Artificial Intelligence can help decode epileptic brains

Epilepsy is the fourth most common neurological disorder affecting nearly 65 million people worldwide. The seizures or ‘fits’ as is commonly known, arise due to unusual electrical activity in the brain and is the chief symptom of epilepsy.

Neither dependent on age or gender, the onset of the seizure is unpredictable without a set pattern of frequency of occurrence or severity, often posing a challenge to the caregiver.

Although epilepsy can be related to previous brain injuries or genetic factors, neurologists have found unprovoked, recurrent seizures in healthy individuals too.

How and why these seizures occur remains a mystery. However, research has found that the source of seizures is within the brain. In other words, the brain itself is the generator of epilepsy.

If the origins are within the brain, then, are there any fingerprints that can be detected? Does the brain offer tell-tale signs which can be mapped to predict the tendency of epilepsy?

Seeking answers to these questions, a team of interdisciplinary researchers conducted a study to peep inside epileptic brains. The results indicate that there exist independent neural networks that can carry disease-sensitive information about the anomaly.

With the help of machine learning models and artificial intelligence, researchers were able to detect and reveal the hidden patterns.

“Epilepsy is not a disorder but the manifesting of something from within the brain’s electrical activity. Interestingly, each one of us has the neural map of epilepsy within our brain. It is only when the network gets fired and manifests externally, in a recurrent manner, it becomes  disorder or epilepsy,” said Tapan Kumar Gandhi, lead researcher of the study from Indian Institute of Technology-Delhi, while speaking to India Science Wire.

The usual diagnosing tool for epilepsy is by EEG (electroencephalogram) readings of epileptic patterns and visible symptoms like convulsions, loss of consciousness or sensory disturbances.

Existing studies reveal specific patterns that represent synchronous activities of sensory, auditory, cognitive and other functions. These activities are indicated by the change in blood flow to the brain and seen as BOLD signals or changes in the Blood-Oxygen -Level-Dependent output.

Recent developments in Magnetic Resonance Imaging or MRI help picture these activities in the brain and detect the cause of seizures such as a lesion or scar. However, MRI is not very useful when a seizure flares up. Whereas, functional MRI — another scanning method— can record regional interactions in the brain when a particular task is being performed.

In 1995, Indian researchers had found that the brain shows prominent neural network connections even in its resting state. Termed as resting-state functional MRI or rsfMRI, the images from this scanning indicate neural patterns in an individual’s brain even when no action is performed.

In the present study, the team utilized rsfMRI technique and performed brain scans on individuals with Temporal Lobe Epilepsy (TLE), which is the most common form of epilepsy.

“We hypothesised that there could be ‘disease-specific networks’ in epilepsy prone brain that can be identified with the help of the machine learning model,” said Gandhi.

Machine learning involves artificial intelligence to read live data instead of preprogramed information. Such a building block of a machine is analogous to a neuron cell in the brain.

Researchers used a tool called Support Vector Machine (SVM) to deal with the complex and non-linear data obtained from the scans. By using another algorithm called Elastic- net-based ranking, the relevant features of the neuroimaging data were extracted. The signals were integrated to reveal the patterns.

The team conducted a pilot study on 132 subjects — 42 with epilepsy the rest healthy individuals. Parameters like age, gender, history of epilepsy, genetic predisposition, incidents of injuries, medications and more, were taken into account. The epilepsy patients underwent three rsfMRI while those in the healthy group were scanned once.

In all, 88 independent components or networks were obtained from the whole- brain imaging data and fed as input to the SVM. From the patterns, top 10 strong networks were correlated with clinical features using another standard method called Pearson’s Correlation to generate the rsfMRI epileptic neural networks.

From the pattern inputs, the SVM could identify epileptic individuals to an accuracy of 97.5 per cent and specific lobes in the brain responsible for the condition. The model also revealed correlations such as the age of onset, frequency of seizures, or duration of illness.

By this, researchers concluded that the independently derived rsfMRI contains epilepsy-related networks. “Our research establishes that with the help of machine learning methods, we can identify these networks, as we had hypothesised. Increased strength in these networks indicates the possibility of a progressing Temporal Lobe Epilepsy,” explained Gandhi.

