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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.

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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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Exploring New Avenues of Alliance Between Microsoft and Artificial Intelligence

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Exploring-new-avenues-of-alliance-between-Microsoft-and-Artificial-Intelligence

Artificial Intelligence is perhaps the ‘trending’ term of the technological paradigm. Microsoft is quite a honcho in the arena of Artificial Intelligence. This statement is further enhanced by Andrew Shuman, the Corporate VP for Microsoft AI and research Group. At the Microsoft’s annual Build Developer’s conference, he regarded “If I think about the kind of AI revolution that’s going on, it’s very much created by new increase in data being available and cloud service being able to run millions of computations”.

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Pertaining to the situation, it is true that Microsoft certainly has all the essential data, in comparison to the other IT companies, which has won the company a premier position in the field of AI. The data includes 100 million Office 365 subscribers and, in OneDrive and certainly has the cloud based services.

Also read: Artificial Intelligence: What the Future Holds for India, Next to US

Now the next section would deal with the utilities of AI in certain sectors:

AI for Office uses – Hard to find a single soul unaware of Microsoft’s Office productivity. But on the downside, the users need to deal with certain upheavals, sometimes causing a lot of difficulties. This entire process, now teamed up with AI, ensures a butter smooth flow of the software.

  • Power Point– The Quick Starter takes the aid of AI to search for the right template based, sometimes, on a single word it is typed into one of the slides. However, behind the scenes, it is actually dependent on the vast well of the structured Bing data.

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  • The Designer Service is also used for image presentations, in the quest for the congruence of faces and even colors that can influence template design choices.
  • AI also enriches Power Point presentations as a cognitive vision system exploring pictures and auto-generating the ALT-Text for them.

  • The Focused Inbox Option in Outlook is mainly supported by the cloud- based machine learning, where the system enriches itself through explicit and implicit The recent past has seen this software gaining much eminence in the Android and IOS versions of outlook.

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  • The last utility is the End –user control, a common theme found across all of Microsoft’s AI It refers to the tools to be personalized to the users. This ensures that the changes often rejected in the Word would be no longer flagged in the writing.

The Cortana Complication – In one word, Cortana is the public face for Microsft’s AI work. The voice assistant installed in million of desktop to be an aid to the users, it is mainly regarded as a hazard with the majority of the Windows users opting for the text box , next to the start button, to type in their queries. Even after this, Microsoft is still being enthusiast to project Cortana as the face and of AI efforts. Microsoft is actually working hard to set it as a household name on the AI front, with its latest discovery of Cortana Speakers, coming to the fore front sometimes next week. On being asked, if Cortana is the main obstacle then why Microsoft doesn’t restore all their efforts for the effective building of the software, Shuman answered, “I think we need to be careful about where we make it Cortana and where we don’t. To me it implies a full set of capabilities instead of little nuggets.”

Also read: Learn to Surf on the Three Waves of Artificial Intelligence

Thus to conclude, whether it is with or without Cortana, Microsoft remains the leading brand name in the AI sector. This again has been explained by Shuman as “We are without a doubt infusing intelligence and understanding in all of our products in ways that’s very shared and shareable,”

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So, that was all about the utilities of AI. Feel free to share the latest information. Also, enroll for the Artificial Intelligence Certification Courses only at www.www.dexlabanalytics.com

 

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Learn to Surf on the Three Waves of Artificial Intelligence

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Learn to Surf on the Three Waves of Artificial Intelligence

How is the future going to be like? Will human workforce be completely redundant? Will machine learning supersede human intelligence? There have been myriad forecasts about the illuminating future of the AI: that it will be capable of analyzing human emotions, evaluating social nuances, heading medical treatments and surgeries, all that shrouds the best of human resources. But what about now? What is the present scenario like? Fortunately, DARPA is here to unleash a stream of answers to various questions, asked as well as unasked.

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DARPA is one of the most intriguing agencies in the US. The stalwarts at DARPA succour outrageous projects – concepts that are completely absurd and far from the accepted paradigms. GPS, legged robots, self-assembling work tools, prediction markets and early internet are some of its incredible creations. And now, they are putting their rear into gear to focus on AI.

DARPA segregates between three distinguishing waves of AI; each is boisterous with its own abilities and challenges. Out of the three, if you ask me which one is more galvanizing, I will point my finger to the third one. However, to decode the third wave of AI, which is the most exhilarating out of the three, you need to understand the first two.

First Wave of AI: Logical Rules and Bespoke Knowledge

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Here, tech specialists formulated algorithms and software programs based on the knowledge and information they imbibed, and tried to feed them with logical commands that were decrypted throughout the years. Most of the software that we are using today, like Windows Operating System, traffic system and even our smartphone apps emanated from first wave AI.

Clear and logical rules formed the crux of first wave AI structure. They were successful in introducing simple logical rules for intricate problems, but what they lacked was incapability of learning and ways to deal with the uncertainties.

Second wave of AI: Statistical Learning SystemsGENBlueCode

In 2004, DARPA initiated its first Grand Challenge – where fifteen autonomous vehicles took part in the competition and they had to complete a 150-mile track in the Mojave Desert. The vehicles were formulated on first wave AI and immediately it revealed the limitations of AI.

Not a single vehicle could finish the entire course; it was an absolute failure.

DARPA learnt its lessons, well. Just one year later, DARPA again organized Grand Challenge 2005 and this time, five groups completed the entire track. But, how? A year ago, they couldn’t.

The groups that could complete the race used the second wave of AI: statistical learning. In this wave, the engineers ignored the exact rules of first wave; instead, they focused on developing statistical models, which they trained eventually on numerous samples to make them highly efficient and accurate.

For better understanding and higher adaptability, statistical learning systems are fetching. If they are properly trained, they can work and adapt themselves to different situations. The brainchild of second wave AI is the notion of artificial neural networks. Besides, the second wave AI is dwarfing humans at speech transcription, face recognition, identifying objects and animals in images, controlling autonomous cars and aerial drones. However, the success of these complex programs leaves the AI pundits clueless. Nobody knows how these systems are working so well. But, we are not complaining!

Third Wave of AI: Redefining First Wave Logical Rules

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In the last and final wave, AI system will take the charge of constructing models, themselves. Precisely, they will now redefine the logical rules, which will sculpt the entire decision-making process. The third wave is efficient in relying on several different kinds of statistical models to draw a bigger picture of the world. They are capable of training themselves better.

They are able to derive information from various kinds of sources in order to come at a nuanced and logical conclusion. The systems are so effective that they can actually extract data from our smart homes, cars, cities, even from our wearable devices and deduce our health status. Moreover, they will also be able to program themselves and help in developing abstract thinking.

However, the only challenge that is up on the front is, “there’s a whole lot of work to be done to be able to build these systems”, as told by the director of DARPA’s Information Innovation Office.

The implementation of the third wave is surely going to be a major step for the entire mankind. However, all of this will take some time. Probably it will eventually taste success in the next twenty years. But, when we will appraise the potentials of the AI systems, the concept of the third wave won’t sound improbable.

At the end of the discussion, I am pondering would there be any fourth wave of AI and if yes, then what it would be like? These questions should however be left on time to analyse and then to research. Till then, may you be focused on the third wave.

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