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Know the 5 Best AI Trends for 2019

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Know the 5 Best AI Trends for 2019

Artificial Intelligence is perhaps the greatest technological advancement the world has seen in several decades. It has the potential to completely alter the way our society functions and reshape it with new enhancements. From our communication systems to the nature of jobs, AI is likely to restructure everything.

‘Creative destruction’ has been happening since the dawn of human civilization. With any revolutionary technology, the process just speeds up significantly. AI has unleashed a robust cycle of creative destruction across all employment sectors. While this made old skills redundant, the demand and hence acquisition of superior skills have shot up.

The sweeping impact of AI can be felt from the fact that the emerging AI rivalry between USA and China is hailed as ‘The New Space Race’! Among the biggest AI trends of 2018 was China’s AI sector – it came under spotlight for producing more AI-related patents and startups compared to the US. This year, the expectations and uncertainties regarding AI both continue to rise. Below we’ve listed the best AI trends to look out for in 2019:

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AI Chipsets

AI wholly relies on specialized processors working jointly with CPU. But, the downside is that even the most innovative and brilliant CPUs cannot train an AI model. The model requires additional hardware to carry out higher math calculations and sophisticated tasks such as face recognition.

In 2019, foremost chip manufacturers like Intel, ARM and NVidia will produce chips that boost the performance speed of AI-based apps. These chips will be useful in customized applications in language processing and speech recognition. And further research work will surely result in development of applications in fields of automobiles and healthcare.

Union of AI and IoT

This year will see IoT and AI unite at edge computing more than ever. Maximum number of Cloud-trained models shall be placed at the edge layer.

AI’s usefulness in IoT applications for the industrial sector is also anticipated to increase by leaps and bounds. This is because AI can offer revolutionary precision and functionality in areas like predictive maintenance and root cause analysis. Cutting edge ML models based on neural networks will be optimized along with AI.

IoT is emerging as the chief driver of AI for enterprises. Specially structured AI chips shall be embedded on majority of edge devices, which are tools that work as entry points to an entire organization or service provider core networks.

Upsurge of Automated ML

With the entry of AutoML (automated Machine Learning) algorithms, the entire machine learning subject is expected to undergo a drastic change. With the help of AutoML, developers can solve complicated problems without needing to create particular models. The main advantage of automated ML is that analysts and other professionals can concentrate on their specific problem without having to bother with the whole process and workflow.

Cognitive computing APIs as well as custom ML tools perfectly adjust to AutoML. This helps save time and energy by directly tackling the problem instead of dealing with the total workflow. Because of AutoML, users can enjoy flexibility and portability in one package.

AI and Cyber security

The use of AI in cybersecurity is going to increase by a significant measure because of the following reasons: (i) there a big gap between the availability and requirement of cybersecurity professionals, (ii) drawbacks of traditional cybersecurity and (iii) mounting threats of security violations that necessitate innovative approaches. Depending on AI doesn’t mean human experts in the field will no longer be useful. Rather, AI will make the system more advanced and empower experts to handle problems better.

As cybersecurity systems worldwide are expanding, there’s need to cautiously supervise threats. AI will make these essential processes less vulnerable and way more efficient.

Need for AI Skilled Professionals:

In 2018, it was stated that AI jobs would be the highest paying ones and big enterprises were considering AI reskilling. This trend has been carried over to 2019. But companies are facing difficulties trying to bridge the AI skills gap in their employees.

Having said that, artificial intelligence can do wonders for your career if you’re a beginner or advanced employee working with data or technology. In Delhi, you’ll find opportunities to enroll for comprehensive artificial intelligence courses. DexLab Analytics, the premier data science and AI training institute, offers advanced artificial intelligence certification in Delhi NCR. Check out the course details on their website.

 

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CoCalc and Juno Help You Master Data Science on Mobile Phones, Here’s How!

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CoCalc and Juno Help You Master Data Science on Mobile Phones, Here’s How!