The team included Rose Dawn Bharath, Sujas Bharadwaj, Sanjib Sinha,Kenchaiah Raghavendra, Ravindranadh C Mundlamuri, Arivazhagan Arimappamagan, Malla Bhaskara Rao, Jamuna Rajeshwaran, Kandavel Thennarasu and Parthasarathy Satishchandra (National Institute of Mental Health and Neurosciences, Bengaluru); Tapan K Gandhi  and Jeetu Raj (IIT, Delhi); Rajanikant Panda (Universitè de Liège, Belgium); Ganne Chaitanya (Thomas Jefferson University, USA) and Kaushik K. Majumdar (Indian Statistical Institute, Bengaluru). The study results have been published in journal European Radiology.

How is Machine Learning Influencing Supply Chain Management?

#Machine_learning is a direct application of #artificial_intelligence which enables a system to learn from data recorded from actions and experiences for better future experiences. Machine learning incorporates learning arising from the combination of different variables enabling better consumer experiences.

The logistics industry and its supply chain management are affected by high number of variables and uncertainties like inadequate area mapping or imbalance between demand and resources availability or vehicle breakdown or even the vagaries of weather. Determining innovative patterns in supply chain data through #machine_learning enabling excellent customer experiences can transform the prospects of most logistics businesses.

Some ways in which #machine_learning is positively influencing supply change management are:

1. Enhancing Last-Mile Delivery Experience

Matching delivery time with customer’s convenience has always been a challenge in last-mile delivery. Before the modern technological interventions took place, it was a trial and error method for finding the addressee present at the time of delivery.   The application of AI in logistics has reinvented the last-mile-delivery experiences. #AI uses algorithms, patterns and predictive insights from large data sets to differentiate categories. For example, we use #machine_learning to identify the type of delivery address – whether it is office or home – and the system automatically figures out the best time to make the delivery attempt. This increases the likelihood of addressee’s presence at the delivery address ensuring successful delivery and improving the customer experience.

#ML also helps to keep the supply chain updated about weather forecasts, traffic situations and other important factors directly or indirectly impacting the delivery schedule. Incorporating all the variables for a best-case delivery schedule increases the likelihood of successful delivery and improves the customer experience.

Successful deliveries in first attempt mean on-time shipment completion which brings in cost economies in the whole supply chain process. 

2. Identifying the Right Delivery Locations

Best of cartographers in the world cannot provide a minute up-to-date map with all possible addresses listed accurately. With net access and ecommerce penetrating the interiors and a continuously expanding habitable landscape, locating unstructured addresses is a tough job for delivery personnel. Indian addresses, where non-standardized, are hard to decipher and locate. Pin codes while helpful to some extent, cover large expanses where locating the ultimate door for delivery is a task cut out for our delivery boys. Supply chain management works with such inaccurate data daily.

#Machine_Learning especially comes in handy here. We look at historical delivery data and use machine learning models to triangulate the approximate geolocation where the address lies.

3. Enabling Field Staff to Take Smart Decisions

In the logistics industry, the on-ground variables are many and situations can change rapidly. A cyclone in Gujarat may require rerouting of shipments via different routes to different locations; a political rally in a locality may disrupt the availability of delivery personnel at the last mile hub or an unexpected surge in volumes from a client may choke certain hubs. There can be multiple resources to such situations.  Using #machine_learning and advanced analytics managers can quickly learn best case and worst possible scenarios. It uses complex algorithms to suggest optimal solutions to field personnel for best decisions sans much error. 

#Machine_learning and AI-based techniques form the foundation which will sustain the next-generation logistics and supply chain ecosystem in the market. #ML is ideally suited for providing insights for improving supply chain management performance through better inventory planning, cost optimization, improvement in customer experience by eliminating fraud, reducing risk, and error free delivery management. It can also encourage the creation of new business models.

Artificial Intelligence for Counterterrorism?

The recent debate between the Associated Press and Facebook about the success of removing content posted by terrorist organizations should be a wake-up call concerning content moderation capabilities on these kinds of platforms. Facebook data indicates the removal of 99% of terrorism content, while AP contends that Facebook’s success is only 38%. The point here is that #machine_learning adds a limited capability to human content mediation. The current state of the art in #machine_learning in this area is far from meeting expectation and is a fantasy, created around the magical tool of #artificial_intelligence (AI).

Terrorist networks will continue to exploit advanced technology in the areas of social network mapping and terrorist recruitment to benefit from the #AI arms race. New #AI_technology in drones, among other things, will result in the production of cheap versions of them and that may easily fall into the hands of terrorists. There is no doubt that terrorist groups like ISIS will attempt to utilize all possible means to pursue terrorist activities. The gaps in content moderation in social media and communication networks will constitute opportunities for ISIS and others as well.

