How to use Deep Learning for Global Warming

Over the past years, our planet has experienced drastic climatic changes. Global warming is now inevitable as observed by scientists with the help of Earth-orbiting satellites and other technological advances. Since the late 19th century, the planet’s average surface temperature has risen about 1.62 degrees Fahrenheit (0.9 degrees Celsius), a change that has been driven largely by increased carbon dioxide and other human-made emissions into the atmosphere. Most of the warming has occurred in the past 35-40 years, and it is all a consequence of human activity.

Climate change has not only affected the global temperature, but it is also the reason behind warming oceans, shrinking ice sheets, glacial retreats, decreased snow covers, rise in the sea level, declination of the Arctic sea ice and also the acidification of oceans. These issues together cause a global challenge.

The world’s current population of 7 billion will grow to around 9.8 billion by 2050 and this augmentation will lead to an increase in the demand for food, materials, transport and energy and further increasing the risk of environmental degradation.

The important question to be asked now is can humanity preserve our planet for our future generations?

The answer is YES. A new study published in the journal Proceedings of the National Academy of Sciences has found that Artificial Intelligence (AI) can enhance our ability to control climate change.

Artificial Intelligence is defined as the simulation of human intelligence processes by machines, especially computer systems. These processes include the learning process (the acquisition of information and rules for using the information), the reasoning of information (using rules to reach approximate or definite conclusions) and self-correction. AI, in particular, has immense potential to help unlock solutions for a lot of problems.

Artificial Intelligence is a broad term under which come two applications – Machine Learning and Deep Learning.

Machine Learning provides systems the ability to automatically learn by developing computer programs that can access data and use it to learn from them and then apply what they’ve learned to make informed decisions. 

On the other hand, Deep learning creates an “artificial neural network” by structuring algorithms in layers. This network can learn and make intelligent decisions on its own. Deep learning is a subfield of Machine Learning. The “deep” in “deep learning” refers to multiple layers of connections or neurons, similar to the human brain.

How can deep learning help the challenge?

Artificial Intelligence can prove to be a game changer if used effectively. The advancement of technology achieved by AI has the potential to deliver transformative solutions. Some possible ways in which deep learning can be useful for the Earth are:-

1. Weather forecasting and climate modeling

To improve the understanding of the effects of climate change and also to transform weather forecasting, a new field of “Climate Forecasting” is already emerging with the help of Artificial Intelligence. This way of saving the planet sounds very promising since the weather and climate-science community have years of data, in turn, providing a fine testbed for machine learning and deep learning applications.

These datasets demand substantial high-performance computing power, hence limiting the accessibility and usability for scientific communities. Artificial Intelligence can prove useful in solving these challenges and make data more accessible and usable for decision-making.

Public agencies like NASA are using this to enhance the performance and efficiency of weather and climate models. These models process complicated data (physical equations that include fluid dynamics for the atmosphere and oceans, and heuristics as well). The complexity of the equations requires expensive and energy-intensive computing.

Deep learning networks can approximately match some aspects of these climate simulations, allowing computers to run much faster and incorporate more complexity of the ‘real-world’ system into the calculations. AI techniques can also help correct biases in these weather and climate models.

2. Smart Agriculture

Precision agriculture is a technique used for farm management that uses information technology to ensure that the crops and soil receive exactly what is needed for optimum health and productivity. The goal of Precision Agriculture is to preserve the environment, improve sustainability, and to ensure profitability.

This approach uses real-time data about the condition of the crops, soil, and air along with other relevant information like equipment availability, weather predictions etc.

Precision Agriculture is expected to involve automated data collection as well as decision making at the farm level. It will allow farmers to detect crop diseases and issues early, to provide proper and timely nutrition to the livestock. In turn, this technique promises the increase of resource efficiency, lowering the use of water, fertilizers, and pesticides which currently flow down towards rivers and pollute them.

Machine and deep learning help in creating sensors that are able to measure conditions such as crop moisture, temperature and also soil composition that will automatically give out data that helps in optimizing production and triggering important actions.

Smart Agriculture has the capability to change agriculture by changing farming methods and proving beneficial for the environment.

