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Machine Learning Tips From Amazon Web Services: What Are The Key Takeaways?

Machine Learning Tips From Amazon Web Services: What Are The Key Takeaways?

Machine learning is a subset of Artificial Intelligence, or, AI which draws from its past experiences to predict future action and act on it.  The growing demand for Machine Learning course in Gurgaon, is a clear pointer to the growth the field is experiencing.

If you have been on Youtube frequently then you would certainly have noticed, how it recognizes the choices you made during your last visit and it suggests results based on those past interactions.

The world of machine learning is way past its nascent stage and has found several avenues where its application has become manifold over the years. From predictive analysis to pattern recognition systems, Machine learning is being put to use for finding an array of solutions.

AWS has been a pioneer in the field as it embraced the technology almost 20 years back, recognizing its potential growth across all business verticals.

 At a recently held online tech conference, vice president of Amazon AI shared his concerns and ideas regarding the journey of ML while pointing out the hurdles still in the way and which need to be addressed.  Here are the key takeaways from the discussion

Growing need for Machine learning

Amazon was quick to realize a crucial fact in the very beginning that consumer experience is a crucial aspect of business which needs to get better with the application of ML.

Despite the impressive trajectory of machine learning and its growing application across different fields there are still issues which pose serious challenge. There are certain issues which if tackled properly would pave the way for a smarter future for all.

Get your data together

Businesses intent on building a machine learning strategy need to understand that they are missing a vital component of the model which is the data itself.  Setting out business objectives is not enough; machine learning model is basically built upon data. You need to feed the model data, accumulated over a period of time which it could analyze and to predict future action. 

Clarity regarding machine learning application

It is understood that you need to apply machine learning in order to find solutions, to do that you need to identify that particular area of your business where you need the solution. Once you have done that, you need clarity regarding data backup, applicability and impact on business. Swami Sivasubramaniam, vice president of Amazon AI at Amazon Web Services referred to these aspects as “three dimensions”.

Another point he stressed was regarding a collaboration between domain experts and machine learning teams.

Dearth of skill

Although there has been a quantum growth in the application of machine learning, there is a significant lack of trained personnel for handling machine learning models. Undergoing a Machine Learning course in Gurgaon, could bridge the skill gap.

Since, this sector is poised to grow, people willing to make a career should consider undergoing training.

In fact, organizations looking to implement machine learning model, should send their employees for corporate training programs offered at a premier MIS Training Institute in Delhi NCR.

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Avoid undifferentiated heavy lifting

Most companies tend to shift their focus from the job at hand and  according to Sivasubramaniam, starts dealing with issues like “server hosting, bandwidth management, contract negotiation…”, when they should only be concerned with making the model work for their business model and should look for cloud-based solutions for handling the rest of the issues.

Addressing these issues would only pave the way towards a brighter future where Machine learning would become an integral part of every business model.

Source: https://searchenterpriseai.techtarget.com/feature/How-to-build-a-machine-learning-model-in-7-steps

 


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How Artificial Intelligence Powers Earthquake Prediction

How Artificial Intelligence Powers Earthquake Prediction

Artificial Intelligence is the key to the future of weather forecasting, a fact well known. But did you know it is also powering earthquake prediction the world over? Yes. Artificial Intelligence techniques like machine learning are gradually being enlisted in forecasting seismic activity.

While earthquake prediction has not yet become an exact science, efforts are on to make improvements and make forecasts reliable. For this, AI powered neural networks, the same technology behind the success of driverless cars and digital assistants, is being used to enhance research based on seismic data.

Neural Networks

A report says that, “Scientists say seismic data is remarkably similar to the audio data that companies like Google and Amazon use in training neural networks to recognize spoken commands on coffee-table digital assistants like Alexa.”

When it comes to studying earthquakes, it is the computer, a fast and able machine, looking for patterns in mountains of data rather than relying on the weary eyes of a scientist. Also, instead of a sequence of words, what the computer is studying is a sequence of ground-motion measurements.

