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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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Decoding the Equation of AI, Machine Learning and Python

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Decoding the Equation of AI, Machine Learning and Python

AI is an absolute delight. Not only is it considered one of the most advanced fields in the present computer science realm but also AI is a profit-spinning tool leveraged across diverse industry verticals.

In the past few years, Python also seems to be garnering enough fame and popularity. Ideal for web application development, process automation, web scripting, this wonder tool is a very potent programming language in the world. But, what makes it so special?

Owing to ease of scalability, learning and adaptability of Python, this advanced interpreted programming language is the fastest growing global language. Plus, its ever-evolving libraries aid it in becoming a popular choice for projects, like mobile app, data science, web app, IoT, AI and many others.

Python, Machine Learning, AI: Their Equation

Be it startups, MNCs or government organizations, Python seem to be winning every sector. It provides a wide array of benefits without limiting itself to just one activity – its popularity lies in its ability to combine some of the most complex processes, including machine learning, artificial intelligence, data science and natural language processing.

Deep learning can be explained as a subset of a wider arena of machine learning. From the name itself you can fathom that deep learning is an advanced version of machine learning where intelligence is being harnessed by a machine generating an optimal or sub-optimal solution.

Combining Python and AI

Lesser Coding

AI is mostly about algorithms, while Python is perfect for developers who are into testing. In fact, it supports writing and execution of codes. Hence, when you fuse Python and AI, you drastically reduce the amount of coding, which is great in all respects.

Encompassing Libraries

Python is full of libraries, subject to the on-going project. For an instance, you can use Numpy if you are into scientific computation – for advanced computing, you have put your bet on SciPy – whereas, for machine learning, PyBrain is the best answer.

A Host of Resources

Entirely open source powered by a versatile community, Python provides incredible support to developers who want to learn fast and work faster. The huge community of web developers are active worldwide and willing to offer help at any stage of the development cycle.

Better Flexibility

Python is versatile. It can be used for a variety of purposes, right from OOPs approach to scripting. Also, it performs as a quintessential back-end and successfully links different data structures with one another.

Perfect for Today’s Millennial

Thanks to its flexibility and versatility, Python is widely popular amongst the millennials. You might be surprised to hear that it is fairly easier to find out Python developers than finding out Prolog or LISP programmers, especially in some countries. Encompassing libraries and great community support helps Python become the hottest programming language of the 21st century.

Data Science Machine Learning Certification

Some of the most popular Python libraries for AI are:

  • AIMA
  • pyDatalog
  • SimpleAI
  • EasyAI

Want to ace problem-solving skills and accomplish project goals, Machine Learning Using Python is a sure bet. With DexLab Analytics, a recognized Python Training Center in Gurgaon, you can easily learn the fundamentals and advance sections of Python programming language and score goals of success.

 

The blog has been sourced from ― www.information-age.com/ai-machine-learning-python-123477066

 


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AI in Cyber Security: Knowing the Difference between Machine Learning and Deep Learning

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AI in Cyber Security: Knowing the Difference between Machine Learning and Deep Learning

The need of the hour in business world is continuous innovation in the field of cyber security. Security vendors constantly brainstorm ideas and methods that’ll keep them ahead of cybercriminals. The gravity of the problem can be understood from a report by Sophos which mentions that almost 50% of Australian businesses were affected by ransomware attacks in 2017.

To keep functioning amidst such threats, businesses require innovative technologies, and artificial intelligence is one such tool that has become vital for cyber security.

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Artificial Intelligence

AI is a trendy term now, thanks to blockbuster Bollywood movies made on AI!

AI is an all-embracing principle that includes a number of technologies─ machine learning and deep learning being important ones among them. Basically, artificial intelligence enables machines to learn on their own from experience, modify techniques when fed with new data sets and carry out tasks that are human-like. When the principles of AI are applied to cyber security, we call it predictive security. AI helps to identify and check if files contain malware, which is carried out with the help of machine learning as well as deep learning. Although these two branches use similar AI principles, the two fields are fundamentally very different.

Moving on, let’s explore their basic differences.

Machine Learning

Machine learning is an artificial system that learns from examples and generates knowledge from past experiences. ML technology doesn’t simply memorize examples; rather it picks up laws and patterns and applies it later where relevant.

Considering today’s advanced threat landscape, conventional approaches fail to offer strong protection to a system. Malware programs are sometimes designed to make slight changes and breach traditional systems. In such situations, machine learning can be a better security option as it can detect these unknown and modified malwares too.

An important advantage of machine learning is that it keeps evolving and improving as it is used more and fed with more data. Machine learning algorithms scrutinize file elements in order to comprehend the nature of attacks, which includes simple things like file size as well as complex things like part of codes.

Deep Learning

The benefits of employing machine learning techniques in cyber security are numerous. However, it has some drawbacks too, which can be overcome with deep learning. The main limitations of ML are its inability to handle many variables at once, requirement of huge computing powers and using up a lot of space. In deep learning, unstructured data is stored in neural networks and decisions are made using predictive reasoning, which is modeled on the workings of human brain. This structure has potential to manage numerous points of information without hampering speed of the system.

