Artificial Intelligence Trends in 2018

Artificial Intelligence (AI) has soared to unbelievable heights in recent times, and even today, with tech giants like Google and Microsoft making constant advancement in this field, AI is almost everywhere. Needless to say, there will be many things that AI will learn to do in 2018, and make the lives of people much easier than it already has. Though we don’t yet have flying cars and floating buildings, like in movies, we do have some great AI like Alexa and Google Assistant.

So, the question that arises now is – what is the future of AI in 2018? Well, some mighty intellectual people may think too much into the future regarding what things AI will be able to do, but for now, let’s you and me focus on what we can expect from AI in 2018.

1. AI will become more Human-Like

In today’s busy world, where manpower itself is not enough to handle the ever-increasing customer demands, many companies are using AI in the form of chatbots and automated answering machines to make their institutions run smoothly. Customer assistance is a major field where AI is said to make a huge progress.

However, it is likely for some people to feel weird listening to a robotic voice talking to them, which is why Artificial Intelligence companies have been working towards making their products more and more human-like, so as to encourage people to use it. AI’s like Amazon’s Alexa is a great example of a personal assistant.

2. Voice Recognition will become much Better

Almost all the smartphones today have a voice recognition technology installed in them, which enables you to talk to the AI and set appointments, call someone, set alarms, and much more. Siri and Google Assistant are some good examples.

However, it becomes irritating sometimes, when the voice recognition doesn’t hear you correctly when voice typing, and you have to edit the whole message again. This is said to change in 2018, as AI companies and their voice recognition tech is said to improve by folds.

This means that your smartphone assistant will hear your commands better, and type your voice message better.

3. Machines will be Data-Driven

Machine Learning has become necessary in today’s time. The immense growth in AI and the IoT (Internet of Things) has made companies invest capital and workforce towards advancing their AI functionalities.

This may sound crude, but AI has become essential for companies to maintain their data flow and important company data, and an AI functionality has become a vital part of every company’s functionality, and that will continue to grow in 2018. Artificial Intelligence functionality has been broadened to new horizons by all the data provided by Internet Of Things, and it will continue to grow exponentially.

In 2018 and beyond, there are a lot of advancements in AI to look forward to. While it may go mainstream in many sectors, others will have a more cautious approach before embracing the technology.

Artificial Intelligence in the Banking Industry

Artificial Intelligence has taken the world by storm and has been advancing rapidly in recent times. It has shown a remarkable potential to augment human efforts and free them up from routine tasks so that they can focus on being better in strategizing and doing complex activities.

In today’s world, almost every aspect of life and business has the potential to be disrupted by AI. It is no wonder that the AI market is expected to surge past the hallowed $100 billion mark by 2025.

How will the banking sector be affected?

AI has had an effect on almost all the industrial sectors, and the banking sector is also one of them. Banks have started using AI for multiple purposes to make their institutions operate smoothly as AI bots can run all round the clock and do the jobs assigned to them flawlessly. The banking sector is using AI increasingly and so, here are 4 ways AI is impacting the banking industry.

1. Improved and Cost-Effective Customer Service

In today’s ever-growing corporate world, it is almost impossible for a human being to bear the burden of all the customers calling the bank’s support helpline. This is where AI plays an essential role.

Apart from being available to customers all round the clock, AI has drastically reduced the manpower and money required for customer service. This has majorly benefited the finance industry.

2. Better Management

Before the advent of AI, companies used to ask advice from bank experts as to how they can maximize profits and minimize taxes. However, it’s in the nature of humans to be imperfect, which is why the predictions were not correct mostly.

Now, customers who are looking for advice can directly ask the bank’s AI program about any questions related to their company. The AI-powered solution can provide a full report with all references and facts, thus helping both the bank and the company.

3. Know your future prospects and returns

With a targeted AI solution, you can keep getting continuous updates on various offers available and build on your current assets to increase your returns. Also, you don’t have to start from scratch as the AI will do all your work for you. Once you have an AI system in place, it will keep your account safe from market fluctuations.

4. Precise investment information and research

The finance sector is a volatile one, and many a time, there are crucial decisions that need to be made. In this case, it is but natural to choose the expert programming of an AI over human predictions and trust the AI’s continuous learning methods to forecast better.

If a bank has an AI systems in place, it can provide you with all the research and reference along with exact facts and figure to help you make the best possible decision. This makes AI an invaluable asset in the financial sector. Investment decisions are very crucial as customers may lose their trust over a bank in case the advisor makes a wrong decision.

Thus, AI has become a vital part of the financial sector and will continue to be, in the future. With so many benefits being derived from the industry it is natural that AI will find increased adoption in the months to come.

Skills Artificial Companies should invest in

If we take a look at the present market trends, it will be easier to get an idea of immense growth of AI . This growth spans companies across industries, maturities, and competencies. With many startups, established ventures, and tech giants gearing up to embrace AI technology, there’s no denying its bright and promising future.

Amidst all this hype around AI, one can identify its rapid adoption. It’s true that a lot of companies belonging to diverse sectors have adopted AI methodologies.

Let’s take a look at some market stats that explain the situation better!

Market statistics

According to reliable market reports, 38% of ventures are already leveraging AI. This is done with a goal to accelerate growth and development. By the end of 2018, the figures will rise to 62%. That explains why AI is important and crucial for ventures across the globe.

However, with the ever-growing importance of Artificial Intelligence and increasing market demand, it’s high time to identify ways to improve it.

The top 4 AI skills

If you are running an AI organization, you must be aware of the skills that empower ventures across the globe. Here’s a quick look at the top four skills every AI Company should invest in:

1. Natural Language Generation or NLP

Natural Language Processing is an innovative technology that helps machines to understand language the way it is spoken or understood by humans. Different approaches like distributional, frame-based semantic analysis, or interactive learning will help upsell your NLP experts’ skills within the AI ecosystem.

2. Chatbots

When it comes to ensuring excellent customer service, most of the ventures opt for chat bots. Chat bots can connect with human intelligence thus helping organizations develop a unique relationship with their customers. Knowledge on AWS S3, AWS ML, jQuery, JavaScript, SQL, and PHP will be vital in developing chatbots.

3. AI-centered hardware

Appliances, graphic processing units, and other hardware units should be designed, developed, and created with AI in mind. Companies should develop hardware systems that are appropriate to perform AI-centered computational tasks. Hardware units are as important as software skills. Quite naturally, it becomes imperative to develop and design them with AI in mind.

4. Biometrics

You must have heard the term ‘biometrics’ while checking out the staff attendance systems in enterprises. AI is growing and that too at a rapid pace. Quite naturally, organizations should enable increased interactions between machines and humans. The ideas shouldn’t be only restricted to speech, body language, and touch recognition. Biometrics encompasses a broader perspective and positive interaction is a crucial part of it.

Final thoughts

Artificial Intelligence processes, technologies, and methodologies are talks of the town. There’s no denying the buzz they are creating in the professional arena. AI companies should strive hard towards developing skills that empower organizations. This will help them deliver immense business value.

You have to identify development opportunities and incorporate the right strategies for the right set of business needs. Develop these skills if you are running an AI organization; they will take your venture to unsurpassed heights!

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. These insights can significantly enhance the effectiveness of digital marketing services.

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-powered recruitment software helps a company focus only on the cream of candidates and thereby saving tons of time.

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.