Contact Us
×

Tag: Artificial Intelligence

Ships Of The Future -Will Run on AI Instead of A Crew?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Technology has taken a high route since Artificial Intelligence has gained immense impetus over the years. Alexa and Siri have become household names as millions of their users, start the day, and close the same with them.

Artificial Intelligence is also seen to be transforming a number of industries including the shipping industry.

This means that your cruise ships are about to you take you into the future. They will be driven by artificial intelligence instead of a crew member. In the year 2017, two friends Ugo Vollmar and clement Renault were all set to work on a self-driving car project until they stumbled upon an article that talked about autonomous shipping which made them sail in a different direction.

Human resources and autonomy 

Autonomy would operate in a different manner when it comes to water than it does for roads. In the case of waterways, it will not completely eliminate the human resources on board. This is because when it comes to cars, there is only one person that takes over the entire control to operate it while for ships, there is a bare minimum of at least 20 crew members on board, all of them being assigned crucial duties.

Thus, in the case of roads, that one person can be completely replaced by autonomy, but not all the crew members can be replaced by autonomy in its entirety.

“Diesel engines require replacement of filters in oil systems—the fuel system has a separator that can get clogged. There are a lot of these things the crew is doing all the time” quoted Oskar Levander, the head of Rolls Royce’s autonomous system efforts.

This is why it can be said that the helm is most likely to be operated with autonomy using a robot or remote control while a part of the crew can help in taking care of the vessel. In addition to this, these automated journeys will have special rules created by the International Maritime Organisation which is most likely to happen in the coming years.

Key examples

One of the examples of companies that have employed artificial intelligence in order to robotize ships is Shone. They visualize employing artificial intelligence by planting sensors like radar and cameras that can help simulate a number of hazards around the ship and to navigate amidst them. Autonomous shipping helps in cutting costs of consumer goods as well as provides a safer environment for passenger ferries and cruise liners. Tugboats and ferries are likely to operate autonomously for at least a part of the time, the ones that only operate for shorter distances and time duration.

Finland and Norway have staked out testing areas for pioneering the commercial applications of autonomous systems that are likely to happen on the small coastal waters of Scandinavia. Rolls Royce orchestrated the first-ever public demonstration of an autonomous voyage by a passenger’s vessel. It was a state-run vessel that happened to avoid obstacles for 1 mile and also docked automatically.

Rolls Royce also revealed that on the day of the demonstration and the trails before that, the vessel was able to perform well even in rough waters, handling snow and strong winds which indicates that we are moving towards a world that will have machines employed everywhere to augment our experiences and make life easier.

Transportation made easy

At ports like Scandinavia where small ferries play a crucial part in the transportation network, in order to carry cars across fjords and connecting them to islands, autonomous systems will have it made it a lot easier. This is because the remote-control systems could allow for an expansion of service at the routes that are not very long, especially during the late hours and help reduce staffing, thus cutting costs, increasing efficiency and saving time. You can save big bucks by employing autonomous systems as the crew costs are really high and you can eliminate a big part of the same with artificial intelligence.

In a nutshell, we can say that we are moving towards living in a world that will be much easy to live in. Machine learning Training and Artificial Intelligence are taking over various industries eliminating its glitches and making operations better and more efficient.

For more details in brief and further career counseling, you can also search for – Imarticus Learning and can drop your query by filling up a form from the website or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Hyderabad, Delhi and Gurgaon.

AI is Now Being Used in Beer Brewing!

Posted on by Imarticus Learning
Reading Time: 3 minutes

AI is now being used in beer brewing -from creating unique beer recipes to adapting recipes as per customer feedback. AI is doing it all…

With the advent of the digital revolution, Artificial Intelligence (AI) has gained immense impetus in recent years. Today, everyone is connected to everything because of the growing importance of the Internet of Things. Right from the time, you wake up until the time you close your day, technology plays a key role in taking you forward.

Alexa and Siri have now become household names and no doubt, why “Her” was a blockbuster in the cinemas. AI and Machine Learning are here to make your work easier, and your life smoother. It is also brilliant to know how even breweries today are using AI to enhance their beer production.

Brewed with AI
As discussed earlier, digitization and technology have significantly impacted our lives across spectrums, and there are several examples of various companies that have started employing AI in their processes to serve their customers better. Breweries are nowhere behind in this race of digitization, so let us discuss a few examples of how they are using AI in order to enhance the experience of the consumers.