Deep learning can form better idea of the big picture because it doesn’t include programs designed to solve a particular problem, rather it includes mathematical models that learn over time. A model is developed such that it can explain well what it ‘’sees’’. For this, large amount of data is used, such as trends, malicious URLs and other modes of attacks.

Cyber attackers need to be correct in their methods only once in order to breach an enterprise. On top of that, security threats are becoming more innovative each day. Hence, technologies like deep learning and machine learning need to be the founding stones of modern security systems. Understandably, these skills are also very high in demand. Artificial Intelligence certification courses are hugely popular. If this subject interests you, then don’t delay in enrolling for deep learning courses in Delhi or machine learning courses in Gurgaon from leading institute DexLab Analytics.

 
Reference: www.cso.com.au/article/648861/artificial-intelligence-vs-machine-learning-vs-deep-learning-what-difference
 

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A Success Story: Evolution of India’s Startup Ecosystem in 2018

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A Success Story: Evolution of India’s Startup Ecosystem in 2018

India’s startup ecosystem is gaining accolades. Steering away from the conventional, India’s young generation is pursuing the virgin path of entrepreneurship by ditching lucrative job offers from MNCs and government undertakings – the entire industry is witnessing an explosion of cutting-edge startups addressing real problems, framing solutions and satisfying mass level.

Interestingly, 2018 has been the year of success for Indian startups or entrepreneurs venturing into the promising unknown. Why? In total, 8 Indian startups, namely Oyo, Zomato, Paytm Mall, Udaan, Swiggy, Freshworks, Policybazaar and Byju’s crossed the $1 billion net worth mark this year and joined the raft of most-revered 18 Indian unicorns.

Besides attracting investments from domestic venture capitalists, these startups are bathed in global investments – foreign investors pumped in vast amounts on our homegrown startups to capitalize their activities. Thanks to their generosity, India proudly ranks as the 3rd largest startup ecosystem in the world, next to the United Nations and United Kingdom with its 7, 700 tech startups.

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Nevertheless, our phenomenal startup ecosystem has some grey areas too, which are addressed below:

Startup Initiatives

No doubt, the Indian government is taking conscious efforts to support the startup culture in the country, and for that Prime Minister, Narendra Modi has initiated the Startup India Programme. It is a noble step towards ensuring continuous creation and smooth functioning of fresh startups in India with technology in tow.

Thanks to technology, startups growth seemed to be 50% more dynamic this year!

Fund Generation

As compared to struggling years of 2017 and before, 2018 has been the year of driving investments. India experienced a 108% growth in total funding process, a big jump from $2 billion to $4.2 billion. Though investments at later stages skyrocketed, a decline was witnessed in the early stages during funding companies.

“In terms of overall funding, it is a good story. However, we are seeing a continuous decline in seed stage funding of startup companies. If you fall at the seed stage, innovation is hit. It is the area, which needs protection,” shared NASSCOM president Debjani Ghosh, which remains a matter of concern.

Employment Opportunities

Of course, the new startups push job creation numbers. It enhances the employment opportunities. Of late, NASSCOM reported that the epic growth in startup ecosystem resulted in creation of more than 40000 new direct jobs, while indirect jobs soared manifold. Today, the total strength of Indian startup landscape stands at 1.7 Lakh.

In the wake of powerful female voices and gender-neutral campaigns, our domestic startup ecosystem witnessed how women employees called the shots. The numbers of women employees spiked to 14% from 10% and 11% in the last two years, consecutively.

Global Position

Globally, India ranks as the 3rd biggest startup ecosystem in the world, and Bengaluru is the kernel of tech revolution. A report mentioned India’s significance in recording the highest number of startup set ups after Silicon Valley and London across the globe.

Quite interestingly, 40% of startups are launched in Tier 2 and 3 cities, indicating a steady rise of startup culture outside prime cities like Mumbai, Bengaluru and Delhi NCR.

With technology and startup leading the show, it’s high time you expand your in-demand skills of machine learning and data analytics. How? Opt for a good Machine Learning Course in India. It’s a surefire way to learn the basics and hone already learnt skills. For more information on Machine Learning Using Python, drop by DexLab Analytics!

 
The blog has been sourced from ― www.entrepreneur.com/article/322409
 

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Facebook and Google Have Teamed Up to Expand the Horizons of Artificial Intelligence

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Facebook and Google Have Teamed Up to Expand the Horizons of Artificial Intelligence

Tech unicorns, Google and Facebook have joined hands to enhance AI experience, and take it to the next level.

Last week, the two companies revealed that quite a number of engineers are working to sync Facebook’s open source machine learning PyTorch framework with Google’s TPU, or dubbed Tensor Processing Units – the collaboration is one of its kind, and a first time where technology rivals are working on a joint project in technology.

“Today, we’re pleased to announce that engineers on Google’s TPU team are actively collaborating with core PyTorch developers to connect PyTorch to Cloud TPUs,” said Rajen Sheth, Google Cloud director of product management. “The long-term goal is to enable everyone to enjoy the simplicity and flexibility of PyTorch while benefiting from the performance, scalability, and cost-efficiency of Cloud TPUs.”

