Artificial Intelligence’s Role in Customer Service

Customer service has always been a vital aspect of any business and often dictates the manner in which a company is perceived in a market. For today’s businesses, however, customer service is a much more important process which also serves as a comprehensive part of their sales and marketing strategies. Being a continuous and often mechanical process, most aspects of the customer service process are being automated. A study by Oracle from 2016 reports that approximately 8 out of 10 businesses in the world today have either switched or are planning to switch to Artificial Intelligence for their customer service needs by the year 2020. The recent developments in AI have largely attributed to a surge in investments into the technology. For customer service, there are various aspects that today apply AI and automation on some level.

AI beyond Chatbots

The most common features of any website today are chatbots. Chatbots are those small pop-ups that appear when you log on to a website assisting you with the basic queries. As of now, that is the extent of their purpose. Responding to FAQs from a pre-programmed set of answers and contingencies, Chatbots could be considered the beginnings of Artificial Intelligence, although calling them that would be a gross overstatement. Yet, chatbots have allowed companies to improve the efficiency of their customer service teams. Being available 24/7 they offer an edge for companies in maintaining a strong presence in the market. No matter how advanced AI may be than chatbots, it is meant to serve only these two purposes and all AI designed for Customer Care function along these lines. However, AI adds layers to those functions often allowing for several functions that involve augmented AI-Human coordinated efforts.

AI-Assisted Human Agent

This is a model that companies follow as an alternative the Chatbots. This model offers the best of both worlds as human agents work hand in hand or rather hand on the mouse. Here the AI serves as a relay between the customer and the human agent. Using data and predictive analysis the AI analyses various previous conversation pertaining to the query in question and generates a custom answer based on the current inputs from the customer. The human agent then edits the answer to make it sound more ‘human’ and delivers it in a simple and understandable manner. After each query is answered the AI learns from the edited answer offered by the agent to enhance its own database the next time it answers. The advantage of this model is its versatility across platforms as well as media.

Unlike chatbots, this model is not limited to text-based queries and can function on a voice level as well. By applying Natural Language Processing, the AI recognizes human features associated with a voice to authenticate calls as well as make suggestions. It is predicted that in the next couple of years a healthy percentage of companies using voice call based business processes will be enhanced by the use of AI.

Swift Response AI

If we had a rupee for every time we refused our patronage to a service because of bad customer care, all of us would be quite rich indeed. In the digital age, quick and efficient customer care is an essential need and people lose patience and loyalty towards a company when they are made to wait and put through a ton of procedures. The AI today are capable of interacting directly with customers for any requests pertaining to information or service and by drawing from a database, offers appropriate suggestions and processes them. If there are queries that are beyond the AI’s processing capabilities it automatically initiates the process and redirects the customer to the concerned human agent. This swift AI servicing is proving to be quite effective.

AI-Derived Insights

One of the most comprehensive applications of AI in customer service has been data analysis. Where humans often fail in general is when we are expected to see the big picture. We have done this for centuries and it seems we never learn. But, AI today is able to grasp each and every aspect of a conversation, transaction or interaction of any kind and process it to derive actionable insights. By using technologies such as natural language processing and machine learning, AI will thoroughly analyze all data available and checks for anomalies and vital information such as trends and patterns which could be used in the future to improve conversion rates and so on. While not used as widely as the previous two, this form of AI has been quite effective as a business accelerator and it is predicted that with the growing popularity of AI it will eventually become a customer service staple.

Conclusion

While the application of AI in customer service still has a long way to go, the changes it is has brought about so far have been substantial. Despite the ‘Don’t fix it, if it is not broken’ attitude that is symbolic of the customer service market, there has been quite a push for the adoption of AI. Given the various possibilities that AI presents, the customer service industry, for now, is in a safe haven with this technology by their side.

Legal is a complex vertical with its own unique style of functioning unlike any other vertical. So, let us have a look at how Artificial Intelligence could influence the legal sector.