3. Distributed Energy Grids

The use of the application of deep learning in the energy grid is spreading increasingly. Artificial Intelligence can help in enhancing the predictability of the demand and supply for renewable resources, in improving energy storage as well as load management, in assisting the integration and reliability of renewable energy and in enabling dynamic pricing and trading.

AI-capable “virtual power plants” can easily aggregate, integrate and also optimize the use of solar panels, energy storage installations and other facilities. Artificial intelligence will enable us to decarbonize the power grid, expand the use and the market of renewables, thus increasing energy efficiency. The decentralized nature of distributed energy grids makes it more possible for them to be used globally.

Final thoughts

In conclusion, Artificial Intelligence techniques like deep learning can prove to be very useful for the environment in the future if used effectively. After years of damaging our planet, it is our time now to save it for the coming generations.

AI in Diabetes – A breakthrough in Healthcare

The National Diabetes Statistics Report, 2017, U.S. states that an estimated 30.3 million people of all ages or 9.4% of the U.S. population suffered from diabetes in 2015 and this count is growing every year.

These figures are alarming to healthcare authorities as well as to the controlling government. With an immediate attention required in this area, brilliant technologies like AI, machine learning and big data can be used to overcome the gap between those suffering and cured.

The economic cost to the US for diabetes care in 2017 alone amounted $327 Billion. This economic burden is getting out of the control with the number of diabetic patients adding every year.

For contributing to the cause, few digital health companies are taking the initiative to lessen this burden with the help of technology. They are leveraging technological advancements to innovate diabetes care solutions like non-invasive insulin delivery systems, continuous glucose monitoring devices, and digital diabetes management platforms.

These devices are the source of behavioral, physiological, and contextual data which can be analyzed and used to come up with more efficient diabetes care.

Today we are presenting some revolutionary startups who are making a contribution to help diabetes care evolve. Their contributions are remarkable with out of the box solutions for the problem at hand. Let’s take a look:

1) Livongo Health leveraging Big Data-Based Approach for Diabetes Care

Livongo Health is leveraging big data to help people manage their health conditions more efficiently and improve patient outcomes.

Hundreds of thousands of people are using their products such as blood glucose meters, blood pressure cuffs, and scales. The added advantage is that these devices collect data and send it to a larger database which is then used by the company for generating insights to benefit their members.

Also, this pattern has encouraged the startup to come up with a reinforcement learning platform where they observe the data and generate a variety of personalized messages to send to their members.

They learn about members’ behavior with the responses received and eventually know what works best for them. We would call this a good start!

2) Bigfoot Biomedical is working on AI-driven automated insulin delivery with an artificial pancreas

Bigfoot Biomedical, a California-based diabetes management company, is working on a mission to develop an automated insulin delivery system with an artificial pancreas. This system sounds promising and can make the lives of diabetic patients easy. The future of diabetes care will change with this product launched in the market.

The startup made it possible by leveraging the potential of AI to devise a closed-loop system that would observe and learn from the user’s response to food, exercise, insulin, and then adjust the dose.

A good head start is that the company has received a substantial financial support and thus the process of product development has fast-forwarded to the clinical trial phase.

It is just a matter of time that an AI-driven automated insulin supply system will become the life-changing diabetes care solution.

3) Glooko is providing mobile and web apps for diabetes care

Glooko is a global diabetes data management company which provides HIPAA-compliant and widely compatible mobile and web apps. These apps synchronize with diabetes care devices and activity trackers to collect data like insulin, blood pressure, blood glucose, diet, and weight.

The company is harnessing the power of Big data and predictive algorithms to empower diabetic care professional with tools to analyze trends and provide necessary recommendations.

Glooko collects data from over 180 exercise and diabetes care devices and then correlates it with exercise, food, medication, and other relevant data to deliver insights with clinical care and self-management.

These apps will contribute a lot to self-management and also sizeable improvement can be made in patient outcomes.

4) Virta Health is using AI to reverse Diabetes

Virta Health is a silicon valley startup which has embarked upon the mission to cater alternative treatment for type two diabetes without any surgery or medication. The company has already received 50% positive results in its clinical trial for reversing the chronic diabetic condition.