Intelligent X
Intelligent X is one of the best examples of how a platform employed AI to enhance their beer. It came up with the world’s first beer, which is brewed with Artificial Intelligence Course and advances itself progressively based on customer feedback. They use AI algorithms and machine learning to augment the recipe and adjust it in accordance with the preferences of the customers. The brewery offers four types of beer for the customers to choose from:

  • Black AI
  • Golden AI
  • Pale AI
  • Amber AI

In order to brew the perfect beer that pleases all your senses, all you need to do is sign up with IntelligentX, train their algorithm according to what appeals to your palate, and you are good to go. In addition to this, you can follow the URL link on your beer can and give your feedback so that they can create a beer you would like. These beers come in classy and minimally designed black cans that reflect their origin and give a feeling that what you are experiencing is the beer from the future.

Champion Brewing
Another example of a very intelligent deployment of AI in brewing beer is that of Champion Brewing. They used machine learning in the process of developing the perfect IPA. They took the big step by initially getting information regarding the best and the worst IPA selling companies to get an insight into how to go about the entire project. Based on the same, did they determine the algorithm of brewing the best IPA with their AI?

RoboBEER
An Australian research team found out that the form of a freshly poured beer affects how people enjoy it. Building on to this, they created RoboBEER, which is a robot that can pour a beer with such precision that can produce consistent foam, pour after pour. These researchers also made a video of how the RoboBEER poured the beer tracked the beer color, consistency, bubble size, and all the other attributes. They then showed the same videos to everyone who participated in the research in order to get seek their feedback and thoughts with regard to the beer’s quality along with its clarity.
Conclusively, this shows how AI has become the nascent yet a very preferred trend, which is even being followed by the breweries around the world. It has added an unusual turn to the way the perfectly brewed well-crafted beer makes its way to your glass. With the help of this ever-evolving technology, we can anticipate our favorite drinks to be made precisely in accordance with our preference only with the help of your smartphone.

By deriving minutest of the insights right from the foam of the beer till the yeast used in the same, companies these days are striving to deliver their best with the help of immense research and execution from the ideation derived from their research amalgamating it with AI and Machine Learning. Looking at the various examples, we can surely say that we are living in the future in the present.

For more information you can also visit – Imarticus Learning contact us through the Live Chat Support or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi and Gurgaon.

How can AI be integrated into blockchain?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Blockchain technology has created waves in the world of IT and fintech. The technology has a number of uses and can be implemented into various fields. The introduction of Artificial Intelligence Training (AI) makes blockchain even more interesting, opening many more opportunities. Blockchain offers solutions for the exchange of value integrated data without the need for any intermediaries. AI, on the other hand, functions on algorithms to create data without any human involvement.
Integrating AI into blockchain may help a number of businesses and stakeholders. Read on to know more about probable situations where AI integrated blockchain can be useful.
Creating More Responsive Business Data Models
Data systems are currently not open, and sharing is a great issue without compromising privacy and security. Fraudulent data is also another issue which makes it difficult for people to share data. Ai based analytics and data mining models can be used for getting data from a number of key players. The use of the data, in turn, would be defined in the blockchain records, or ledger. This will help data owners maintain the credibility, as the whole record of the data will be recorded.
AI systems can then explore the different data sets and study the patterns and behaviors of the different stakeholders. This will help to bring out insights which may have been missed till now. This will help systems respond better to what the stakeholder wants, and guess what is best for a potentially difficult scenario.
Creating useful models to serve consumers
AI can effectively mine through a huge dataset and create newer scenarios and discover patterns based on data behavior. Blockchain helps to effectively remove bugs and fraudulent data sets. New classifiers and patterns created by AI can be verified on a decentralized blockchain infrastructure, and verify their authenticity. This can be used in any consumer-facing business, such as retail transactions. Data acquired from the customers through blockchain infrastructure can be used to create marketing automation through AI.
Engagement channels such as social media and specific ad campaigns can also be used to get important data-led information and fed into intelligent business systems. This will eventually help the business cycle, and eventually improve product sales. Consumers will get access to their desired products easily. This will eventually help the business in positive publicity and improve returns on investments (ROI).
Digital Intellectual Property Rights
AI enabled data has recently become extremely popular. The versatility of the different data models is a great case study. However, due to infringement of copyrights and privacy, these data sets are not easily accessible. Data models can be used to show different architectures that cannot be identified by the original creators.
This can be solved through the integration of blockchain into the data sets. It will help creators share the data without losing the exclusive rights and patents to the data. Cryptographic digital signatures can be integrated into a global registry to maintain the data. Analysis of the data can be used to understand important trends and behaviors and get powerful insights which can be monetized into different streams. All of this can happen without compromising the original data or the integrity of the creators of the data.