Innovation has been at the heart of data science evolution. Cutting-edge technology advancements are found influencing data science training and learning mediums. Besides conventional channels, such as desktops and laptops, there’s now a new way to master machine learning coding systems, i.e. through mobile phones. A robust combination of tools is now at your service to help you code and monitor complex machine learning frameworks using mobile phones.

Take a look at these two tools; they are perfect tools for completing random machine learning tasks.

CoCalc

It is a pioneering web app that hosts coding environments amidst the cloud. It is a sophisticated online work domain that helps you perform mathematical calculations in the cloud. Later, you can share your projects even successfully.

CoCalc is primarily student-friendly software. It is crafted for students’ training modules and machine learning training programs. Thus, it comes loaded with a slew of potent data science packages, including Pandas, and all this makes it easier to develop Jupyter notebooks.

A lot of teachers are found using CoCalc to design courses. People can even chat using CoCalc, which further enhances collaboration on projects and improves the overall learning experience. What’s more, its customer service is also quite responsive. Their team of experts is always a step ahead to assist you.

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Juno

The notable iPhone app helps user code in CoCalc on any mobile devices. In fact, Juno is specially designed for mobile and boasts of superb keyboard support. It tackles multi-screen multitasking challenges and provides support to Python code completion.

Quite interestingly, Juno is largely free for users. That makes it more suitable for mastering demographic. The experts have tried their best to make the free versions of Juno as interactive and fun as possible – engage with introductory notebooks available on Python, Matplotlib, Jupyter, SciPy and NumPy without shelling any extra penny. They not only keep things interesting but also feel good on the pocket.

However, if you want to savour the benefits of Juno Pro that connects you to an arbitrary Jupyter server, you have to make a one-time purchase and use it on all your devices.

Power of Combination

Surely, an effective combination of these two abovementioned tools comes as a soothing balm in the life of working professionals. They are the ones who need to be constantly on the go. Now, with these powerful tools at the tap of their fingers, they can work on myriad data science assignments while being at home or travelling.

However, as a downturn, coding on mobile is not as easy as it seems to be. Mobile devices are not highly configured to support rapid content creation. As a result, they take more time finishing an assignment as compared to laptops and desktops.

But, of course, if you are an adult learner, Juno and CoCalc are sure-fire ways to make progress along the bustling field of artificial intelligence and machine learning. In case, you want to learn more about AI, opt for an encompassing artificial intelligence certification in Delhi NCR.

 

The blog has been sourced from ― www.analyticsindiamag.com/learn-data-science-on-your-mobile-phone-with-these-tools

 

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Advancement in Genomics with Artificial Intelligence

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Advancement in Genomics with Artificial Intelligence

Artificial Intelligence is raging hot, and the healthcare industry is not left behind. Reports suggest AI will help the healthcare industry generate $6.7 billion in revenue. In healthcare, genomics is one of the most notable areas that have evolved significantly after the rise of AI. Involving processes like gene editing and sequencing, genomics is largely performed in agriculture, customized medicine industry and animal husbandry.

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Researchers have long been conducting DNA analysis. However, their initiatives used to be stalled midway because of several challenges – such as the massive size of the genome, high cost, regulatory factors, prediction norms and technology limitations. On top of that, a vast amount of data on genes and genomes further added up to the problem of ostentatiously large amount of patient data. 

Fortunately, today, researchers are better off using machine learning for genomics – they can now perform gene synthesis, construct precision and personalized medicines and understand the genetic makeup of each orgasm amongst others.

Major Development Highlights

  • Elevation Project by Microsoft grabbed eyeballs when its researchers collaborated with a set of biologists from UC Berkeley to assist in gene editing using AI. They decided to combine their efforts and increase efficiency and accuracy of CRISPR technology – which is basically a gene-editing tool for resulting in genetic improvements.

Together, they also launched Elevation, which uses Machine Learning technology to forecast effectively the off-target effects that take place during the process, thus increasing the efficiency of the entire process.