Virta has taken the nutrition centric approach which is based on ketogenic diet. With this diet, the body burns fat for fuel and not carbs. Virta has a user-friendly app which allows the user to enter ketones, blood sugar, and other relevant information.

Once the details are entered, the app uses AI to device a customized treatment plan for the individual.

Additionally, this app helps patients find specially assigned clinicians and health coaches for immediate assistance and consultation. Another great step in improving self-management!

5) Digital Diabetes Clinic by GlucoMe

GlucoMe is an Israeli startup which has invented a digital diabetic clinic which uses a cloud-based solution for remote diabetic care. With this facility, the healthcare professionals can remotely monitor the patient’s insulin and blood glucose and adjust the dose accordingly as and when necessary.

The data is transferred from smart glucose monitors and insulin pens to a mobile app which helps in monitoring and making decisions that support the platform to function.

AI and machine learning are used to generate meaningful insights and actionable treatment plans. The healthcare will be simplified to a great extent with the use of digital diabetic clinic.

Final words

Personalized treatment plans based on real-time data along with intelligent insulin delivery algorithms are the need of the hour. Technical advancement in the field of healthcare has a promising future and startup initiatives like these can open up a gamout of opportunities for healthcare professionals and patients.

The Artificial Intelligence in Music Debate

Music has come a long way from the early days where the only instrument was the human vocal cord. The use of computer-based technology in music started in the 60s when the iconic Moog synthesizers starting taking over the British music scene and paved way to the irreplaceable artform that is progressive rock. If it was not for the Minimoog, Keith Emerson would have just stuck to literally rocking the Hammond L100, riding it like a bucking bronco, brutalizing it with knives and occasionally when he got the time even playing music on it. Yes indeed, the 60s and 70s were crazy times. But the introduction of integrated circuits and eventually processors paved way for a lot of innovation in music and the discovery of many new genres of music.

However, technology in music didn’t stop with the synthesizers. Over the past few decades, many technologies have been incorporated into music including the controversial auto-tune. In recent times though the most technology to make its way into the musician’s arsenal is Artificial Intelligence. While the overuse of technology in music is heavily debated and often shunned by the musicians of yore, there is still a lot of support as well. So, let us have a look at how AI is being used in the music industry today.

Algorithmic Composition

Surprisingly the roots of Artificial Intelligence in music date back to as early as 1965 when synth pioneer and inventor Ray Kurzweil showcased a piano piece create by a computer. Over the years, the use of computer programs in the composition, production, performance, and mass communication of music has allowed researchers and musicians to experiment and come up with newer technologies to suit their needs.

Algorithmic composition is one of the more complex applications of AI in music. Kurzweil’s early efforts towards producing music using computer algorithms relied on pattern recognition and recreation of the same in different combinations to create a new piece. The process of creating music cognitively used today have spawned from this idea. The algorithms used therein are created by analyzing certain human parameters that influence how music is perceived. The first step here is for the cognitive system to understand the music as a listener and then draw vital information from it as a musician would. To achieve this the AI system must first be able to compute the notational data in relation to its audio output. Aspects such as pitch, tone, intervals, rhythm, and timing are all taken into consideration here.

Based on the approach and the result intended there are several computational models for algorithmic composition such as mathematical models, knowledge-based systems, evolutionary methods etc. Each model has its own way of uploading a piece of music into the system, how the system would process it, what information it can derive from it and how it would do it. Composition is not always the purpose of an Algorithmic system. Musicians can use it for comprehension or even draw inspiration.

Non-Compositional Applications

While AI-based music composition is a work in progress that has been going on for decades and could take quite a few more to perfect, AI serves the music industry in many ways outside the studio.

Engagement Tracking

Today almost every form of art and entertainment profits most from the digital medium. This is also the case with music if it wasn’t obvious enough. Artists create music for a target audience which is spread around the many platforms like Youtube and Spotify. So, the engagement data recovered from these platforms have a huge impact on the songs that are created as well as how they are promoted. Artificial Intelligence plays a central role in this process. AI systems often act as the buffer between the distributors, advertisers, marketers and the audience for all their processes including monetization and the transactions involved therein.