#Machine_learning has a technology aspect, a social context, and an industry dimension. On the one hand, it is a product of high technology and a market for it. The social context is where it impacts the daily lives of people. In this sense, there is a growing #AI intervention with an influence on the socio-economic conditions of people. This is an evolving phenomenon, which requires social, political, legal and ethical evaluations, in addition to technology.

#Machine_learning relies on algorithms that are known as classifiers. The classifier needs to be trained by data and works better if the difference in data, no matter how massive it is, clearly shows it. As it is fed by labeled categories, it is fragile against unforeseen conditions. It does not have a cognitive ability comparable to humans in this sense. That is why one would not expect #machine_learning to be able to respond to the complexities of societal and cultural value settings. The automated tools in one setting may be fragile in others. However, it is also next to impossible to monitor contents at today’s scale of social media and relevant platforms only with human capability. The need for #machine_learning is obvious.

The uploaders of content are aware of the deficits of machine learning enhanced tools. They develop measures to bypass the filters of automated tools. They may modify the content until they reach the goal of staying on the platform as much as possible. Human probes would help automated tools to discover blind spots. However, the idea of creating efficient filters may not always be possible. The industry dimension of machine learning does not like to disappoint customers. Providers may be faced with considerable fines/penalties if they cause government dissatisfaction in the case of benign posts. This situation results in over-filtering which puts machine learning to the side of “artificial” rather than the desired component of “intelligence” in content management.

The Amazing Ways Dubai Airport Uses Artificial Intelligence

AI Customs Officials

The Emirates Ministry of the Interior said that by 2020, immigration officers would no longer be needed in the UAE. They will be replaced by artificial intelligence. The plan is to have people just walk through an AI-powered security system to be scanned without taking off shoes or belts or emptying pockets. The airport was already experimenting with a virtual aquarium smart gate. Travelers would walk through a small tunnel surrounded by fish. While they looked around at the fish that swim around them, cameras could view every angle of their faces. This allowed for quick identification.

AI Baggage Handling

Tim Clark, the president of Emirates, the world’s biggest long-haul carrier believes artificial intelligence, specifically, robots should already be handling baggage service including identifying them, putting the bags in appropriate bins and then taking them out of the aircraft without any human intervention. He envisions these robots to be similar to the automation and robotics used at Amazon.com’s warehouses.

Air Traffic Management

In a partnership with Canada-based Searidge Technologies, the UAE General Civil Aviation Authority (GCAA) is researching the use of artificial intelligence in the country’s air traffic control process. In a statement announcing the partnership is 2018, the director-general of the GCAA confirmed that it is UAE’s strategy to explore how artificial intelligence and other new technologies can enhance the aviation industry. With goals to optimize safety and efficiency within air traffic management, this is important work that could ultimately impact similar operations worldwide.

Automated Vehicles

Self-driving cars powered by artificial intelligence and 100% solar or electrical energy will soon be helping the Dubai Airport increase efficiency in its day-to-day operations, including improvements between ground transportation and air travel. Imagine how artificial intelligence could orchestrate passenger movement from arrival to the airport to leaving your destination's airport. In the future, autonomous vehicles (already loaded with your luggage) I could meet you at the curb. Maybe AI could transform luggage carts to act autonomously to get your luggage to your hotel or home, eliminating any need for baggage carousels and the hassle of dealing with your luggage.

How expectation influences perception

For decades, research has shown that our perception of the world is influenced by our expectations. These expectations also called "prior beliefs," help us make sense of what we are perceiving in the present, based on similar past experiences.

Consider, for instance, how a shadow on a patient's X-ray image, easily missed by a less experienced intern jumps out at a seasoned physician. The physician's prior experience helps her arrive at the most probable interpretation of a weak signal.

The process of combining prior knowledge with uncertain evidence is known as Bayesian integration and is believed to widely impact our perceptions, thoughts, and actions. Now, MIT neuroscientists have discovered distinctive brain signals that encode these prior beliefs. They have also found how the brain uses these signals to make judicious decisions in the face of uncertainty.

"How these beliefs come to influence brain activity and bias our perceptions was the question we wanted to answer," says Mehrdad Jazayeri, the Robert A. Swanson Career Development Professor of Life Sciences, a member of MIT's McGovern Institute for Brain Research, and the senior author of the study.