Studying Aftershocks
Image Source: cbs8.com

Studying Aftershocks

Scientists in the US have experimented with neural networks to accelerate earthquake analysis and the speed at which they were producing results and studies was 500 times faster than they could in the past. Also, AI is not only useful in studying earthquakes but it is being used in forecasting earthquake aftershocks as well.

In fact, researchers say it is a time of great scientific advancement, so much so, that “technology can do as well as — or better than — human experts”.

Artificial Intelligence
Image Source: smithsonianmag.com

Artificial Intelligence

Geophysicist Paul Johnson’s team in the US has been studying earthquakes for quite some time now and it has made advancements in “using pattern-finding algorithms similar to those behind recent advances in image and speech recognition and other forms of artificial intelligence, (where) he and his collaborators successfully predicted temblors in a model laboratory system — a feat that has since been duplicated by researchers in Europe”, says a report.

Now Mr Johnson’s team has published a paper wherein artificial intelligence has been used to study slow slip earthquakes in the Pacific Northwest. While advancements are being made in the field of studying slow slip earthquakes, it is the bigger and more potent ones that really need to be studied. But they are rare. So the question remains – Will Machine Learning be able to analyse a small data set and predict with confidence the next big earthquake?

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Machine Learning 

Researchers claim “that their (machine learning) algorithms won’t actually need to train on catastrophic earthquakes to predict them.” Studies conducted recently suggest “seismic patterns before small earthquakes are statistically similar to those of their larger counterparts”. So, a computer trained on hundreds and thousands of those small temblors might be able enough to predict the big ones.

For more on artificial intelligence, and its varied applications, do peruse the DexLab Analytics website today. DexLab Analytics is a premier institute in India offering Machine Learning courses in Delhi.

 


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How Machine Learning Helped Demystify Locust Breeding Sites

How Machine Learning Helped Demystify Locust Breeding Sites

Even as the coronavirus pandemic rages on and India is living through a strict lockdown to abate the spread of the novel virus, a disastrous spell of a plague of crop destroying locusts has struck Rajasthan, Gujarat and parts of Madhya Pradesh.

Threatening to balloon into an agrarian crisis, the destruction of crops on this scale is being seen as one “worst in decades”. In fact, such large scale breeding of locusts and an attack by them is the worst in 27 years, government officials said.

In such frightening circumstances, what we can truly bank upon to detect and fight locust attacks is advanced technology like machine learning techniques. This essay aims to demystify how machine learning can be used to detect locust breeding patterns by studying soil moisture through remote sensing.

The Study

Image Source: spiedigitallibrary.org

A study called “Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture” shows how researchers have “used two machine learning algorithms (generalized linear model and random forest) to evaluate the link between hopper presences and SM (Soil Moisture) conditions under different time scenarios…It was found that an area becomes suitable for breeding when the minimum SM values are over 0.07  m3  /  m3 during 6 days or more. These results demonstrate the possibility to identify breeding areas in Mauritania by means of SM, and the suitability of ESA (European Space Agency) CCI (Climate Change Initiative) SM product to complement or substitute current monitoring techniques based on precipitation datasets.”

The Findings

The study found that “it is widely assumed” that rainfall over 25 mm in two consecutive months is conducive to locust breeding. Likewise, various soil moisture conditions affect breeding patterns greatly. So, the study finds that it is important to have “variable creation as a previous step to modeling”. Different time intervals of locust breeding were tested by the researchers for model creation. Also, different soil moisture values were considered.

Image Source: spiedigitallibrary.org

It was found that the “highest performance was acquired by the RF (Random Forest) algorithm when dividing the whole survey time into ranges of 6 days, and selecting the minimum SM as the variable value.” GLMs of Generalised Linear Models, however, did not work well according to the study.

The applied methodology of machine learning offers promising results to accurately identify breeding areas based on data pertaining to 30 years of SM values. The ESA CCI soil moisture data is one of the most authoritative ones in the world. Thus the researchers who conducted this study are confident that their results signify a breakthrough in locust monitoring technique prevalence in the world.

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Conclusion

This study, thus, proposes a machine learning approach based on SM time series “to predict breeding areas, by means of remote sensing”. Artificial Intelligence and Machine Learning will help future researchers and scientists to study and produce better warning systems based on the results of this study. In this study only soil moisture data has been used but more variables like temperatures can also be taken into account to accurately predict breeding grounds in the future.