How do you balance Machine Learning theory and practice?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Machine learning is no longer a technology from the future. The technology giants like Google, Facebook, Netflix, etc. have been using machine learning to improve their user experience for a very long time. Now, the applications of machine learning are growing across the industries and this technology is driving businesses worth billions of dollars. Along with the applications,  the demand for professionals with expertise in ML has grown immensely in the past few years.
So, it is indeed a good time to learn machine learning for better career prospects. A machine learning course is the best practical way to start your learning process. However, often people get too much stuck to the theory and fall behind in the practical experience. Well, it is not the best way to learn anything. This article will help you balance learning machine learning theory and practice. Read on to find out more.
Theory vs Practice 
For practitioners of ML, the theory and practice are complementary aspects of their career. To become successful in this field, you will have to strike the balance between what you read and the problems in real life. So many people avoid building things because it is hard. Building involves constant tracing of bugs, endlessly traversing stack overflow, attempts to bring so many parts together and so many more work. Theory on the other hands is comparatively easy.
You can find all the concepts settled in place and we can just consume everything as to how we wish things will work. But if it doesn’t feel hard, you are not learning anything properly. It will be a lot easier for us to rip through journals and understand the concepts, but reading about the achievements of others will not make you any better in this field. You have to build what you read and fail so many times to get an understanding that cannot be achieved by reading alone.
Build what you read
It is the one simple thing you can do to strike a balance between theory and practice. Build a neural network. It may perform poorly, but you will learn how different it is from the journals. Attend a Kaggle competition and let your ranking stare at you even if it is low. Hack together a javascript application to run your ML algorithms in the back end only just to see it fail for unknown reasons.
Always do projects. Your machine learning certification program might have projects as part of their curriculum, but don’t be limited to those. Just remember that everything you make during the learning process does not have to work. Even the failures are great teachers in this process. They will provide you with the practical experience you will need to excel in the industry.
Practicing everything you read may make it harder for you, but once you learn to volley theory and practice back and forth, you will certainly get the results better than you were looking for. Only such a balanced approach towards ML will help you make an effect on the real world problems.

How Machine Learning is Reshaping Location-Based Services?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Today life is a lot different from what it used to be a decade ago. The use of smartphones and location-empowered services is commonplace today. Think about the driving maps, forecasts of local weather and how the products that flash on your screen are perhaps just what you were looking for.
Location-enabled GPS services, devices that use them and each time we interact and use them generates data that allows data analysts to learn about our user-preferences, opportunities for expansion of their products, competitor services and much more. And all this was made possible by intelligent use of AI and ML concepts.
Here are some scenarios where AI and ML are set to make our lives better through location-based services.

  • Smart real-time gaming options without geographical boundaries.
  • Automatic driver-less transport.
  • Use of futuristic smartphone-like cyborgs.
  • Executing perilous tasks like bomb-disposals, precision cutting, and welding, etc.
  • Thermostats and smart grids for energy distribution to mitigate damage to our environment.
  • Robots and elderly care improvements.
  • Healthcare and diagnosis of diseases like cancer, diabetes, and more.
  • Monitoring banking, credit card and financial frauds.
  • Personalized tools for the digital media experience.
  • Customized investment reports and advice.
  • Improved logistics and systems for distribution.
  • Smart homes.
  • Integration of face and voice integration, biometrics and security into smart apps.

So how can machine learning actually impact the geo-location empowered services?
Navigational ease:
Firstly, through navigation that is empowering, democratic, accurate and proactive. This does mean that those days of paper maps, searching for the nearest petrol station or location, being late at the office since the traffic pileups were huge and so many more small inconveniences will be a thing of the past. We will gracefully move to enhanced machine learning smartphones that use the past data and recognize patterns to inform us if the route we use to commute to office has traffic snarls and provide us with alternative routes, suggest the nearest restaurant at lunchtime, find our misplaced keys, help us locate old friends in the area etc all by using a voice command to the digital assistant like Alexa, Siri or Google.
ML can make planning your day, how and when to get to where you need to be, providing you driving and navigational routes and information, and pinging you on when to leave your location a breeze. No wonder then that most companies like Uber, Nokia, Tesla, Lyft and even smarter startups that are yet to shine are investing heavily on ML and its development for real-time, locational navigational aids, smart cars, driverless electric vehicles and more.
Better applications: 
Secondly, our apps are set to get smarter by the moment. At the moment most smartphones including Google, Apple, Nokia among many others are functioning as assistants and have replaced those to-do lists and calendar keeping for chores that include shopping, grocery pickups, and such.
Greater use of smart recommendatory technology:
And thirdly, mobile apps set smartphones apart and the more intelligent apps the better the phone experience gets.  The time is not far off when ML will be able to use your data to actually know your preferences and needs. Imagine your phone keeping very accurate track of your grocery lists, where you buy them, planning and scheduling your shopping trips, reminding you when your gas is low, providing you with the easiest time-saving route to commute to wherever you need to go and yes, keep dreaming and letting the manufacturer’s know your needs for the future apps. The smart apps of the future would use your voice commands to suggest hotels, holiday destinations, diners, and even help you in budgeting. That’s where the applications of the future are headed to.
In summation, ML has the potential to pair with location-using technologies to improve and get smarter by the day. The future appears to be one where this pairing will be gainfully used and pay huge dividends in making life more easily livable.
To do the best machine learning courses try Imarticus Learning. They have an excellent track record of being industrially relevant, have an assured placement program and use futuristic and modern practical learning enabled ways of teaching even complex subjects like AI, ML and many more. Go ahead and empower yourself with such a course if you believe in a bright locational enabled ML smart future.