The researchers trained animals to perform a timing task in which they had to reproduce different time intervals. Performing this task is challenging because our sense of time is imperfect and can go too fast or too slow. However, when intervals are consistently within a fixed range, the best strategy is to bias responses toward the middle of the range. This is exactly what animals did. Moreover, recording from neurons in the frontal cortex revealed a simple mechanism for Bayesian integration: Prior experience warped the representation of time in the brain so that patterns of neural activity associated with different intervals were biased toward those that were within the expected range.

Hadoop Map Reduce For Analysing Information

Market Study Report, LLC, has added a detailed study on the #Hadoop_market which provides a brief summary of the growth trends influencing the market. The report also includes significant insights pertaining to the profitability graph, market share, regional proliferation and SWOT analysis of this business vertical. The report further illustrates the status of key players in the competitive setting of the #Hadoop_market, while expanding on their corporate strategies and product offerings.

#Hadoop, the #Apache_Hadoop developed by Apache Software Foundation is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. The base Apache Hadoop framework is composed of the following modules? Hadoop Common? It contains libraries and utilities needed by other #Hadoop modules? Hadoop Distributed File System (HDFS)? A distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster? Hadoop YARN? A platform responsible for managing computing resources in clusters and using them for scheduling users' applications; and? Hadoop Map Reduce? An implementation of the Map Reduces programming model for large-scale data processing.

Decision Managment

Decision management, also known as enterprise decision management (EDM) or business decision management (BDM) entails all aspects of designing, building and managing the automated decision-making systems that an organization uses to manage its interactions with customers, employees, and suppliers. Computerization has changed the way organizations are approaching their decision-making because it requires that they automate more decisions, to handle response times and unattended operation required by computerization and because it has enabled "information-based decisions" – decisions based on analysis of historical behavioral data, prior decisions, and their outcomes.

Decision management is described as an "emerging important discipline, due to an increasing need to automate high-volume decisions across the enterprise and to impart precision, consistency, and agility in the decision-making process". Decision management is implemented "via the use of rule-based systems and analytic models for enabling high-volume, automated decision making".

Organizations seek to improve the value created through each decision by deploying software solutions (generally developed using BRMS and predictive analytics technology) that better manage the tradeoffs between precision or accuracy, consistency, agility, speed or decision latency, and cost of decision-making within organizations. The concept of decision yield, for instance, focuses on all five key attributes of decision-making: more targeted decisions (precision); in the same way, over and over again (consistency); while being able to adapt "on-the-fly" (business agility) while reducing cost and improving speed, is an overall metric for how well an organization is making a particular decision.

Speech Recognization

When discussing the rise of #deep_learning, the accuracy of automated approaches is typically compared to the gold standard of flawless human output. In reality, real-world human performance is actually quite poor at the kinds of tasks typically being considered for #AI_automation. Cataloging imagery, reviewing videos and transcribing are all tasks where humans have the potential for very high accuracy but the reality of their long repetitive mind-numbing hours sitting in front of the screen means human accuracy fades rapidly and can vary dramatically from day to day and even hour to hour. For all their accuracy issues, automated systems promise far more consistent results.

#Speech_recognition is an area where humans at their best still typically outperform machines. In real-life real-time transcription tasks like generating closed captioning for television news, however, it turns out that commercially available systems like Google’s Speech-to-Text API are actually almost as accurate as their human counterparts and are far more faithful in their renditions of what was said.

Look more closely at the captioning of some stations and an interesting pattern emerges: the quality of the human captioning can vary from day to day and even over the course of a single day.

Real-time transcription is typically outsourced to third-party companies who employ contractors to type up what they hear. Quality can vary dramatically between contractors and even the same individual might perform better in the morning when they are more rested or just have a bad day.

Different transcriptionists can exhibit different kinds of errors, meaning the same word can be spelled correctly for part of the day and exhibit far more typographical errors during the rest of the day.

Some stations tape their morning shows and rebroadcast them as-is directly from tape in the afternoon, but may choose to retranscribe them in the afternoon on the off chance that breaking news forces the station to interrupt the taped show. This means that the exact the same show may have different typographical errors in the afternoon than it did in the morning.

Big Data Algoritms

#Clustering #algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) #algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative #algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering #algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for #big_data.

