How Machine Learning can help with Business Development

Accelerated business growth has always been about innovation in functionalities such as customer experience, employee management, and others. And with advanced machine learning technologies, businesses are now able to make useful changes to bring better results for their companies.

The biggest advantage of machine learning in the corporate sector is the ability to make automated decisions without taking risks in any manner. This is why the corporate sector is expecting $59.8 billion revenue with machine learning and AI by 2025. More and more companies are integrating machine learning into their businesses to innovate various aspects.

Here are the top 4 ways that businesses can leverage machine learning.

1. Bringing personalization to customer service

Businesses keep on looking for effective ways to improve the quality of customer service and reduce the investment requirements. And machine learning offers those exact solutions to obtain those goals. With ML technologies, businesses get the ability to combine their years of data related to customer services and merge it with natural language processing technology.

The NLP algorithms make customer interactions more personal by leveraging the data to provide satisfactory services. Each and every customer gets the most accurate answers to their questions, which makes them happy. Plus, the same technology reduces the need for too much investment, resulting in lower customer servicing costs.

2. Making recruitment process convenient and successful

For a long time, hiring and recruitment processes faced multiple struggles. The difficulty in shortlisting the right candidates, removing the human biases, asking the right questions and keeping it cost-effective have presented troubles for recruiters.

But now, with machine learning, it is possible to bring automation in the hiring process. Corporates are now able to shortlist candidates among thousands of applications without skipping a valuable candidate. The machine learning tools are able to analyze credentials and match them with relevant job profiles.

Also, the same technology can detect biases and remove those factors while conducting the assessment. It all makes machine learning a cost-effective and successful way of hiring people.

3. Improving finance management and handling methods

Machine learning also offers the capacity to manage financial processes of a company. In fact, the processes such as payment, invoice analysis, and others can become automatic with machine learning.

A huge number of invoices can be analyzed in no time. The companies can reduce their efforts and time on managing their finances and save a lot of cash too. Plus, the security of machine learning technologies provides protection to the processes at the same time.

4. Marketing and Management

Marketing and management can also get innovative results with machine learning. The AI tools are already being used in gathering customer data, supply chain management, and other processes.

Companies are leveraging machine learning tools to find data related from social media about products, logos, and other factors. All this data is used to create a better brand exposure and to get successful outcomes.

All in all, AI and machine learning offers innovation to almost every part of a business. So, it would be wise to integrate right tools and algorithms to improve ROI and make your business a success.

Artificial Intelligence in Social Media Marketing

It is increasingly being proven that AI is helping marketers find leads and engage like never before through social media. Targeting social media users over the online space is a highly specialized and complex field. Artificial Intelligence (AI) can be a potential catalyst that can drive the fortunes of digital businesses across the globe.

Why the need to introduce AI into marketing?

73% of B2B users need to be nurtured for quite some time before they are converted to active leads for the business. In a traditional ecosystem, marketers simply cannot keep pace with the lead management scenario that evolves daily for each and every enquiry. AI can help devise and deliver personalized content to nurture leads to a better degree.

How does this happen?

Marketers can leverage the basic principle of AI to understand human psychology and its implications in the real world scenario. When marketing automation uses AI, it helps understand and monitor various aspects of customer behaviour like –

1 – How they spend their time online

2 – What posts or products get the most visibility from them on social media

3 – What do they use the social media for

They combine this with past marketing campaign data and then build appropriate marketing messages that have good potential for success. Here are some ways in which this can be done.

1. Better insights on CRM

Bits and pieces of useful information may lay hidden in plain view in various mediums like emails, phone calls, or social media posts/comments. With Artificial Intelligence, you get the right hints about what steps can be taken to subtly induce the prospect to move up the sales funnel towards successful conversion.

With the presence of sufficient amount of data, you can also configure a sentiment analysis activity to grasp the purchase goals/motivators behind the words used by the prospects on social media.

2. Align social media content with buyer persona

With a structured buyer persona, a marketers’ targeting efforts yield better fruits. AI can help smartly segregate customers into personas for a better level of personalized marketing. AI can also learn about which type of content (blog posts, whitepapers, videos, case studies, or other marketing collaterals) will help which type of personas at a particular level in the sales funnel. This way, customers will find only the content which is relevant and timely to their particular need.

3. Social sentiment analysis

With continuous social media analysis a viable brand analysis picture can be formed. However, keeping track of all the posts and ads and analyzing these on different platforms can be complex.

It eats up a lot of time for the marketer which could have been otherwise used to push fresh and unique content for the readers. This is where AI comes in. Take NY Times’ case – it has been using chatbots in innovative ways to create a deep one-on-one experience for its readers.

Thus, with astounding levels of competition to capture eyeballs on the social media, these advantages prove that AI adopters will have an upper hand in this regard.

Natural Language Processing tools for Chatbots

Language understanding tools have the capacity to make chatbots smarter. These tools are designed to enhance the communication capabilities of the chatbots. The ability to understand the sentiment, create an automatic summary and find a relationship between the topics. All these abilities can make your chatbot much more effective for the users.