  • Nvidia and Scripps Research Translational Institute (SRTI) improvised their operations for developing deep learning tools and methods. They aim to process and analyze genomic and digital medical sensor data that would increase the use of AI and prevent the spread of diseases, promote health and streamline a host of biomedical research measures.
  • Google released DeepVariant – it is a cutting-edge deep learning model designed to analyze genetic succession. Last year, they devised a new version DeepVariant v0.6, which features brand new accuracy developments that helps get a more accurate picture of an entire genome.
  • Deep Genomics, a budding startup in Canada is found leveraging artificial intelligence to decipher genome and ascertain the most suitable drug therapies based on DNA found on the cell. The company specializes in the field of personalized medicines.

Genomics in India

Following the footsteps of its global partners, India too is slowly maneuvering into the space of AI-powered genomics – several startups, like Artivatic Data Labs are building power in this new field with radical innovations. Another Chennai-based startup, Orbuculum is leveraging AI to predict debilitating diseases and optimize disease diagnosis.

End Note

Major breakthroughs are happening in the new world of genomics. But, of course, understanding human genome and developing genomic medicines is beyond the human capabilities. Often, it needs analysis of millions and millions of data and performs several repetitive tasks for which AI seem to be the most feasible solution. Undeniably, advancements in AI and ML technology have resulted in a comprehensive understanding of genomics – they are the best way to interpret and proceed on genomic data.

FYI: DexLab Analytics is a top-notch artificial intelligence training institute in Gurgaon. It offers excellent in-demand skill training for students, professionals and anyone who is interested in data.

 

The blog has been sourced from ― www.analyticsindiamag.com/when-artificial-intelligence-meets-genomics

 

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Self Healing Machines: The Future Weapon to Fight Cyber Crime

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Self Healing Machines: The Future Weapon to Fight Cyber Crime

Science and technology is the lifeblood of digital success. Connected devices, innovation trends and swift urbanization are transforming the economies for good. But, as it’s said, you have to bite the bitter with the sweet – digital dependency on shared infrastructure comes with its own cons.

“The more machines, the more critical our cyber-security problem becomes. Increased attacker sophistication means devices are now attacked at the deepest levels, including firmware and embedded software. In this new threat landscape, we cannot just rely on manual human intervention. We have to change the paradigm,” shares Boris Balacheff, Chief Technologist for Security Research and Innovation at HP.

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Self Healing Machine is the Answer

For that, conventional security devices won’t be enough – self-healing machines are the answer. They not only detect advanced cyber attacks but also shut off the system completely and restore it later without human intervention. Their response is quick.

As cyber criminals are on the rise, HP strives to reinvent device security to a great extent. For twenty years and more, HP Labs have been working on computer security. Its latest innovation drive is focused on bringing down cyber-resilience at hardware level. Just like the perpetrators employ newer tech capabilities, inventors of today’s security devices should also look up to artificial intelligence – in that respect, self-healing machines should never be only reactive but also proactive. The design and architecture of the machines should be as if they can immediately respond to attacks as well as fix their flaws then and there before someone else does.

Security Is In Focus

The prognosis of the World Economic Forum’s Global Centre for Cybersecurity and the UC Berkeley Center for Long Term Cybersecurity suggests that in the age of innovation and technology modification, security is at the fulcrum of future digital success. It needs combined effort and trust from law enforcement, public and private sector and the entire civil society. In order to build a secure and better cyberspace, we need to strike private-public partnership and work together.

To say the least, government and private players are coming together to meet several challenges transpiring from the intersection of security and innovation. Major breakthroughs in the field of personalized healthcare, 3D digital printing and AI are hitting new highs.

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Fortunately, forecasts say self-healing machines can prove useful across a wide range of operations. For example, they can reduce the load of supporting previous products that still relies on security updates. They can quickly predict malfunctions, even before the device starts showing anomalies. In fact, some of the IoT devices are already attached to vibration and ultrasonic sensors to make monitoring easy.