For more on machine learning applications, do peruse the Dexlab Analytics website today. This article was brought to you by DexLab Analytics, a premier institute offering Machine Learning courses in Delhi.

 

The blog has been sourced fromMachine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture

 


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93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

The Covid-19 pandemic has struck India like it has scores of countries across the world. As of May 27, over 1,51,000 Indians have been tested positive for the novel virus and over 4000 people have died due to the contagious disease. India has been under lockdown for over two months now in an attempt at abating the spread of the virus due to movement and contact.


 

With all offices closed and work from home decreed across numerous sectors of the economy, professionals have been forced to adapt to a new mode of work and training. With more time on hand since they are working from home, professionals are upgrading their skills by taking up online training modules and classes. A recent LinkedIn survey throws light on this phenomenon.

LinkedIn’s Work Force Confidence Index

India’s foremost social networking site that helps individuals network with professional peers and find jobs and appointments has conducted a survey called Work Force Confidence Index. As per the survey conducted between April 27 and May 3, “India’s professionals are logging learning hours for not just knowledge acquisition but also to increase productivity. About half of respondents from mid-market firms joined courses that help them manage time better, improve prioritisation or stay organised”.

93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

93% respondents to upskill online in next two weeks

According to LinkedIn News India, 1040 professionals were surveyed by LinkedIn and 93% of them said “their time spent on e-learning will either increase or remain the same over the next two weeks”. Moreover, 60% of the respondents of which 74% were from the engineering domain said e-learning was a conduit to furthering industry knowledge. “Advancing in one’s career was a driver for 57% of all respondents and 3 in 10 active job seekers undertook e-learning to make a career pivot,” said LinkedIn News India.

What respondents learnt

Of the respondents, 45% said they hoped to learn to collaborate with peers through online learning in lockdown. Also, 43% said they wished to learn to manage time and prioritise and stay organised. Moreover, 40% said they hoped to learn something unrelated to work through online platforms. Becoming a leader and managing personal finances were pegged at 37% and 32% respectively by the study as goals and 24% said e-learning could actually lead to a change in career paths for them.

Advantages of e-learning

Travelling to work and back is taxing and time consuming. When you are working from home, you save on energy and time that can be used for something productive like e-learning training modules. They are easy on the pocket, accessible from absolutely anywhere you are and convenient to absorb and retain information and new things learnt. Moreover, there is a large online community to help you out with study material and guidance.

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There are many popular e-learning courses in India, especially those around data science and artificial intelligence. DexLab Analytics is a premier credit risk modeling training institute that also trains professionals in artificial intelligence, machine learning and data science. This article was brought to you by DexLab Analytics.

 


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How AI is Powering Manufacturing in 2020

How AI is Powering Manufacturing in 2020

The world has seen a transformation in its economic activities since the coronavirus pandemic broke out. Economies have come to a grinding halt and manufacturing has dipped. Now what nations need is resilience and strength to carry on production in all sectors. What they are most depending on is the power of Artificial Intelligence to enhance the manufacturing process and help save money and drive down costs.

Here are some examples of how AI is powering the manufacturing sector in 2020.

  • AI is being used to transform machinery maintenance and quality in manufacturing operations today, according to Capgemini.
  • Caterpillar’s Marine Division is using machine learning to analyze data on how often its shipping equipment should be cleaned helping it save thousands of dollars.
  • The BMW Group is using AI to study manufacturing component images in and spot deviations from the standard production procedure in real-time.

In fact, a study shows that in the four earlier global economic downturns companies using AI were actually successful in increasing both sales and profit margins. Companies are all striving to utilize human experience, insights and AI techniques to give manufacturing a fillip in these times of a crisis.

Manufacturing using AI in real-time

Real-time monitoring of the manufacturing process is advantageous because it translates to sorting out production bottlenecks, tracking scrap rates and meeting customer deadlines among other things. The huge cache of data used can be utilized to build machine learning models.