Information Fussion

A more precise definition of the field of #information_fusion can be of benefit to researchers within the field, who may use such a definition when motivating their own work and evaluating the contribution of others. Moreover, it can enable researchers and practitioners outside the field to more easily relate their own work to the field and more easily understand the scope of the techniques and methods developed in the field. Previous definitions of information fusion are reviewed from that perspective, including definitions of data and #sensor_fusion, and their appropriateness as definitions for the entire research field are discussed. Based on the strengths and weaknesses of existing definitions, a novel definition is proposed, which is argued to effectively fulfill the requirements that can be put on a definition of #information_fusion as a field of research.

Internet Of Things (IOT)

MediaTek has launched the new i700 the platform, which is an #AI chipset that can be used for all things #IoT. According to the company, the platform features high-speed edge #AI computation that can be used for #image_recognition, accelerated development of #AI-enabled #IoT products, and more. The company states that the chipset will be helpful in building smart cities as well. MediaTek states that the i700 platform will start shipping next year to global clients.

The new MediaTek i700 platform features an octa-core CPU that has two Cortex-A75 cores clocked at 2.2GHz and six Cortex-A55 cores clocked at 2.0GHz. The CPU is coupled with the IMG 9XM-HP8 #Image_Signal_Processor, which is clocked at 970MHz. The inbuilt #AI_processor has dual cores and comes embedded with #AI Accelerator as well as the #AI Face Detection Engine. The #AI Engine and the CPU together help the i700 perform #AI computations up to five times faster than its predecessor. MediaTek has also made sure that the platform is fully compatible with Google’ s Android #Neural_Networks API and other frameworks such as TensorFlow, Caffe, TF Lite, etc.

Will artificial intelligence replace doctors?

Several new studies have shown that computers can outperform doctors in cancer screenings and disease diagnoses. What does that mean for newly trained radiologists and pathologists?

A young Johns Hopkins University fellow recently asked that question while chatting with Elliot Fishman, MD, about #artificial_intelligence (AI). The two men were on the opposite ends of the career spectrum: Fishman has been at Johns Hopkins Medicine since 1980 and a professor of radiology and oncology there since 1991; the fellow was preparing for his first job as a radiologist.

 Fishman laughs when he tells the story, but he understands the concern. Over the past few years, many #AI proponents and medical professionals have branded radiology and pathology as dinosaur professions, doomed for extinction. In 2016, a New England Journal of Medicine article predicted that “#machine_learning will displace much of the work of radiologists and anatomical pathologists,” adding that “it will soon exceed human accuracy.” That same year, Geoffrey Hinton, PhD, a professor emeritus at the University of Toronto who also designs #machine_learning algorithms for Google (and who received the Association for #Computing Machinery’s A.M. Turing Award often called the Nobel Prize of computing, in 2019), declared, “We should stop training radiologists now."

The reason for the predictions? #AI’s tantalizing power to identify patterns and anomalies and to examine “pathologies that look certain ways,” says Fishman, who is among the enthusiasts: He’s studying the use of AI for early detection of pancreatic cancer.

“The hope is that if we could pick up early tumors that are missed, we would have better outcomes,” he says.

An array of studies have offered glimpses of #AI’s enormous potential. In a study published by #Nature_Medicine in May 2019, a Google algorithm outperformed six radiologists to determine if patients had lung cancer. The algorithm, which was developed using 42,000 patient scans from a #National_Institutes of Health clinical trial, detected 5% more cancers than its human counterparts and reduced false positives by 11%. False positives are a particular problem with lung cancer: A study in JAMA Internal Medicine of 2,100 patients found a false positive rate of 97.5%.

Furthermore, #AI performed comparably to breast screening radiologists in a study in the March 2019 Journal of the National Cancer Institute. At Stanford University, computer scientists developed an algorithm for diagnosing skin cancer, using a database of nearly 130,000 skin disease images. In diagnostic tests, the algorithm’s success rate was almost identical to that of 21 dermatologists, according to a study published in Nature in 2017. In another skin cancer study, #AI surpassed the performance of 58 international dermatologists. The algorithm not only missed fewer melanomas, but it was less likely to misdiagnose benign moles as malignant, the European Society for Medical Oncology found.