How Machine Learning can affect Economics

Machine learning has found its way into multiple business applications. It makes computing the process more efficient, cost effective and trustworthy. Machine Learning is no longer a fantasy but a set of authentic business technologies that will help the management take better decisions in the long run. Be it Facebook recommending friends tag based on photos or voice recognition systems like Siri and Cortana, Machine Learning helps us to make smarter and better decisions.

The power of machine learning goes mainstream

Studies show that in 2017, ¼ of the respondent companies used 15% of their IT budget on machine learning and have seen phenomenal ROI emanate from it. These numbers are expected to rise in the future because of fast infusion of machine learning into different and diverse industry verticals.

Machine learning is broadly used in the field of life sciences, healthcare, hospitality, and retail. Interestingly, there is one more vertical that machine learning can impact but it is not that popular yet.

It is the field of Economics!

Research shows that machine learning is yet to revolutionize economics the same way it has done for other fields. But it will majorly expand its possibilities and more economists should start to implement machine learning in their studies.

Want to see how machine learning can impact the field of economics? Read on…

How Machine Learning and Economics come together?

When an economist analyzes economic data, they try to figure out the relationship between two dynamic and seemingly unrelated conditions. For example, an economist might sort through real estate data to figure out how much the size, location, or other factors will determine the price people are willing to pay for a home.

Machine learning will not only help an economist figure out the relationship between the user and external factors but also will use that data to predict on how much that same house is worth and what should be expected from potential buyers.

Machine learning will not directly influence economic research but will help economists with the research data and predications.  Machine learning is primarily useful in collecting new sources of data. For example, economists have already been able to convert satellite data into estimates of economic growth, as well as to measure neighborhood income levels in Boston and New York using Google Street View, Yelp, and Twitter.

Another feature of machine learning is it allows economists to analyze language as data. Algorithms can be used to identify news articles too. It can gauge if the sentiment of the text is negative or positive. Brand marketers can then utilize this information to roll out campaigns accordingly.

Machine learning is also useful for testing out economic theories. Economists are working on the theory that share prices should incorporate the most relevant information, thus making it possible to predict future stock market fluctuations.

To conclude

Although machine learning is a new concept, it has a promising future in the world of economics. Some of the above examples show succinctly why machine learning has the (nearly) perfect answer to some of the most complex queries faced by an economist.

How the use of Artificial Intelligence in Education can improve Student Retention at Universities

It is seen that there are scores of university students who enroll in higher education but do not end up obtaining a degree. Since they leave the education midway, they end up not being awarded the degree they had enrolled for. Universities and educational institutions are increasingly looking to technology to address this challenge of student retention. With the help of Artificial Intelligence and Machine Learning, they are seeking a way to improve the retention rates for higher education in countries like US and India.

By analyzing data from forms, educational literature, surveys, and studies, AI can detect the key reasons behind attrition and dropouts. This, in turn, helps the institutions to plug the gap wherever possible and improve their own university rankings by improving the retention rates.

How does Artificial Intelligence in Education work?

A student and an institution exchange volumes of information at every stage of the educational journey – right from initial expression of interest to completion of the programme and awarding of the degree. This helps the AI system to mine data that is of particular importance for tracking retention record of a student.

So, metrics like falling grades or increased absenteeism may provide early indicators of a likely dropout in the future. Once the student advisor or university professor gets an alert of the same, he/ she can counsel the student about ways to overcome the present challenges and continue pursuing the education.

Imagine if the AI system weren’t in place – the institution would’ve never got to discover the likelihood of a student dropping out until it was very late i.e. when the student actually drops out. Rather than taking a reactive stance, AI helps the institution and university to take proactive action and avert attrition from actually happening.

Use Cases in Artificial Intelligence’s impact on Student Retention Rates

There has been an interesting project that saw a public university bring in the benefits of Artificial Intelligence to tackle this very problem of falling student retention. The University of Oklahoma had witnessed a drastic fall in the number of higher education students returning for sophomore year in college. Out of the first-time students who started school in fall 2013, only 64% returned for the second year in fall 2014.