Conclusion

But, of course, self-healing machines can never be left alone to guard themselves – most of the companies will seek a human in the loop. Humans form an integral role in operating self-healing machines. “We need to continue to reinvent the security of the machines that we will depend upon for years to come,” shares Balacheff. “It’s our only way to win.”

DexLab Analytics offers state of the art artificial intelligence certification in Delhi NCR – the course infrastructure is well fed, student-friendly and follows a practical approach.

The blog has been sourced from ― www.wired.com/brandlab/2018/10/fighting-cybercrime-self-healing-machines
 

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How Artificial Intelligence Can Help Tackle Emotionally Charged Situations

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How Artificial Intelligence Can Help Tackle Emotionally Charged Situations

An increasing number of jobs are incorporating artificial intelligence. But artificial intelligence applications tacking emotional problems is something rarely heard of.

Researches reveal that people who face workplace harassment don’t always report it to concerned authorities– fear of judgment and discomfort in recalling emotionally disturbing situations being among the many reasons. However, scientists believe that people shying away from human assistance might be able to open up in front of a tech.

Let’s look at some examples of artificial intelligence applications assisting humans in emotional situations.

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Spot:

Back in February 2018, Shaw and his partners who are software engineers by profession introduced Spot. This web-based chatbot is powered by artificial intelligence and helps people share and report painful incidents. The app incorporates expertise of psychologists as well as police personnel and has a robust interview technique ensuring recorded narratives are highly accurate. Moreover, the person interacting has the option to remain anonymous.

Spot is trained to gather information from the person’s initial description of the incident and respond to cues by posing specific questions, but never asking leading questions that might intimidate the user. Finally, a detailed and time-stamped PDF report is generated, which the user can choose to share or keep personal.

Advantages of artificially intelligent tools dealing with human emotions:

  • Apps are accessible 24/7
  • No prior appointment with HR is needed
  • Eliminate inconsistencies associated with human interactions
  • Help overcome a common human weakness– emotional memory recall
  • Machines can act more logically in situations where humans fail to think clearly.

Navigating through human emotions:

Ixy is another AI based app that aims to minimize anxiety in human chats by facilitating interactions over texts. It samples texts to help users understand how he/she is perceived by others.

Israeli enterprise Beyond Verbal has launched ‘’emotional analytics’’ software, which is a patented technology that measures the emotional factor of voice based on its modulation. This tech is employed in call centers so worker interactions can be fine-tuned as per customer needs. Other important uses of this software include examining employee morale and improving the working of AI virtual assistants by helping understand elements between lines of human conversations.

Yoram Levanon, the inventor of Beyond Verbal tech and its chief science officer, visualizes the development of applications more advanced than the ones described above, such as virtual assistants that can examine vocal biomarkers and predict a person’s emotional and physical state. He firmly believes that modern AI apps need to work in tandem with human emotions; that’s the key for AI to be complementary to human work and not its replacement.

To help, not replace humans:

Recently, there’s been a lot of unease and insecurities associated with AI. There’s fear that AI might eat into our human side and corrode human emotions. But the opposite of this is actually possible and these apps prove that point. Done correctly, machines can help us become better humans.

In today’s tech-driven society, the first step in unlocking the powers of modern equipment is enrolling for comprehensive Artificial Intelligence Certification Courses. From factory machinery, to chatbots, and now even human emotions, everything is forming inseparable bonds with AI. Join the leading artificial intelligence training institute in Gurgaon DexLab Analytics and begin a successful journey in this field. For course details, look up the brochure on DexLab’s website.

 

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Sell Yourself Well: Most Common Artificial Intelligence Interview Questions

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Sell Yourself Well: Most Common Artificial Intelligence Interview Questions

Artificial Intelligence is seeping through our daily lives. Day by day, the robust technology is building a profound impact in the most beguiling ways, increasing the demand for AI professionals, blessed with the in-demand skills and expertise. No matter what, the future of AI seems to be all bright and beautiful.