Here are 5 NLP tool choices that can help your chatbot deliver high impact performance in customer servicing.

1. LUIS

Microsoft offers cognitive services to provide language intelligence and other capabilities. The LUIS or Language Understanding Intelligence Service offers high-quality models that help chatbots understand various entities and intents. The availability of the models related to times, places and others enhance the performance of chatbot. The users can get much better experience, as an active understanding of the language is used.

The tool is compatible with various platforms such as KiK, Slack, Facebook Messenger, Skype, SMS, and others. You can get both free as well as paid versions of the tool.

2. RASA NLU

When you are looking for an open source method of classifying the intent, RASA NLU is your answer. The tool offers a complete set of APIs that make entity extraction highly convenient for the chatbot.The libraries come along with the tool that enhances the capacity of any bot. The corporate sectors like health, insurance, travels, telecoms and banks get the maximum advantage of this tool.

The tool is available for free and works on platforms such as Facebook Messenger, Telegram, SMS, Skype, Line, and others. This is why the tool has gained an incredible level of popularity in multiple sectors.

3. Amazon Lex

The tool can provide high-quality language capabilities to your mobile applications. The specific properties include natural language recognition and speech recognition too. Hence, the chatbot can provide both text and voice experience to the users.

This will improve the ability of the bot to assist and help the users conducting various tasks such as placing orders, opening an account, making bookings, and others. The tool offers a simple console, which makes the processes much more convenient for the chatbot.

4. API AI

Known as one of the most trustworthy language understanding tools, API AI makes brand specific interactions easier. The businesses can leverage this tool to create bots that can provide multiple services to the users.

The conversational interface with this tool takes not much effort. Plus, the powerful features of the tool allow the bots to answer highly complex questions asked by the users. The bots get to leverage the stored knowledge along with the machine learning. Hence, the bot learns and gets better.

Some other valuable features include multilingual interactions, cross-platform support, easy integrations and others.

5. ChatScript

ChatScript allows you to create a script for the dialog conducted by the chatbot. The tool uses the scripting rules to create extensive texts. Plus, it scans documents, memorizes old interactions and manages a large volume of users.

All the mentioned tools have their own set of properties. You can add these tools and enhance the capabilities of your chatbot.

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

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!

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.

Artificial Intelligence (AI) – A Great Marketing Tool

Effective marketing is the key to perfect branding in the business world, and numerous businesses owners across the world strive hard to take their brands to the professional arena. Here social media comes across as a valuable tool for businesses.

With 91% of the brands utilizing 2+ social media channels, and almost $8.3 billion in social media generated in 2015, this has become the primary channel to acquire users and improve brand visibility on the digital ecosystem.

In order to extend the dramatic business opportunities presented by a good social media strategy, businesses are increasingly adopting Artificial Intelligence (AI).

AI – A quick background

Changes in the tech world and business landscape led to the emergence of innovative marketing strategies and campaigns. MAPs or marketing automation platforms are the best examples of such remarkable innovations. If market reports are anything to go by, a whopping 91% of marketers believe that MAPs had been highly successful and effective in promoting brands.

We are in a consumer-centric business world where every company or establishment is working on consumer-oriented business models. Quite naturally, SMM or Social Media Marketing has emerged as one of the most effective business advertising strategies. The latest inclusion to this list is AI or Artificial Intelligence.

Ingenious and creative marketers in the B2B arena are including AI strategies while creating business marketing campaigns. Let’s find out how Artificial intelligence is helping marketers create better SMM strategies.

Harnessing the benefits of AI

How would it be if computers had the power to comprehend certain aspects and actions of the human world? That would be great, and this is what AI ensures for you. Artificial Intelligence refers to the computers’ ability of comprehending actions in the natural world. Business marketers simply can’t ignore the potential benefits offered by AI.

The idea of integrating AI into existing business marketing models is undoubtedly fascinating. Artificial Intelligence is a compelling proposition and a highly potent tool, and here’s how it can be used to improve business marketing.

1. Create a personalized experience for your customer

Consumers want special treatments and personalized shopping experiences. As a brand owner, you will have to offer the right set of products or services to the appropriate buyer group. It’s here that you need to identify and understand buyer personas. Artificial Intelligence not only helps you understand these buyer personas but also conforms your marketing contents to those insights. You gain the opportunity to create personalized experiences for your target audience.

2. Real-time social media interactions

When it comes to captivating the attention of your target prospects, none other than popular social media channels will prove to be effective. With refined AI strategies, you can build real-time and effective communications with potential leads and prospective consumers.

3. Analyze customer behaviour

Business marketers often have to mine effective Customer-Relationship-Management tools to understand consumer behaviors and preferences. AI will help you analyze emails, phone calls, and social media contents thus gaining targeted insights into aspects capable of driving your prospects to take decisions.

Irrespective of the business size or sector, driving a deeply personalized consumer experience is a crucial necessity. Artificial Intelligence can help you ensure an awesome CX for your target prospects. In case you haven’t invested in this technology for your business yet, it is time you start thinking on these lines.