The university worked with IBM (for IBM Watson, its well-known proprietary cognitive computing, and AI system) and analyzed unstructured data like student essays. This helped to assess the tone of language, personality insights, and natural language classification. With this data, the university could better identify potential retention risk students and counsel them before they dropped out.

The outcome of the project was highly positive – from 64.2% in 2014, the retention rate climbed to 86.1% in 2015 and reached 92.1% in 2017.

To sign off

This post shows the exciting potential for Artificial Intelligence to make a truly lasting impact on the education and academics sector. The more universities and institutions embrace AI, the higher will be the likelihood of retention of students into the system.

AI in Logistics Industry

Artificial Intelligence (AI) has become a major topic in almost every business sector. Leaders talk about all kinds of positive impacts that robotics, machine learning, and other AI technologies can make possible. Optimizing these advanced technologies can save time and improve quality as well.

Parcel and logistics is one such industry that has started leveraging AI to influence supply chain and other associated processes.

AI is reshaping almost every procedure of parcel and logistics. Interested to know which areas has it impacted? Read on to know more

1. Automation in productivity

Productivity in logistics doesn’t rely solely on human expertise now. Advanced algorithms and robotics are bringing automation and reducing human errors. Automatic processes provide better quality products in terms of packaging, management and distribution preparation. Plus, they also reduce overall logistics cost for companies.

2. Enhanced delivery models

Automakers and Artificial Intelligence experts are partnering to incorporate best technologies. Logistics and parcel industry can get the best outcome with this partnership. Future presents a chance to incorporate autonomous vehicles, self-driving drones and parcel carriers for delivery. All of these technologies can provide exceptional accuracy and cost-effectiveness to the industry.

3. Efficient route optimization

Most logistics and parcel companies struggle with route optimization. Bad route selection increases fuel consumption and also affects customer satisfaction. Ultimately, it all impacts the cost of delivering parcels.

An AI integrated vehicle can resolve all these problems. Modern self-learning technologies can offer a platform for parcel companies that optimize routes on its own. Technology can find a coordinated and faster route for a delivery. This can reduce about 30 percent of travel distances and also decrease the need for vehicles for about 10 percent.

Machine learning uses data in real-time to provide a dynamic route for delivery teams. It anticipates potential traffic problems, weather conditions, distances and many other factors to decide the most cost-effective routes for delivery.

4. Improved customer experience with chatbots

In recent studies, more than 62% of consumers accepted that they feel comfortable about having a virtual assistant answer their questions. Chatbots can automate and also enhance customer interaction. Both websites and call centers of logistics companies can improve customer experience with immediate and accurate assistance.

5. Delivering intelligent interfaces

Self-learning via Artificial Intelligence is allowing machines to understand vast data related to logistics. Analyzing scenarios in terms of historical data, machines can resolve complex issues related to the supply chain. Machine learning enables them to create intelligent interfaces for automated decision-making.

6. Understanding consumer behavior

Two-way communication is a necessity in parcel and logistics industry. Companies need to know when and why customers need a product. This allows them to understand an overall demand for a product in a particular location. However, manual analysis of consumer behavior seems almost impossible.

On the other hand, technologies are becoming smarter and better in terms of consumer behavior analysis. This is absolutely perfect for the industry, as they can now know why consumers want a product. Anticipating consumer behavior with AI proves much more accurate and efficient. Algorithms take much less time to conduct a predictive analysis and anticipate consumers’ demands.

A rapid growth in AI is ready to empower this industry. Are you ready?!

NLP tools for Artificial Intelligence

There is consistent research going on to improve Artificial Intelligence (AI) so that it can understand the human speech naturally. In computer science, it is called as Natural Language Processing (NLP). In recent years, NLP has gained momentum because of the use of neural networks. With the help of these networks, there has been increased precision in predictions of tasks such as analyzing emotions.

With its advent in the world of computer science, a non-linear model for artificial computation has been created that replicates the neural framework of the brain. In addition, this structure is capable of performing NLP tasks such as visualization, decision-making, prediction, classification, etc.