This is why we are here to help you crack major AI job interview questions and guide your career through this fascinating field of science and technology. Go through the following questionnaire and showcase your knowledge, skill and talent. This will highlight how well you know the various nuances of AI and its implications.

What is Artificial Intelligence?

AI is the budding field of computer science and IT – which stresses on creating intelligent machines that imitate human brain’s cognitive abilities. It’s the simulation of human intelligence processed by machines using computer systems. Some of the notable AI activities are:

  • Speech recognition
  • Learning and planning
  • Problem-solving

What are the fields where AI is used?

Since its inception, AI is used across fields of extreme diversity, and some of them are mentioned below:

  • For customer support, including chatbots, sentiment analysis bots and humanoid support robots
  • In the linguistic field of processing natural language
  • Across IT fields, like computer software, sales prediction and analysis

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Highlight the advantages of Fuzzy Logic Systems.

Following are the key advantages of Fuzzy Logic System:

  • Easy to understand
  • Simple constructible logics
  • Takes in inaccurate, ill-mannered and malformed input data
  • Flexibility to include and delete the rules as per convenience in the FLS

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What is FOPL?

FOPL is the short form of First-order Predicate Logic, which is a compilation of formal systems, where the statement is divided into two sections: a subject and a predicate. The predicate has the power to determine or modify a subject’s characteristics.

What do you mean by Greedy Best First Search Algorithm?

This is an incredible algorithm method, where the node nearest to the goal expands first. f(n) = h(n) is the default explanation of nodes, and this process is largely applied in the subsequent levels, where priority queue comes into question.

Do you know the artificial key in AI?

An artificial key in AI is built by assigning a number to an individual record, when a standalone key goes missing.

What is an alternate key in AI?

All the candidate keys except primary keys are called alternate keys.

Mention the components of Robotics.

These are the following components, which we would require to build a robot:

  • Actuators
  • Pneumatic Air Muscles
  • Sensors
  • Power Supply
  • Electric Motors
  • Muscle Wires
  • Ultrasonic and Piezo Motors

Hope these general job-interview questions have helped you grasp the underlying features of AI and its applications. For more research in this specific area of interest, we recommend artificial intelligence certification in Delhi NCR – DexLab Analytics is the go-to institute in this case.

 
The blog has been sourced from — www.janbasktraining.com/blog/artificial-intelligence-interview-questions
 

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DexLab Analytics Partnered With DU for Vishleshan’18

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DexLab Analytics Partnered With DU for Visheshan’18

DexLab Analytics in association with Department of Business Economics, Delhi University proudly presented Vishleshan’18, an analytics conclave to nurture budding talent pool. Each year, Delhi University organizes an annual competition, where in data enthusiasts get an opportunity to showcase their analytical capabilities and complex problem-solving skills. This year, DexLab Analytics shared the platform with the esteemed institutional body under DU – and we can’t feel more obliged!

Our sincere gratitude and good wishes rests with the Department of Business Economics, University of Delhi; they recognized our efforts towards the data analytics community and shared interest in collaborating with us, which was indeed an honorable moment for us.

Now, coming to the event details, Analytics Conclave – Vishleshan’18 was segregated into two rounds. The first round also known as the elimination round comprised of an online quiz session, candidates were required candidates to be well-versed in all verticals of analytics. The second round was a lot more challenging, because here selected teams were allotted a case study each. In this round, DexLab Analytics played a crucial role – the seasoned consultants actively participated in structuring these all-encompassing case studies.

The case studies were all in sync with this year’s theme ‘AI and Machine Learning: Transforming Decision Making’, which means bagging the winner title was no mean feat. Various teams, all from notable institutes and in accordance to eligibility criteria (only post-graduates or MBA students allowed) participated in the contest. Out of them, only 5 teams were finally selected to present their case studies in front of a distinguished panel of judges at the DU campus on 8th September 2018.