Analytics

Analytics data is an essential aspect of almost every online venture today. And music is no exception. Music creators, independent and signed alike rely on analytics data to manage their online presence. AI-based analytics tools help bring speed and accuracy to the analytics processes, so as to help musicians keep up with the fast-paced internet competition.

It’s not Rock and Roll, Man

Music, being an art form that demands a great deal of creativity also demands a human mind behind it that is creative rather than analytical. This is the debate that has been surrounding the AI-music alliance for a long time. Technology, in general, has had the music world divided for eons. In a decade where bands like Pink Floyd thrived exclusively on ‘space age’ technology like the EMS VCS3 and a whole bunch of other stuff, acts like Rush, Motorhead and Aerosmith roughed it out with just the rudimentary instruments. Although most musicians are always open to new technologies or eventually warm up to them, that has not been the case with AI. The prospect of having to put in very less creative effort to compose may entice some, (especially given the volume of music that is being put out everyday) it also opens up the debate of industry giants such as Sony, Universal, Warner and Tips misusing or overusing it which could lead to an eventual vacuum in creativity.

I’m Perfect! Are You?

AI has always been a source of contradiction among all communities so it is no surprise that the music community is both supportive as well as skeptical of it. So far efforts towards harnessing Artificial Intelligence-based composition have been of infinitesimal proportions. So, there is not much out there for us to judge and take sides. Ventures like Artificial Intelligence Virtual Artist (AIVA) are working exclusively towards bringing out the full potential of AI as a means of completely automating music production. While such ideas are a grey area today, how these will come to influence music as we know it, only time will tell.

What you Should know about Google AutoML

One of the highlights of last year’s Google I/O was the announcement of Google’s Automated Machine Learning or AutoML which according to founder Sundar Pichai was their ‘AI Inception’. The Machine learning Cloud software suite is finally about to hit the alpha stage and as of now, it seems like there is a lot that developer and designers alike can leverage from this tool. So let us explore this new addition to the Google family.

What is AutoML?

AutoML is Google’s Cloud software suite for Machine Learning tools based on Google’s Neural Architecture Search (NAS). Using AutoML a user can train deep networks without having any expertise in deep learning or Artificial Intelligence. This is achieved by leveraging NAS to derive the most suitable data network for the task at hand. This is a huge step for businesses as they will be able to harness the power of machine learning without using experts with years of experience in any field of automation. Customization of various AI product models meant for mundane as well as complex tasks.

How Will Businesses be able to Leverage AutoML?

Today, with deep learning still being in its infancy, most business use, AI for simplistic tasks. And considering the fact that AI and Machine learning experts are hard to come by and are quite expensive to hire, most companies try to keep their deep learning efforts to a minimum. This is where AutoML makes a world of difference. One of the most common applications of deep learning for businesses today is Image processing. Today companies rely on Image classification networks that run on neural matrices. This although running on machine learning tools, requires quite a bit of manual effort to isolate the proper network for the image datasets and then train the models to adapt to the task they intend to accomplish. With AutoML companies can skip the tedious processes of research and get directly into the transfer process. As a matter of fact, Google’s first AutoML tool is intended for exactly this aspect for visual companies.

AutoML Vision

This is AutoML’s first tool which will help developers create custom image recognition models. According to Google this tool will allow developers to upload their labeled data and the system will automatically create a custom machine learning model for them. This, in turn, will allow them to focus more on product development, design and other important aspects.  Although still in its alpha stage, this tool is being used by many big-time players such as the animation giant Disney.

Conclusion

As the concept of ‘Software 2.0’ catches on, developers around the world are getting used to the new tools largely built upon their predecessors, to write software without actually having to code each and every part of it. Tools like AutoML are a good example of how this is being applied to every aspect of software irrespective of the purpose. With AI the scope is further enhanced. Yet, for such tools to achieve any degree of comprehensiveness, they themselves need to be further developed to accommodate the needs of the upcoming generations of developers who will be relying upon them to do most of the work. So the road ahead will be the chicken and egg paradox where the efforts of both the man and the machine will craft the future. Today we have that sense of direction as to where we need to focus our efforts in terms of research, which is great news for AI tools such as AutoML.

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.