Artificial Neural Networks that benefit NLP

An artificial neural network combines the use of its adjoined layers, which are input, output and hidden (it may have many layers), to send and receive data from input to the output layer through the hidden layer. While there are many types of artificial neural networks (ANN), the 5 prominent ones are explained in brief below:

1. Multilayer perceptron (MLP)

An MLP has more than one hidden layers. It implements the use of a non-linear model for activating the logistic or hyperbolic tangent function to classify data, which is linearly inseparable otherwise. All nodes in the layer are connected to the nodes following them so that the network is completely linked. Machine translation and speech recognition NLP applications fall under this type of ANN.

2. Convolutional Neural Network (CNN)

A CNN neural network offers one or many convolutional (looped or coiled) hidden layers. It combines several MLPs to transmit information from input to the output. Moreover, convolutional neural networks can offer exceptional results without the need for semantic or syntactic structures such as words or sentences based on human language. Moreover, it has a wider scope of image-based operations.

3. Recursive neural network (RNN)

A recursive neural network is a repetitive way of application of weight inputs (synapses) over a framework to create an output based on scalar predictions or predictions based on varying input structures. It uses this transmission operation by crossing over a particular framework in topological order. Simply speaking, the nodes in this layer are connected using a weight matrix (traversing across the complete network) and a non-linear function such as the hyperbolic function ‘tanh.’

4. Recurrent Neural Network (RNN):

Recurrent neural networks provide an output based on a directed cycle. It means that the output is based on the current synapses as well as the previous neuron’s synapses. This means that the recorded output from the previous information will also affect the current information. This arbitrary concept makes it ideal for speech and text analysis.

5. Long short-term memory (LSTM):

It is a form of RNN that models a long-range form of temporal layers accurately. It neglects the use of activation functions so it does not modify stored data values. This neural network is utilized with multiple units in the form of “blocks,” which regulate information based on logistic function.

With an increase in AI technology, the use of artificial neural networks with NLPs will open up new possibilities for computer science. Thus, it will eventually give birth to a new age where computers will be able to understand humans better.

How AI will lead to the era of Robotics

The concept of robots has been around for so long that it precedes the roots of most modern technology. Automated machines that are capable of doing more menial tasks could be dated back to as early as 4th century BC with steam-powered automatons doing menial tasks.

Back then scholars and philosophers like Homer saw robots as a means of human salvation as it presented the possibility of human equality, slavery being a huge issue at the time. However, a couple of millennia later the take on robotics had a slightly darker undertone. Works of post-industrial novelists such as James Orwell and movies such as ‘The Terminator’ depicted robotics as the end of mankind.

Even early 20th-century technology experts believed that robotics could lead to massive layoffs and could destroy working-class communities and they were not wrong. The first sign of mainstream robotics came in the early 70s when hydraulic arms started taking over production lines at a car and heavy machines factories. Cities such as Detroit and Munich suffered massive layoffs. Yet the use of automated machinery continues with several verticals such as AI built around them.

The State of Robotics Today

Robots have come a long way from their early hydraulic single motion ancestors and are surprisingly doing a lot a lot of things that most humans could only dream of achieving in their entire lifetimes. From Toyota’s Kirobo having a conversation in space to Sophia’s Saudi Arabia Citizenship, robots are going places and bringing certain science fiction theories that were once dismissed as hogwash, to life. With the exception of breaking Asimov’s three laws of course. Nonetheless, robotics is going through huge advances today especially with technologies like AI and Machine Learning catching up quite fast. Certain industries have become so accustomed to robotics that industry veterans now wonder how they survived without them. So, let us take a closer look at the some of those things.

1. The Space Bots:

When the seven Mercury Astronauts were under the threat of being replaced by a monkey, the last thing they would’ve been thinking would be that after 50 years people have to go through the same thing with robots. Well even if they did think that, they would not have been wrong. Because today most of the transplanetary missions are being carried out by rovers and the robots aboard the ISS are beginning to function more and more like their human counterparts. For astronomers and researchers, it is truly hard to imagine sending another human to the moon let alone to Mars.

So, in that respect robots have allowed us to go farther than we would have ever imagined possible and they aren’t just there planting a flag, instead they are drilling on its surface, running tests and sending in chunks of data that would have taken a human a lifetime to collect.