Artificial intelligence and machine learning are driving the technology realm. Not only are they the pioneers of effective decision-making processes but also engines of faster and cheaper predictions for all big and small companies. Next to the US, India is deemed to be biggest hub of artificial intelligence, thus it’s time for prestigious Indian educational institutes, like Delhi University to start training the bright young minds for the next big boom of AI and machine learning. And that’s exactly what they were found doing.

However, as it’s said, teamwork divides the task and multiplies the success – the organizers of Vishleshan’18 approached DexLab Analytics, a leading data analytics training institute in Gurgaon, Delhi NCR. Together, they believed they would better analyze the data acumen of the participants and foster a symbiotic association for more knowledge sharing in the future.

Perhaps, not surprisingly, DexLab Analytics has created a place of its own, in the niche analytics industry. Comprehensive in-demand skill training courses are crafted keeping in mind the students’ requirements and industry demands. Moreover, the consultants who bring in considerable domain experience in the related field are all experienced and loaded with expertise. Together with you, this institute can be considered as a center of excellence in the big data analytics domain!

 

For a more detailed report, click the link below:

www.prlog.org/12728482-dexlab-analytics-is-case-study-partner-for-analytics-conclave-vishleshan-18.html  

 

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How Machine Learning and AI is Influencing Logistics, Supply Chain & Transportation Management

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How Machine Learning and AI is Influencing Logistics, Supply Chain & Transportation Management

More than 65% of top transportation professionals agree that logistics and supply chain management is in the midst of a revolution – a period of incremental transformation. And, the most potent drivers of change are none other than machine learning and artificial intelligence.

Top notch companies are already found leveraging the tools of artificial intelligence and machine learning for fine-tuning its superior strategies, including warehouse location scouting and enhancing real-time decision-making. Though these advanced technologies nurture large chunks of data, the logistic industry has for long been hoarding piles of data. Today, the difference lies in the gargantuan volume of data, as well as the existence of powerful algorithms to inspect, evaluate and trigger the process of understanding and its respective action.

Below, we will understand how AI streamlines logistics and transportation functionalities, influencing profitability and client satisfaction. Day by day, more companies are fusing Artificial Intelligence with Internet of Things to administer logistics, inventory and suppliers, backed by a certain amount of precision and acumen. Let’s delve deeper!

Predictive Maintenance

AI-powered Sensors monitor operational conditions of machines; thus can detect discrepancies even before the scheduled machine servicing based on manufacturer’s recommendation. Then they alert the technicians prior to any potential equipment failure or service disorientation. Thanks to real-time wear and tear!

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Shipping Efficiency

Powerful algorithms are constantly used to tackle last minute developments, including picking the best alternate port in case the main port is non-operational or something like that, planning beforehand if the main carrier cancels a booking and even gauging times-of arrival.

Machine Learning capabilities are also put to use for estimating the influence of extreme weather conditions on shipping schedules. Location specific weather forecasts are integral to calculate potential delays in shipments.

Warehouse Management

Machine learning has the ability to determine inventory and dictate patterns. It ascertains the items which are selling and are to be restocked on a priority basis, and items which need sound remarketing strategy.

Voice recognition is a key tool that uses AI to ensure efficiency and accuracy through successful Warehouse Management System – a robotic voice coming out of a headset says which item to pick and from where, enabling a fast process of warehousing and dispatching of goods.

Once, the worker founds the item, he/she reads out the number labeled on them, which the system then tallies with its own processed data list through speech recognition and then confirms the picked item for the next step.  The more the system is put to use, the more trained it gets. Over time, the system learns the workers’ tone and speech patterns, resulting into better efficiency and faster work process.

Delivery

 A majority of shipping companies are competing with each other to have the most robust and efficient delivery service, because delivery is the final leg of a logistic journey. And it’s vitally important – predictive analytics is used to constantly maneuver driver routes, and plan and re-plan delivery schedules.