Supervised and unsupervised machine learning algorithms can study multiple production shifts’ real-time data within seconds and predict processes, products, and workflow patterns that were not known before. A report suggests 29% of AI implementations in manufacturing are for maintaining machinery and production assets.

Detecting Outages

It was found that the most popular use of AI in manufacturing is predicting when equipment are likely to fail and suggesting optimal times to conduct maintenance. Companies like General Motors analyze images of its robots from cameras mounted above to spot anomalies and possible failures in the production line and thus preempt outages.

Optimizing Design

General Motors uses AI algorithms to give and produce optimized product design. General Motors can achieve the goal of rapid prototyping with the help of AI and ML algorithms. Designers provide definitions of the functional needs, raw materials, manufacturing methods and other constraints and the company along with AutoDesk has customized Dreamcatcher to optimize for weight and other vital criterion. In this way, AI comes together with human endeavor to produce a-class product designs that cost lesser.

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Inconsistencies

Nokia has begun using a video application that takes the help of machine learning to alert an assembly operator if there are inconsistencies in the production process in one of its factories in Oulu, Finland. It alerts a machine operator about inconsistencies in the production of electronic items and this helps preempt poor production process and helps the company save on a lot of money and capital.

There are many other production processes AI is helping revolutionize. Only time will tell how much of AI will power the manufacturing sector. But this technological advancement is surely making an impact on economies worldwide. Meanwhile, for more details, do peruse the DexLab Analytics website. DexLab Analytics is a premiere machine learning institute in Gurgaon.

 


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5 Chatbots You Should Know About

5 Chatbots You Should Know About

Chatbots or “conversational agents” are software applications that mimic or imitate written or spoken human speech for the purposes of facilitating a conversation or interaction with a human being.

These applications have become one of the most ubiquitous software applications out there with the advancement of machine learning technology and NLP.

“Today’s chatbots are smarter, more responsive, and more useful – and we’re likely to see even more of them in the coming years… chatbots are used most commonly in the customer service space, assuming roles traditionally performed by living, breathing human beings such as Tier-1 support operatives and customer satisfaction reps.”

Conversational agents are becoming a common occurrence partly due to the fact that barriers to entry in creating chatbots such as sophisticated programming knowledge have become redundant.

How Chatbots work

The crux of chatbot technology is natural language processing or NLP, the same technology “that forms the basis of the voice recognition systems used by virtual assistants such as Google Now, Apple’s Siri, and Microsoft’s Cortana.” “Chatbots process the text presented to them by the user…infer what they mean and/or want, and determine a series of appropriate responses based on this information.”

Here are 5 companies using chatbots for various roles like marketing, communicating with marginalized groups and patients suffering from sleeplessness and memory loss.

Endurance

Russian technology company Endurance developed a companion chatbot to help dementia patients cope with decreased verbal ability. Many patients with Alzheimer’s disease use the chatbot to converse with. In turn, the chatbot identifies deviations in conversational patterns of the patient that might indicate a problem with memory and recollection.

Casper

Casper’s Insomnobot 3000 is a conversational agent that aims to help insomniacs by posing as a companion to talk to while the rest of the world sleeps. However, at this point, “Insomnobot 3000 is a little rudimentary.”

UNICEF

International child advocacy nonprofit UNICEF is using chatbots to help people living in developing countries speak out about the most urgent needs in their communities. The bot, named U-Report, focuses on large-scale data gathering via polls. UNICEF then uses feedback as the basis for potential policy recommendations.

MedWhat

This chatbot aims at making medical diagnoses faster, easier, and more transparent for both patients and physicians. MedWhat is powered by a highly sophisticated machine learning system that offers increasingly accurate responses to user questions based on behaviors that it “learns” by interacting with human beings. Also, it acts as a repository of a vast source of medical journals and medical advice.

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Roof Ai

Roof Ai is a chatbot that helps real-estate marketers to “automate interacting with potential leads and lead assignment via social media”. The bot identifies potential leads via social media and responds immediately, irrespective of the time of the day. “Based on user input, Roof Ai prompts potential leads to provide a little more information, before automatically assigning the lead to a sales agent.”

To learn more about machine learning powered technology, follow DexLab Analytics. DexLab Analytics is a premiere institute for Machine Learning training in Gurgaon.

