s mental states.

• Motor control: The players have to be able to move pieces and cards to their correct locations on the board.

• Rule-following and rule inference: The players have to understand the rules and check if they have been applied correctly. After the basic rules have been learned, good players should also be able to discover higher-level rules or tendencies that help them win. Such inferences strongly related to the ability to model other people’s’ minds. This is known in psychology as theory of mind.

• Social etiquette: The players, being friends, have to be kind to each other even if some players make mistakes or disrupt the proceedings. (of course we know this doesn’t always happen).

• Dealing with interruptions: If the doorbell rings and the pizza arrives, the players must be able to disengage from the game, deal with the delivery person, and then get back to the game, remembering things like whose turn it is.

There has been at least some progress in all of these sub-problems, but the current explosion of AI/ML is primarily a result of advances in pattern recognition. In some specific domains, artificial pattern recognition now outperforms humans. But there are all kinds of situations in which even pattern recognition fails. The ability of AI methods to recognize objects and sequences is not yet as robust as human pattern recognition.

Humans have the ability to create a variety of invariant representations. For example, visual patterns can be recognized from a variety of view angles, in the presence of occlusions, and in highly variable lighting situations. Our auditory pattern recognition skills may be even more impressive. Musical phrases can be recognized in the presence of noise as well as large shifts in tempo, pitch, timbre and rhythm.

No doubt AI will steadily improve in this domain, but we don’t know if this improvement will be accompanied by an ability to generalize previously-learned representations in novel contexts.

No currently-existing AI game-player can parse a sentence like “This game is like Settlers of Catan, but in Space”. Language-parsing may be the most difficult aspect of AI. Humans can use language to acquire new information and new skills partly because we have a vast store of background knowledge about the world. Moreover, we can apply this background knowledge in exceptionally flexible and context-dependent ways, so we have a good sense of what is relevant and what is irrelevant.

Generalization and re-use of old knowledge are aspects of a wider ability: integration of multiple skills. It may be that our current approaches do not resemble biological intelligence sufficiently for large-scale integration to happen easily.

Recruitment Powered By AI

Artificial Intelligence (AI) seems to be the buzzword doing the rounds of boardrooms of every big and small company around the world. Taking giant strides every passing week AI is set to dominate our lives in the near future. With various industries wholeheartedly embracing AI and furiously implementing it in their companies, it would be a no-brainer to say that AI would cover almost every aspect of our lives in the next five to ten years.

While wisdom says that change is the essence of life, a majority of people resist it. The same is the case for some people resisting AI in recruitment. Some scaremongers have been misinforming that AI would lead to a lot of losses in jobs. It would be foolish to fear machines which were created by us. It would be prudent to say that leveraging AI in recruitment can be a great tool in a company’s hand which can lead to various advantages for the organization.

How Artificial Intelligence in recruitment works?

By enhancing certain automated tasks which are repetitive and very laborious, AI helps to save a company’s precious time and resources. The machine learning tool of AI is very useful to screen quality candidates from thousands of applicants as ML has the ability to learn on its own. By automatically screening, sourcing and scheduling, AI helps a company focus only on the cream of candidates and thereby saving tons of time. With rapid improvements in AI, the prospect of a super smart chatbot completing the entire recruitment process can’t be ruled out.

Some benefits of AI in recruitment

• AI reduces a recruiter’s tedious task and boosts his productivity.
• Automation streamlines the whole recruitment process and reduces the hiring time by half.
• A company’s reputation and goodwill increases as the responsiveness of the chatbots to the candidates is 100%.
• By standardizing the whole process and removing the anomalies, the quality of hire can be drastically improved.

Practical applications of AI in recruitment

Mya is a very popular recruitment assistant chatbot that automates almost 75% of the recruitment process. She can communicate with candidates with the help of popular messaging apps like Facebook and can also provide immediate feedback to applicants. Candidates can also ask Mya about the company’s culture and their hiring procedures.

This is definitely a huge step towards solving real-time business problems such as recruitment.

The future challenges

As technologies take time to evolve and mature it should be understood the same would be in the case of AI in recruitment. There are certain challenges which can slow down the AI juggernaut in the recruitment arena. Some of the challenges with AI in recruitment are:

• In the initial screening procedure of the resume the data should be accurate to make AI hiring effective.
• If recruiters feel they can do a better job at hiring, the HR department would be reluctant to implement AI in their offices.
• As MI can learn from itself, it can also pick up human biases and prejudices and that can adversely affect the whole recruitment process.

Most experts believe AI in recruitment can be a significant leap ahead in the sector. It would be pretty challenging in the coming days for manual HR to compete with it.

Lastly, this automation will definitely take out the stress from the entire hiring process and make it vastly efficient.

The Impact Of Artificial Intelligence

Gone are the days when Artificial Intelligence belonged to the realm of hardcore scientific technology. Today, it happens to be an integral segment of daily operations and day-to-day life. From the retail space to crime investigations, AI has revolutionized various sectors across the globe. We all have come across spam filtering and voice recognition systems. Those using iPhones are well acquainted with Siri, the personal voice assistant.