Natural Language Processing (NLP)

The #Natural_Language_Processing also powers camera features like #AI Color, a Sin City-inspired effect that keeps a subject in color while everything else in the scene is black and white, and a 3D object-scanning tool — Live Object — that recreates real-world objects in digital environments. The Mate 20 Pro’s Animoji-like Live Emoji and 3D Face Unlock tap into the #NPU for facial tracking, while it's Master AI 2.0 camera mode leverages it to recognize scenes and objects automatically and adjust settings like macro and lens angle. Additionally, #AI Zoom uses NPU-accelerated object tracking to automatically zoom in and out of subjects; video bokeh highlights the foreground subject while blurring the background; and Highlights generates edited video spotlights around the recognized face.
#artificialintelligence #machinelearning #robotics #datamining #bigdata#cybersecurity

RoboticProcess Automation (RPA)

You have a variety of factors to consider when identifying opportunities for robotic process automation: If a process is predictable, repetitive, and high-volume, for example, it might be a prime candidate for RPA.

However, due to high expectations – and sometimes misplaced hopes – some people veer off the path to a successful RPA initiative before they really get going. When this happens, it can be the result of a basic misunderstanding about what RPA is or how it works.

RPA improves business processes:

RPA automates processes. If those processes need to be improved, though, you have to do that work – RPA won’t do it for you, and automating a flawed process isn’t productive.

“As companies look to digitally transform themselves, they are looking to streamline and modernize processes,” says John Thielens, CTO at Cleo. “While RPA perhaps can be viewed as a form of streamlining, it streamlines processes in place, but by itself does not necessarily improve them.”

Thielens notes that this misunderstanding can occur in organizations that are looking for process improvements as part of a broader digital transformation; they might see RPA as a solution to process woes when it’s better looked at as a tool for achieving new efficiencies and productivity gains with well-established processes.

There’s a related mistake people make with RPA: Automating a process you don’t fully understand. Eggplant COO Antony Edwards recently told us that this is a common pitfall: “Most people don’t have clearly defined processes, so they start automating, and either automate the wrong thing or get lost in trying to reverse-engineer the process.” 

Image Processing

#GlobalImageProcessing Systems Market Analysis, Forecast & Outlook (2019-2024)” provides an extensive research and detailed analysis of the present market along with future outlook. The #ImageProcessing Systems Market report covers the analysis of key stake holders of the #ImageProcessing Systems industry. Key players of the Image Processing Systems market are being profiled along with their respective financials and growth strategies.

Important application areas of Image Processing Systems are also assessed on the basis of their performance. Market predictions along with the statistical nuances presented in the report render an insightful view of the #ImageProcessing Systems market. The market study on #GlobalImageProcessing Systems Market 2018 report studies present as well as future aspects of the #ImageProcessing Systems Market primarily based upon factors on which the companies participate in the market growth, key trends and segmentation analysis.

Robotics

A mobile motor created by a team at the Massachusetts Institute of Technology (MIT) could change the way we view and build #robots. The #robot consists of five tiny fundamental parts that have the ability to assemble and disassemble into different functional devices — with the end goal of having it build other, larger #robots. MIT Professor Neil Gershenfeld, who was a part of this groundbreaking project, said that he based the concept of how all forms of life are made up of 20 amino acids.“It’s a fundamentally different way in how you build #robotics systems,” Gershenfeld told #Digital Trends. It’s groundbreaking in the sense that the new system is a step closer to creating a standardized set of parts that could be used to both assemble other robots and to adapt to a specific set of tasks.

About Machine Learning

#Machine #Learning has gained prominence as an important element of Data Science. It is allowing businesses to better cater to their customers, who have varied tastes and preferences. #Machine Learning is a subset of #Artificial #Intelligence and it uses #AI to provide systems the ability to learn and improve customer experience without having to be programmed.

This in itself, is enough to make #MachineLearning an interesting domain.# Machine #Learning is being implemented in multiple fields and businesses, and it is reaping great benefits. After all, adapting to customer requirements and leveraging data is a sound plan.

Imagine an application that will show you results depending on the data collected about your preferences, and choices. An application that will make almost accurate collections, which are tailor-made for you. I have been using the subscription-based music application, Saavn, which has great Machine Learning capability.

It analyses user preferences and automatically improves the experience, simply by generating playlists according to the music choices of the customer. I am, in fact, very satisfied and do not mind paying the subscription fee. I gave you this example to illustrate how far the roots of# Machine #Learning has reached. It is no more jargon, it is present and functioning all around us.