2. Drones:

There is no better field to measure the impact of robotics than the defense sector. Robots have been a huge part of many important military operations in the past two decades. Particularly the Drones guided by AI are capable of flying, targeting and even firing from long range as a staple of the U.S. air force.

However it is not just the terminator style drones that are making the headlines, but recent years have also seen a hike in the number of shopping drones and transport drones. The use of flight-capable drones guided by AI could be argued as one of the largest prospects for the retail industry.

3. Transportation:

Automated transport is a field that has been picking up pace off late. Self-driven cars, locomotives and aircrafts are thought by many experts to be the future of transportation. Every day, AI advancements in transport is showing promising results that could in the near future be translated to mainstream modes of transport.

4. Machine Learning:

One of the most recognizable features in robotics today is the technology known as machine learning. Machines and programmes that are capable of analyzing various patterns of the tasks that they are assigned to and create their own set of algorithms to function around more effectively. The introduction of machine learning to robotics has been one of the largest leaps in the industry. Humanoid robots are making a mark in several areas of the industry with many acting in movies, working as astronauts, therapists, nurses, yes that’s right nurses! Soon the caring feminine touch will be replaced by the cold metal claws of a robot named after the tiny metal gerbils from ‘Thundercats’.

Is the ‘Storm’ really Coming?

Well back in 1984, that line could be dismissed as James Cameron just being crazy but today we are really not in a position to tell. As a matter of fact, something big is bound to happen by the year 2029. Not on Skynet proportions but more on the lines of an AI-based global network that will function all by itself. Coming to think of it that sounds exactly like Skynet but on a more nerdy tone.

Levity aside in the decade to come organizations like SpaceX, Tesla and Google are going to put most of their resources into developing AI technology to such an extent that total automation would be possible. Elon Musk’s dream of setting up a space colony on Mars revolves largely around the prospect of AI technology that would help us from the designing phase of the space crafts to the setting up of habitats on the red planet.

Backed by AI, the possibilities of advancements in robotics are endless. Even the hardware capabilities are going through an overhaul with robots now being equipped to mimic human-like body physics. At this point the possibilities seem endless, but, only time can tell how far we can go with this.   

How AI will shape the future of Internet of Things (IoT)

The Internet of Things (IoT) has been the topic of discussion for the past few years. It seems as though everyday the IT universe is finding new applications for IoT and its mainstream plausibility is becoming more. While considered a brand new vertical with endless possibilities IoT is just an extension of Artificial Intelligence. The very idea of IoT spawned from the prospects that AI has shown in the past. The idea of devices being connected with each other and communicating is something that is truly a remarkable point in human civilization. When we take a closer look, it sheds light on the extent to which AI has grown and the development it has brought about in other verticals.

While the application of AI in other verticals such as robotics, automobile, marketing etc. create reason for argument due to the various threats they pose, IoT is a vertical that at the moment poses no such threat, unless they start transforming into little killer robots and tearing your house apart.

Device Development and AI

Today we have more machines around us than we have human beings. If we introspect it might seem that we are spending more time with our smartphones than we are with other human beings. While a frightening realization, it is the future that we have been building since we first sprouted on this earth. At the brink of achieving that reality, we are now at the stage where we are exploring choices and trying to make the right steps towards them. The devices that we are coming up with reflect these steps and that is where the concept of AI raises some interesting questions for IoT.

While AI is primarily used as the cornerstone of devices, in IoT it plays several roles. There is much there that could influence how the IoT would react with our world. Further along the way it would boil down to the popular paradox of the chicken and the egg. Which technology would shape our future- AI or IoT?

It is easy to argue that advances in both these technologies would be of equal consequence. However, that is not the case. The very correlation between these two verticals is just as defined as how contrastingly they could influence each other. Machine learning is a key aspect of the progress that IoT is making. An IoT network that would consist of devices with sensors, video surveillance tools etc. will be capable of monitoring the functioning of the other devices. For software related issues certain devices will be equipped with troubleshooting tools both for themselves as well as other devices. Data is the instigating factor that could influence all these technologies and it is data that will continue to govern them in the future. The expectations would again fall upon AI to make the best out of the data.