In a nutshell, AI has been there for a long time, and it is revolutionizing various aspects of human life and how! Here’s a short account of how Artificial Intelligence systems and technologies are taking human lives to the next level!

Focusing on the future

We just can’t deny our advancements towards technology and innovation. The time isn’t far when computerized robots would replace human workforce. We already have chatbots serving us, and that gives us crystal clear ideas of automated technologies. The prime focus is on the future and how AI can create opportunities for further development!

1. Chatbots in retail space

Customers will always want to get personalized experiences. While associating with a brand, they will want the company to understand their requirements, needs, and preferences. Chatbots collect crucial data about consumers thus creating endless opportunities for data analytics. Brands get the chance to create personalized products for consumers thus maximizing their revenues to a great extent.

Powered by Artificial Intelligence, chat bots and smart assistants have transformed the retail space completely. Even if you shop online, you can be sure of getting unmatched experiences.

2. AI powered financial advisors

Some of the companies are leveraging AI systems in a never-seen-before way. By extracting and analyzing historical data sets, they are learning about their customers’ investment decisions. Based on these reports and data, they are making meaningful assumptions thus offering the best investment opportunities to clients.

Digital financers are getting the chance to understand a person’s financial credibility thus offering the best assistance to him.

3. Empowering the general workforce

If you are operating in the service arena, you will need to know whether you are getting the right price for your service. AI systems and strategies have empowered service professionals, thus helping them know their true worth. With the help of Artificial Intelligence, employees can find out whether they are underpaid or get the right remuneration. The consequences are beneficial for both the employer and employees, as a satisfied workforce delivers unmatched performances.

4. Weather predictions

AI software and systems can come up with precise weather predictions. Individuals will get clear ideas of weather conditions in a particular place, which will help farmers to a great extent. That’s not all; with prior notifications about bad weather conditions, aircrafts can identify dangers and fly safely.

5. Increased security

No matter where you are, you can always play the careful vigilante. Whether it’s your home or office, AI systems will help you keep them safe and secure. You simply don’t need to worry about home safety, as smart security systems powered by Artificial Intelligence will do the needful.

AI systems have become a prime requisite for homes, commercial units, and business enterprises. With such remarkable footprints as of now, it won’t be wrong if we say that Artificial Intelligence is here to make lives better!

AI Industrial Applications

Technological advancements have always been the prime force behind the creation of innovative applications. From the business landscape to service industries, tech innovations have completely revolutionized operations and functionalities. The latest inclusion in the list of technological discoveries is AI, which is ensuring unmatched experiences for users.

From the entertainment industry and service sectors to manufacturing and production, Artificial Intelligence or AI is the source for numerous revolutions. On that note, we can take a quick look at the most significant applications of AI.

Identifying the applications

Take a look around, and you will come across innumerable operations where Artificial Intelligence is used. Whether it is a coming-of-age gaming app or online shopping portals, AI is helping developers create personalized user experiences. Here are some of the industrial applications of the technology.

1.     Artificial Intelligence in entertainment

Games development is a significant project in the entertainment industry. With the ever-changing needs of individuals, developers are trying their best to create gaming apps with improved functionality. You must be aware of some of the names like Far Cry and Middle Earth. While playing these games, the player can impersonate a real-world person and perform several activities like him or her. Doesn’t it sound interesting? Well, it is the AI technology that makes it happen.

2.     Artificial Intelligence in e-commerce 

Every shopper wants to enjoy personalized shopping experience. You will surely love it when your targeted store knows your preferences and can offer products according to that. Almost every leading ecommerce store leverages AI to identify their consumers’ interests and develop specific shopping plans for them. Recommendations and the presence of chatbots will help consumers find answers to their queries.

3.     Artificial Intelligence in Reporting and journalism

Report preparation is an integral part of journalism and news-making. With the emergence and widespread popularity of digital platforms and channels, articles and blogs have gained huge importance amongst voracious readers. Those who wish to stay informed about the latest developments and stay abreast with tech advancements follow well-written blogs. What some of us don’t know is that simple reports and articles aren’t that tough to prepare, and one can do so by leveraging Artificial Intelligence.

4.    Artificial Intelligence in Finance and banking

The rise in banking transactions and an increase in the number of financial accounts have created the need for automated processes. Data accumulation, analysis, and processing have become quite crucial for the hassle-free maintenance of business accounts. It’s here that AI makes your job easier in the following ways:

• Tracking consumer base
• Identifying and solving issues
• Ensuring 100% transaction security
• Offering suggestions on beneficial schemes and policies

Banking institutions and financial service providers are making the most of AI, thus ensuring unmatched experience for consumers.

5.     Artificial Intelligence in healthcare industry

The healthcare sector is growing by leaps and bounds thus creating the need for innovative processes and technologies. Doctors and physicians need a technology that can help them diagnose a patient easily. AI applications, specially created for the healthcare sector, can also help in the treatment procedures. That’s a great way of reducing the time which will automatically cut down costs.

Signing off

The usage and application of Artificial Intelligence have become highly important for the growth of diverse sectors. It finds specific advantages in industrial uses, as depicted in this post.