 


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A Guide to Free Ebooks on Statistics and Machine Learning

A Guide to Free Ebooks on Statistics and Machine Learning

Machine Learning is an acquired knowledge science. It has to be taught and studied. For this, it is imperative to have the best books on the subject at hand. However, most books on the subject are expensive and not easily accessible. This is only fair given the amount of hard word that goes into writing these books.

In a situation this critical, it is best to rely on the good old Internet for assistance. There are some good Samaritans who have chosen to make their works freely available to all. Here is a great guide to free ebooks available online so you can brush up on your concepts and be industry ready at the earliest.

Think Stats – Probability and Statistics for Programmers by Allen B Downey

For the free ebook click here http://www.greenteapress.com/thinkstats.

This is an introduction to statistics and probability for those who have a basic grounding in Python programming. “It’s based on a Python library for probability distributions (PMFs and CDFs). To make things easier for the reader, most of the exercises have short programs,” says a report.

Bayesian Reasoning and Machine Learning by David Barber

For the free ebook click here http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/091117.pdf.

When it comes to Bayesian statistics, this book is a classic. “This takes a Bayesian statistics approach to machine learning.”This is a book worth checking out for anyone getting into the machine learning field and trying to make a career out of the subject.

An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

For the free ebook click here http://faculty.marshall.usc.edu/gareth-james.

This popular entry is an introduction to data science through machine learning. “This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field. It’s filled with practical real-world examples of where and how algorithms work. For those with an inclination towards R programming, this book even has practical examples in R.”

Understanding Machine Learning by ShaiShalev-Shwartz and Shai Ben-David

For the free ebook click here https://www.cse.huji.ac.il/~shais/UnderstandingMachineLearning/index.html.

“This book gives a structured introduction to machine learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms. Following that, it covers a list of ML algorithms, including…stochastic gradient descent, neural networks, and structured output learning.”

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A Programmer’s Guide to Data Mining by Ron Zacharski

For the free ebook click here http://guidetodatamining.com.

This book has chapters covering recommendation systems. “It takes a…visually entertaining look at social filtering and item-based filtering methods and how to use machine learning to implement them. Other concepts like Naive Bayes and Clustering are also covered. There is a chapter on Unstructured Text and how to deal with it, in case you are thinking about getting into Natural Language Processing. Examples in Python are also available in case you want to practice.”

For more on Machine Learning do peruse the DexLab Analytics website. DexLab Analytics is a premiere institute offering Machine Learning courses in Delhi.

 


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Top Six Applications of Natural Language Processing (NLP)

Top Six Applications of Natural Language Processing (NLP)

Words are all around us – in the form of spoken language, texts, sound bytes and even videos. The world would have been a chaotic place had it not been for words and languages that help us communicate with each other.

Now, if we were to enhance language with the attributes of artificial intelligence, we would be working with what is known as Natural Language Processing or NLP – the confluence of artificial intelligence and computational linguistics.

In other words, “NLP is the machine’s ability to process what was said to it, structure the information received, determine the necessary response and respond in a language that we understand”.

Here is a list of popular applications of NLP in the modern world.

1. Machine Translation

When a machine translates from one language to another, “we deal with “Machine” Translation. The idea behind MT is simple — to develop computer algorithms to allow automatic translation without any human intervention. The best-known application is probably Google Translate.”

2. Voice and Speech Recognition

Though voice recognition technology has been around for 50 years, it is only in the last few decades, owing to NLP, have scientists achieved significant success in the field. “Now we have a whole variety of speech recognition software programs that allow us to decode the human voice,”be it in mobile telephony, home automation, hands-free computing, virtual assistance and video games.

3. Sentiment Analysis

“Sentiment analysis (also known as opinion mining or emotion AI) is an interesting type of data mining that measures the inclination of people’s opinions. The task of this analysis is to identify subjective information in the text”. Companies use sentiment analysis to keep abreast of their reputation and customer satisfaction.

4. Question Answering

Question-Answering concerns building systems that “automatically answer questions posed by humans in a natural language”. The real examples of Question-Answering applications are: Siri, OK Google, chat boxes and virtual assistants.