DHL invests on semi-autonomous vehicles that drive independently without human intervention carrying deliverables to people across urban communities. Another company, Starship Technologies, founded by the co-founders of Skype employs six-wheeled robots across London packed with hi-tech cameras and GPS. The robots are stuffed with cutting edge technology, but are controlled by humans so that they can take charge as and when required minimizing any negative outcomes.

Overall, artificial intelligence and machine learning has started augmenting human role for efficient logistics and transportation management. With all the recent developments in the technology sphere, it’s only a matter of time until AI becomes a necessary management part of supply chain.

Data Science Machine Learning Certification

And of course all this excites us to the core! If you are excited too, then please check out our brand new Machine Learning Using Python training courses. We combine theoretical knowledge merged with practical expertise to ensure students get nothing but the best!

The blog has been sourced from:

https://www.forbes.com/sites/insights-penske/2018/09/04/how-artificial-intelligence-and-machine-learning-are-revolutionizing-logistics-supply-chain-and-transportation/#eb663dd58f5d
https://aibusiness.com/streamline-supply-chain-ai
 


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Forecasting Earthquake Aftershocks with Artificial Intelligence

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Forecasting Earthquake Aftershocks with Artificial Intelligence

Recently, a study where a huge number of earthquakes were analyzed using machine-learning models, fared better at indicating the regions affected by aftershocks than traditional methods of analyzing the same.

This study puts forward new ways of analyzing how ground stress, which is caused by a massive seismic activity like earthquake, trigger aftershocks that follow. Researchers believe that this advancement in aftershock detection can open up fresh avenues for assessing seismic risks.

Phoebe DeVries, a seismologist at Harvard, believes this new research to be a demonstration of the immense opportunities that machine learning has in this field.

Contrary to the general idea that aftershocks aren’t as damaging as the main earthquake, they can actually be more devastating. As an example consider the 7.1 magnitude earthquake that shook Christchurch area in New Zealand in September 2010. It didn’t take lives but the 6.3 magnitude aftershock that occurred over 5 months later caused massive damage and took 185 lives.

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Standard Method

Currently, the problem lies not in predicting the magnitude of aftershocks; rather seismologists find it difficult to forecast the spots where the aftershocks will hit. The traditional method used for aftershock forecasting involves calculating changes in stress of nearby rocks that’s produced by the main earthquake and using these calculations to find out the likelihood of aftershocks striking a particular area. This stress-failure process is good for defining after-shock patterns, but sometimes it fails to generate correct results.

There’s a lot of data available on previous earthquakes. DeVries and her group has used this data and applied it in machine learning models to create better predictions.

Neural Networking

Data related to over 131,000 main and after tremors were analyzed by scientists. It included some of the most destructive earthquakes, like the 9.1 magnitude quake that shook Japan in 2011. Employing this massive data set, neural networks were trained and these modeled a grid of cells that surrounded every main tremor location at a distance of 5 kilometers. Neural networks were given the signal that an earthquake had occurred and also fed in data related to the changes in stress at the centre of each grid cell. Following this, the neural networks were asked to give the probability of each cell generating aftershocks.

After testing this method for 30,000 main shocks and aftershock events, it was concluded that the neural networks forecasted the after tremor locations more accurately as compared to the stress-failure method. The networks treated each cell as an individual problem instead of calculating the overall effect of stress on the rocks. Furthermore, the ML models also implied some physical changes that occur in the ground due to the main shock and other important parameters that researchers don’t normally consider in seismic studies. One of them is the stress changes that occur in certain materials, like metals.

To conclude, it can be said that this new study is a motivating step forward in the study of seismic activities. AI and ML are breaking new grounds in every field of study. Understandably, Artificial Intelligence courses are all the rage among students wanting to leap forward in their careers. If data, numbers and forecasts interest you then this artificial intelligence certification in Delhi NCR should definitely be considered.

 

Reference: https://www.nature.com/articles/d41586-018-06091-z

 

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