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 Big Data can help HR with hiring decisions

The Human resources (HR) function is usually not the first thing that comes to mind when we think of big data. That’s because it is still a developing concept and a majority of the companies are still using traditional methods to perform HR related tasks.

Organizations should make the most of big data to find the right level of adaptability to offer work-life balance to its employees as well as to determine the right benefits and bonuses in order to encourage employee loyalty towards its company. It can also be used to increase the quality and usefulness of regular training programs, thus maximizing the use of their human capital.

Big data has proven itself fruitful in many businesses; be it sales, marketing or accounting. Today we explore how big data will impact the HR function:

1. Amplify the quality of new recruits

Hiring the wrong person for the job is probably the worst mistake an HR team can make. With the help of big data, recruiters can be more analytical and strategic when it comes to finding the ideal candidate for the job.

If they get access to online employee resume databases, employment records, social media profiles, tests and other profiles, it will help the recruiter find the best candidate with the highest potential by sorting this information and narrowing down the talent pool.

Take the case of Royal Dutch Shell; they made their employee play specially designed video games in order to analyze the best idea generators in their team. As a result, the team found it easier to recruit employees that had the 6 main qualities the company needed i.e. mind wandering, social intelligence, goal-orientation fluency, implicit learning, task-switching ability, and conscientiousness.

2. Promote better training and employee success rate

Training can be an expensive affair if the overall employee retention is unsuccessful. Big data allows businesses to measure the effectiveness of the training program so they can make better investments when it comes to training and development of their employees. If regular performance evaluations are conducted, the efficiency can be measured via big data and it will help the HR understand the effectiveness of their employee development programs.

Check out IBM’s strategy in this context. Traditionally an outgoing personality has been seen as a key trait, but IBM compared worker surveys and tests with manager assessments and found that the most important characteristic of sales success was actually emotional courage. Successful salespeople may or may not be outgoing, but they do need to be persistent, and not take no for an answer.

3. Prevent employee attrition

Making strategic workforce decisions without data to back them up is like guessing, and it’s an issue that has prevented the HR from making a bigger impact on business outcomes. Workforce analytics is the art and science of connecting data to discover and share insights about your workforce that will lead to better business decisions. In order to reduce employee turnover, HR needs to become more data-driven, looking past simple descriptive analytics and towards more exploratory analytics, predictive analytics.

To conclude

These are a few ways in which big data can help the HR perform at a much more efficient rate. It is an infant concept but once companies truly start implementing it in their recruitment processes, it will yield great results.

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.

Blockchain Technology in Life Sciences

In the medical sector, new and innovative therapies keep improving life sciences. However, the same innovation challenges the supply chain. Life science is in a desperate need for authenticated and secure drugs that can become available whenever required.

Why is blockchain a solution?

Blockchain technology has gained an immense level of growth in terms of investment. Experts project that this growth can reach up to a $3 billion market level as we all reach 2025.

Blockchain technology is applicable in almost every step of the supply chain of life science. And at each of those steps, this technology offers a unique benefit.

1. Provenance

Verifying product’s origin point becomes immutable with this technology. Tamper-proof blocks allow the use of digital markers. This way, all the chances of counterfeit product get diminished. Plus, life science supply chain meets every regulatory requirement.

2. Record management

The industry faces a need for extensive documentation. Highly complex records are created and managed, which increases the costs and administrative activities. All these processes can become automatic with this technology. Businesses need smart contracts that include regulations and logic of processing. Hence, all business data can get verified without wasting any time or money.

3. Sensitive data security

Access control is also possible with this technology. Networks act according to incorporated rules and restrict access to critical medical information. Hence, authorities know who accesses certain information and when. This can become a great advantage for healthcare consumers as well, who want to keep their health records confidential.

4. Managing internal process

There is a huge list of internal processes that life science companies have to manage. Tracking products, transactions, and factory operations are a few major internal processes. All in all, companies have to concentrate on their products in different steps such as raw material collection, packaging as well as labeling.

Choosing this technology can integrate each and every process spread across systems. Companies can skip multiple reconciliations and track everything with the help of a single ledger. This ledger will be available to every authoritative body. So, no need to hassle.