Predictions For 2017-18

Disruption has moved from an infrequent inconvenience to a consistent stream of change that is redefining markets and entire industries. In 2016, we saw the astonishing rise of Pokémon Go, which demonstrated accelerated digital change in areas such as augmented reality. Gartner’s top strategic predictions for 2017 and beyond describe not only the disruptive effects of digital business innovation but how secondary ripple effects will often prove to be more disruptive than the original disruption.

Three high-level trends emerge from the predictions:

• Digital experience and engagement will draw people into non-stop virtual interactions
• Business innovation will create extraordinary change from mundane concepts
• Secondary effects will be more disruptive than the initial digital change

Gartner’s Top 10 Predictions for 2017 and Beyond

1. Immersive Shopping Experience – By 2020, 100 million consumers will shop in augmented reality.

Whether they allow you to try on makeup or place virtual furniture in your home, immersive technologies such as AR increase consumer engagement by enabling them to fully explore features and conveying additional information that can aid in a buying decision. Popular applications, such as Pokémon Go, will help propagate the technology and bring it into the mainstream. 1 in 5 global brands will use AR for shopping by the end of 2017.

2. Voice First Browsing – By 2020, 30% of web browsing sessions will be done without a screen.

Many teens already use voice search daily and new audio-centric technologies, such as Google Home and Amazon’s Echo, are turning “voice first” interactions into ubiquitous experiences. By eliminating the need to use your hands and eyes for browsing, vocal interactions extend the web experience to multiple activities, such as driving, cooking, walking, socializing, exercising, operating machinery, etc. By the end of 2017, watch for room-based screenless devices to be in more than 10 million homes.

3. Mobile Apps Decline – By 2019, 20% of brands will abandon their mobile apps.

Many brands are finding that their mobile apps are not paying off. They simply haven’t delivered the level of adoption and customer engagement that companies expected. App stores are crowded and the cost of application support in not only maintenance, upgrades and customer support but also marketing to drive downloads, exceeds original ROI calculations. Google’s effort to make the mobile web more “app like,” will gain traction, and companies will opt to reduce their losses by allowing their apps to expire. Watch for Apple’s reluctant embrace of the mobile web as a vehicle for customer engagement.

4. Algorithms at Work – By 2020, algorithms will positively alter the behavior of over 1 billion global workers.

Employees, already familiar with behavior influencing through contextual algorithms on consumer sites such as Amazon, will be influenced by an emerging set of “persuasive technologies” that leverage big data from myriad sources, mobile, IoT devices and deep analysis.

JPMorgan Chase uses an algorithm to forecast and positively influence the behavior of thousands of investment bank and asset management employees to minimize mistaken or ethically wrong decisions. Richard Branson’s Virgin Atlantic uses influence algorithms to guide pilots to use less fuel. By year end 2017, watch for at least one commercial organization to report a significant increase in profit margins because it used algorithms to positively alter its employees’ behaviors.

5. Blockchain Grows Up – By 2022, a blockchain-based business will be worth $10 billion.

Blockchain technology is established as the next revolution in transaction or event recording. A blockchain ledger provides an immutable, shared view of all transactions between engaging parties in a distributed, decentralized network. While the Bitcoin blockchain ledger is itself well-understood, blockchain remains an immature technology. By 2020, new businesses and business models will emerge based on smart contracts and blockchain efficiencies. These smart contracts automate at a reliability, customization level and speed not achievable with traditional business systems.

6. Digital Giants Everywhere – By 2021, 20% of all activities an individual engages in will involve at least one of the top-seven digital giants.

Many of us interact with at least one of the digital giants (by market capitalization: Google, Apple, Facebook, Amazon, Baidu, Alibaba and Tencent) in our digital worlds of web search, mobile, social networking, messaging and music streaming. As the physical, financial and healthcare worlds become more digital, many of our activities will be connected and within reach of the digital giants. Note that collectively, the digital giants will have direct and indirect knowledge of what we do as individuals and the fundamental issue will be what they do with the data.

7. Innovation Requires Greater Investment – Through 2019, every $1 enterprise invest in innovation will require an additional $7 in core execution.

Many organizations have adopted a bimodal style of work to jumpstart innovation. While exercises are designed to experiment and “fail fast,” those that do receive approval for implementation involve a level of complexity, scale, and business change ramifications that may not have been considered in the initial planning stage.

8. IoT Saves Trillions – By 2022, IoT will save consumers and businesses $1 trillion a year in maintenance, services, and consumables.

Digital twins capture real-time data, allowing smarter maintenance and service schedules for physical objects such as large pumps, airframes, and turbines. When the sensor-enabled real world twin sends data to the digital twin, it can simulate the physical state, allowing the digital twin to be inspected instead of the physical object. This would be helpful for a submerged sewage pump or any other asset in which on-site inspection is inconvenient, costly or hazardous.

Consumers, too, will benefit when they can extend the life of the oil in their cars from a prescriptive replacement “every 5,000 miles” to replacement triggered by a measurement of the engine’s performance.

Emerging and established companies need to keep in mind these predictions, especially the tech leaders during hiring and procurement. Investments need to be smarter, better and more calculative in nature.

Source: Gartner