Joseph Spisak, Facebook product manager for AI added, “Engineers on Google’s Cloud TPU team are in active collaboration with our PyTorch team to enable support for PyTorch 1.0 models on this custom hardware.”

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2016 was the year when Google first introduced its TPU to the world at the Annual Developer Conference – that year itself the search engine giant pitched the technology to different companies and researchers to support their advanced machine-learning software projects. Since then, Google has been selling access to its TPUs through its cloud computing business instead of going the conventional way of selling chips personally to customers, like Nvidia.

Over the years, AI technology, like Deep Learning have been widening its scopes and capabilities in association with tech bigwigs like Facebook and Google that have been using the robust technology to develop software applications that automatically perform intricate tasks, such as recognizing images in photos.

Since more and more companies are exploring the budding ML domain for years now, they are able to build their own AI software frameworks, mostly the coding tools that are intended to develop customized machine-learning powered software easily and effectively. Also, these companies are heard to offer incredible AI frameworks for free in open source models – the reason behind such an initiative is to popularize them amongst the coders.

For the last couple of years, Google has been on a drive to develop its TPUs to get the best with TensorFlow. Moreover, the initiative of Google to work with Facebook’s PyTorch indicates its willingness to support more than just its own AI framework. “Data scientists and machine learning engineers have a wide variety of open source tools to choose from today when it comes to developing intelligent systems,” shared Blair Hanley Frank, Principal Analyst, Information Services Group. “This announcement is a critical step to help ensure more people have access to the best hardware and software capabilities to create AI models.”

Besides Facebook and Google, Amazon and Microsoft are also expanding their AI investment through its PyTorch software.

DexLab Analytics offers top of the line machine learning training course for data enthusiasts. Their cutting edge course module on machine learning certification is one of the best in the industry – go check out their offer now!

 
The blog has been sourced from — www.dexlabanalytics.com/blog/streaming-huge-amount-of-data-with-the-best-ever-algorithm
 

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Best Machine Learning Questions to Crack the Toughest Job Interview

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Best Machine Learning Questions to Crack the Toughest Job Interview

The robust growth of artificial intelligence has ignited a buzz of activities along the scientific community. Why not? AI has no many dimensions – including Machine Learning. Machine Learning is a dynamic field of IT– where, one gets access to data and learn from that data, resulting into massive breakthroughs in the field of marketing, fraud detection, healthcare, data security, etc.

Day by day, companies are recognizing the potentials of Machine Learning. This is why investment in this notable field is spiking up as much as the demand for skilled professionals. Machine Learning jobs are found topping the list of emerging jobs displayed on LinkedIn – the median salary of a ML professional is $106,225, which pretty much suffices for a well-paying career option.

Importantly, we’ve picked out 5 best interview questions about Machine Learning that’ll optimize your chances of getting hired. Known to all, though ML skill is in high demand, grabbing a job in this booming field of technology is no mean feat. Employers seek particular knowledge and expertise in this field to get you hired. Our 5 best interview questions will help you expand your knowledge base on ML and hone your skills ahead of time.

You can also check out our Machine Learning training course – it comprises of industry-standard course material, real life use cases and encompassing curriculum.

What is Machine Learning?

While you define the exact meaning of the term, make sure you convey your good grip over the nuanced concepts of machine learning, and its real life applications. Put simply, you must show the interviewers how well versed you are in AI and machine learning skills.

What is the difference between deductive and inductive Machine Learning?

Deductive ML begins with a conclusion, and then proceeds towards making deductions about that conclusion. Inductive ML starts from examples and ends with drawing conclusions.

How to choose an algorithm for a particular classification problem?

The answer here is subject to the degree of accuracy and the size of the training set. For a tiny training set, low variance/high bias classifier will work, and vice versa.

Name some methods of reducing dimensionality

Integrate features with feature engineering, eliminating collinear features, or use algorithmic dimensionality reduction – these procedures can definitely reduce dimensionality.

What makes classification and regression differ?

For definite answers, classification is far better a tool. It predicts class or group membership. On the other hand, regression entails prediction of a response.

What does a Kernel SVM mean?

Kernel SVM is the short form of Kernel Support Vector Machine. Kernel methods are basically a specific class of algorithms used for patter analysis and amongst them the most popular one is the Kernel SVM.

Data Science Machine Learning Certification

What do you mean by a recommendation system?

Recommendation system is a common feature for those who have worked on Spotify or shopped at Amazon. It’s an information filtering system that forecasts what a user wants to hear or see, structured on the choice patterns given by the user.

No second thoughts, these interview questions will set you on the right track to crack an interview – but, if you want to gain a deeper understanding on Machine Learning or AI, obtain Machine Learning training Gurgaon from the experts at DexLab Analytics.

 
The blog has been sourced from —

https://www.simplilearn.com/machine-learning-interview-questions-and-answers-article


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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!

For Machine Learning training course, drop by DexLab Analytics…

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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