5. Multi-party collaboration

Collaboration is the most valuable property of this technology. Hence, clinical trial officers, trial sponsors, and multiple regulators can access and share data at the same time. A secure and shareable network brings transparency to the supply chain. Hence, every considerable party receives trustworthy collaborative network to rely on.

With speed and trust in life science, this technology offers the ability to transform the industry for good. Complications can go away and sensitive data can become more secure.

Happier patients

Soon blockchain is going to enable companies to present more relevant data for patients. Accurate information, continuous product availability, and other features can improve patients’ satisfaction. However, it all comes down to the manageability of health care and drug companies.

Life science sector requires a technology boost to attain much-needed goals in terms of security, speed, and quality. Hopefully, companies will understand this requirement and move forward in this direction as soon as possible. Only time will tell!

Advantages of Apache Cassandra Database

As one of the better-known NoSQL database, Apache Cassandra is fast becoming a preferred database of enterprises and SME’s alike. Its robust performance in applications needing heavy write systems traversing massive volumes of data is what makes it stand apart from its contemporaries.

A typical Cassandra database consists of keyspace (similar to a schema in a relational DBMS), column families (consistent with a table in a relational DBMS), and rows/columns. It also utilizes a Cassandra Query Language (just like SQL) to retrieve records, carry out actions, and communicate with the Cassandra database.

Here are five reasons why Cassandra makes for a great database system

1. No single point of failure

Its masterless architecture makes Cassandra highly fault tolerant. Because of this, any downtime affecting a few nodes will not impact the overall performance of the system.  This enhanced fault tolerance level is a great draw for enterprises who wish to provide ‘always-on’ online services to their customers.

If we look beyond a single datacenter then Cassandra can be of great help. It allows seamless replication of the data center, thus facilitating a strong disaster recovery and backup/retrieval system within your organization.

2. Handling massive datasets made easy

Hulu, NetFlix, Instagram, and Apple, the list of enterprise users who benefit from Cassandra speak a lot about its capability to handle humongous volumes and variety of data. If your organization too faces a probability of data volume expanding exponentially and scaling up at a rapid rate then you need not look beyond Cassandra.

You can rely on Cassandra to continue delivering optimized performance without any impact of the huge rapid change in the data it is handling

3. Logging is simplified

In today’s homogenous environment, a typical company has to deal with multiple clients and servers (Android, web, iOS to name a few). In such an environment, logging and analyzing logs become huge challenges to deal with. Cassandra comes across as a viable solution to centralized logging.

This way, your development team need not spend a lot of time on logging and can instead focus on better product development.

4. Fast reads and superfast writes

Workloads like metrics collection and logging need extremely fast writes for optimal performance. This is where Cassandra scores heavily over its peers. It offers a scalable read-write performance. This means that if you know a single server’s write performance, you can accurately assess how many servers are needed in a particular cluster to meet the performance expectations.

5. Active community support

A lot of young minds are focused on expanding the possibilities around Cassandra. They are highly active and provide assistance in case of issues around managing or configuring complex database setups using Cassandra. The monitoring and troubleshooting systems around the software make it a truly high-performance open source NoSQL database system.

Thus, it is clear that Cassandra offers a host of benefits that can add tremendous business value to your tech offerings. It is time that you explore Apache Cassandra for your enterprise database management needs.

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!

IoT in Agriculture

IoT has shown tremendous potential in various gadgets such as wireless speakers, electric appliances, light sources, etc. It has also been used in practical and innovative applications in industries (termed as Industrial Internet of Things or IIoT). Sectors such as agriculture is seen as one of the biggest beneficiaries of IIoT and the various advantages it offers to streamline the farming and cattle-rearing operations.

Why the need of IIoT in agriculture?

With the help of Industrial Internet of Things, the agriculture industry is evolving to enable growers and farmers face various hurdles on the field efficiently. Before the implementation of this technology, the sector was facing low rewards, heavy workload on labor, and high risks. Moreover, farmers used to face unexpected risks, economic recessions, and sudden changes in the environment as well. All this greatly affected the overall growth of crops in fields, until now.

How IoT can evolve farming?