5. Automatic Summarization

Automatic Summarization is the process of creating a short, accurate, and fluent summary of a longer text document. The most important advantage of using a summary is it reduces the time taken to read a piece of text. Here are some applications – Aylien Text Analysis, MeaningCloud Summarization, ML Analyzer, Summarize Text and Text Summary.

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6. Chatbots

Chatbots currently operate on several channels like the Internet, web applications and messaging platforms. “Businesses today are interested in developing bots that can not only understand a person but also communicate with him at one level”.

While such applications celebrate the use of NLP in modern computing, there are some glitches that arise in systems that cannot be ignored. “The very nature of human natural language makes some NLP tasks difficult…For example, the task of automatically detecting sarcasm, irony, and implications in texts has not yet been effectively solved. NLP technologies still struggle with the complexities inherent in elements of speech such as similes and metaphors.”

To know more, do take a look at the DexLab Analytics website. DexLab Analytics is a premiere institute that trains professionals in NLP deep learning classification in Delhi.

 


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How Pharma Companies Are Using Machine Learning and AI to Fight Against COVID-19

How Pharma Companies Are Using Machine Learning and AI to Fight Against COVID-19

In the midst of a crisis as big as COVID-19 which has as per the current update taken more than 80,000 lives around the world and caused nations to go under a lockdown, machine learning and artificial intelligence has played a vital role in detecting COVID-19 positive cases and helping fast track the tests and analysis of drugs that can be a solution to the problem.

Countries around the world are building algorithms to better understand the molecular structure of the diseaseso that the so-called protein around the SARS-CoV-2, the virus that causes COVID-19, can be blocked.

Use of AI and machine learning around the world

The legendary story of a Canada-based AI named Bluedot is not something one can forget. Buledot is a low cost AI based tool that predicted the outbreak of the disease in December 2019 and also predicted the top 20 destinations where passengers from Wuhan, the origin of pandemic, would arrive.

Deargen a South-Korean company built an AI tool named MT-DTI to test and analyze the molecular bond of the SARS-CoV-2 protein and recommend medication on the basis of the same. Even though their recommendations are yet to be reviewed and tested properly, their find might prove to be a super solution to reduce the number of death counts and covid positive cases.

Insilico Medicine, a Hong-Kong based company instead of building a platform to recommend the currently available medication to break the protein of covid-19 virus and stop it from replicating, built an AI based tool to formulate and test the potency of new and chemically advanced medication to break the chain.

Benevolent AI, a British startup based on its machine learning tool discovered six important covid-19 virus fighting compounds that can prove to be helpful in curing and limiting the spread of the disease. Out of the six compounds discovered, ‘baricitinib’, a compound used to cure rheumatoid arthritis according to the sources, proves to be the safest compound that can be tested on the covid positive volunteers.

Use of AI and machine learning in India

The spread of the covid-19 pandemic in India began by the end of January 2020 and till date more than 5000 cases have been reported. The Government of India declared a lockdown by mid-March 2020 and in the middle of all this when misinformation and rumors were rising day by day, to tackle the problem the Government of India launched its covid-19 tracking app called Aarogya Setu that means ‘a bridge of health’.

This AI-based app uses bluetooth and the mobile location of the user and informs them if there is any person covid positive within 6 feet of their location or not. The app checks the user’s data in their own database and in case the data matches a corona positive patient then it only notifies their presence keeping their identity anonymous.

India has also launched a whatsapp chatbot and any one using the whatsapp platform can access the chatbot by simply typing “MyGov Corona Helpdesk” and get the relevant and authentic information relating to the symptoms and how they can get help from direct sources.

Apart from the above initiatives to tackle the pandemic, medical practitioners, data analysts and data scientists from around the world are trying to find ways and means and analyzing the database to capture the trends and are working day and night to find best possible cure to fight the disease.

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To know more, do read DexLab Analytics previous blogs on how AI is helping fight the coronavirus pandemic here. DexLab Analytics is a premiere artificial intelligence training institute in Gurgaon that gives Machine Learning training in Gurgaon.

 


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