IoT in Data Analytics

The idea of developing actionable insights is something that in recent years has provided a huge update for the use of AI and IoT services. As these technologies function using data, the uses become well defined and the margin of error depends only on the validity of the data. This creates avenue for wearable ‘smart’ devices to actually function in a sentient manner. Devices such as the heart rate monitor watches, various goggles allow provide vital data that could be relayed to your doctor, your banker, even your barber, who could avail the analyzed output that they could use to customise the service they provide.

Deep Learning

Deep learning is a breakthrough in IoT. This technology facilitates devices to go beyond the prosaic machine learning algorithm. Deep learning draws from a plethora of sources to arrive at a solution on any given subject. This comprehensive approach to producing solutions could become a key driving force for IoT and how the various devices around us function under it.

Conclusion

The many exabytes of data that is being produced now allow for further proliferation on the IoT front. Going ahead, it is AI’s data analytics capabilities that could facilitate this growth. Both machine learning and deep learning both function on the data that is procured through AI data analytics.

With the AI data analytics process being non-stop, big data and other verticals are proving to be vital resources for IoT. Many industry experts believe, actionable insights will be the key to the future. The possibilities with actionable insights are endless and investments in AI have been made to speed up and increase the productivity.   

Machine Learning enhances User Experience for Music

When creativity meets technology, you get incredible outcomes. And that is what the music streaming industry is investing in these days to improve user experience amidst brutal competition. They push new boundaries with technology and diversify the music genre so that everyone can appreciate it.

The era of personalized music with machine learning

In the latest news, the music discovery process is getting personalized results with revolutionary machine learning. Nowadays, almost every big name of the industry is leveraging AI to create better and more personalized music lists.

So, you should not get surprised if the suggested music from Spotify, Pandora and Apple Music seems exactly what you want to hear. All these music-streaming providers implement complex algorithms to pick subtle cues and create personalized music list for you.

• Pandora combines the same technology with data analytics to make suggested playlists for listeners. The algorithms used by Pandora evaluate the songs or artists selected by a user. With that, it creates a playlist that has similar attributes, matching the personal preferences of that user.

• Spotify is probably the most enthusiastic player when it comes to using algorithm technology in music streaming. The company uses a collaborative filtering approach. The algorithms collect music streaming data from multiple users and compares it together. This comparison is conducted with Echo Nest, which is considered best in this technology for music search. Apart from collaborative filtering, Spotify also includes NLP and audio models in its method of providing personalized music.

How Machine learning is evolving music streaming personalization

As mentioned earlier, music-streaming companies are using a variety of AI technologies to make song discovery advanced and personalized.

Here are three major technologies revolutionizing the music-streaming industry.

1. NLP or Natural Language Processing

NLP enables algorithms to understand human language. APIs are used for sentiment analysis, which harnesses the meaning behind spoken and written words. The model of NLP allows music streaming providers to collect data from a variety of resources all over the internet. Algorithms collect data from articles, news, blogs and other resources available on the internet. Using the written text regarding a music, the machines understand the characteristics and provide them with the right playlists.

2. Collaborative filtering

Collaborative filtering is a comparative study of the users’ music listening behavior. The technology helps in understanding the popularity and characteristics of songs. Algorithms collect data from a wide range of users. These datasets include information regarding stream counts, saved tracks, page visits and many others.

By incorporating all kinds of streaming data together, algorithms create a personalized list of tracks for the listeners.

3. AI audio models

Companies like Spotify understand that NLP and collaborative filtering cannot offer justice for new songs. That is why they use another form of AI-Integrated audio model. This technology works just like the face recognition technology. However, the algorithms inspect the audio models instead of pixels. With raw audio evaluation, companies provide new songs to the users in their playlist.

Thus, it would not be wrong to say that machine learning has found a strong place in the large ecosystem of music discovery. With proven phenomenal outcomes, the justification of marrying AI with music does make total business sense!