IoT has a lot of scope in agriculture. So, let us have a look at some of the ways it can improve this sector.

1) Controlling climatic conditions in greenhouses

Greenhouses require subtle conditions so that the growing plants can stay healthy. Previously, this process was quite labor-intensive, but with the help of IoT-based equipment, it has greatly helped in reducing the burden on growers.

The process involves using sensors that monitor attributes like soil moisture, intensity of light, humidity, temperature, etc. These sensors connect to appliances that automate processes such as air or water control. Some sensors are even smart enough to deduce signs of pest infestations.

2) Safety of crops in logistics

IoT technology has also evolved the supply chain management of agricultural products, retail and logistics. Farmed food that is shipped to various places is tagged using Radio Frequency Identification (RFID) tags that help in easy tracing and tracking.

This increases consumer confidence and transparency levels about the source and origin of the food product they would be consuming. Some IoT gadgets used for monitoring the crops are so advanced that they provide real-time data for packaging, transport, and storage of farm foods.

3) Monitoring crops

A lot of IoT-based machines and robots are coming up to help in experimental farming. A few of these are designed for monitoring crops in a field. With synchronizing capabilities, these machines are able to record data like yield maps for crops or link information related to crop prices.

Such robots are said to be so capable of their sensors and features that they are able to keep track of every single crop stalk in fields.

4) Livestock farming efficiency

Not just plants, but farm animals can also be monitored using this technology. Internet of Things can monitor problems like infection threats among chicken, cattle, etc. and inform farmers about it before it gets too late. Even food intake habits can be monitored for farm animals that can help in deciding the right food for them.

No doubt, the world of IoT has so much to offer farmers in the world of agriculture. The ROI that this investment will bring is bound to outweigh the costs associated with it. Hence we can expect to see rapid adoption of IoT technology in the agribusiness.

How to effectively use data from Internet of Things (IoT)

The Internet of Things (IoT) is a digital revolution that has taken the world by storm. Having digital control over your home and office when you’re not around is fantastic progress in today’s world. It is a sure shot way of simplifying people’s lives.

The global IoT market has risen from 157 billion dollars in 2016 and is expected to reach a whopping 457 billion dollars by 2020. This translates to a compound annual growth rate (CAGR) of 28.5%.

The gains from IoT data

Today, companies are utilizing Internet of Things and its data to score on mission-critical business parameters like elevated customer experience, operational improvements, better yields in supply chain management, and overall revenue acceleration.

Manufacturing transportation and utilities are the key business sectors where the proliferation of IoT will be the greatest, thanks to benefits like improved quality management, better asset tracking, and manufacturing intelligence.

Here are 3 ways to use your IoT data to meet your KRAs

1. IoT for Cost Management

Most IoT users do not make the most of the data they receive via their devices. They sometimes may use just a small part of data received. They fail to realize the value of the statistics they obtain from their devices. These figures can be used to track operational issues, errors, and shortcomings.

The forecast for IoT devices as of now is to target errors and to boost improvement. Over time, Internet of things data can improve the economic state over different verticals. Of course, it will take a different amount of time to develop for different fields.

2. IoT to Revamp Products and Services

A lot of feedback from IoT users is generated. Analyzing it can be helpful in further bettering the devices and the services used by the customers. After using IoT devices for a certain time, a feedback generally pops up asking the user about his experience with the device and how the company can put in an effort to improve its usage.

The company should value this feedback, as it will help them create loyal customers. Because in today’s competitive market, every company comes up with similar products and services. If a brand wants to keep a customer loyal, they have got to take customer feedback seriously.

3. IoT for Procuring and Allocation of Data

To accentuate the value of data to its full potential, one needs to learn how to accommodate and evaluate data from various Internet of things devices that he is using. Only by doing this, issues can be trouble shooted and resolved more efficiently.

This data should be publically accessible where IoT developers can analyze the data, point out the mistakes and suggest optimal solutions. This concept is known as Open Data Market. It is a well-emerging concept and is the future of IoT development.

In conclusion, IoT data should be taken seriously as it can help the business grow and improve in various ways. It is an innovative new technology and should be used to its full potential.