How AI in The Energy Sector Can Help to Solve The Climate Crisis?

Posted on by Imarticus Learning
Reading Time: 3 minutes

How AI in the Energy Sector Can Help Solve the Climate Crisis
Have you not complained about the crisis that is looming large in our environment? The news reports of untimely floods, missing rain patterns, fires in forests, carbon emissions and smog affect each and every one of us. The Davos meeting of the World Economic Forum threw up some important measures that we need to take in enabling AI, ML and technology as a whole in symbiotically tackling the climate crisis of all times.
The main cause of the changes in climate is being attributed to emissions of carbon and greenhouse gases. And each and every person in tandem with AI, technology and the big industrial players have a bounden duty to support such measures and immediately move to reduce these emissions if we wish to halt such catastrophic climate changes. Noteworthy is the funding of nearly billion dollars in such ventures by Bill Gates and Facebook’s Mark Zuckerberg.
Here is the gist of the top suggestions. In all these measures one looks to technology and artificial intelligence to aid and achieve what we singularly cannot do. This is because the noteworthy improvements brought about by AI are
AI helps compile and process data:
We just are not doing enough to save our planet. The agreement between countries in Paris to be implementable means elimination of all energy sources of fossil-fuel. AI enabled with intelligent ML algorithms can go a long way in processing unthinkable volumes of data and providing us with the insight and forecasts to reverse the climatic changes, use of fossil fuels, reduction of carbon emissions, waste etc, and setting up environment-friendly green systems of operations.
AI can help reduce consumption of energy by ‘server farms’
The widespread use of digitalization has led to server farms meant to store data. According to the Project Manager, Ms. Sims Witherspoon at Deepmind the AI British subsidiary of Alphabet when speaking to DW said that they have developed a bot named Go-playing with algorithms that are “general purpose” in a bid to reduce the cooling energy of data centers of Google by a whopping 40%. This does amount to a path-breaking achievement when you consider that a total of 3 percent of the energy globally used is just used by the ‘server farms’ to maintain data!

Encouraging the big players to be guardians of the climate.

The industrial giants are using technology, AI and ML to reduce their footprints of carbon emissions. AI tools from Microsoft and Google are aiding maximized recovery of natural resources like oil, coal, etc. Though with no particular plans or place in the overall plan-of-action such measures do go a long way in preserving the environment through reduced emissions and set the trend into motion.

Using smartphone assistants to nudge for low-carbon climate-friendly changes.

The rampant use of smartphones and devices of AI makes this option possible and along with zero-click AI enabled purchases the virtual assistant bolstered through ML algorithms and tweaked infrastructure can be used to influence choices of low-carbon climatic and emission-reduction changes.

Social media can transform education and societal choices.

The biggest influencer of social change is the social media platforms like Instagram, Facebook, Twitter, etc these can be harnessed to publicize, educate and act on choices that help reduce such carbon emissions and use of resources.

The reuse mantra and future design.

Almost all designing is achieved through AI which can help us design right, have default zero-carbon designs, commit to the recycling of aluminium and steel, reward lower carbon footprints, grow and consume optimum foods and groceries and create green and clean smart cities.
Summing up the suggestions to be placed at the UN Global Summit for Good AI at Geneva, it is high time we realize that the future lies in data and its proper use through AI and empowering ML. We need new standards for use of the media and advertising digitally. All countries need to globally work to reduce the use of fossil fuels in automobiles and transportation. We must cut our emissions by half in less than a decade and this is possible through proper use of data, AI, ML, and digitization.
If you care enough to be a part of this pressing solution to environmental change, learn artificial Intelligence at Imarticus Learning, how AI has the potential to harness data and control the damage to our environment. Act today.
Reference:

How can you start programming machine learning and artificial intelligence?

Posted on by Imarticus Learning
Reading Time: 2 minutes

One of the biggest developments in the world of computing over the last few years has undoubtedly been artificial intelligence. The ability for a machine to automatically learn and apply methods to improve the quality of output is one of the most in-demand jobs in the world today. Companies are willing to pay big bucks to create systems that can understand their user and make predictive techniques based on their behavior.
These techniques and systems are already being employed by some of the biggest companies in the world. If you’re looking to start your machine learning course, here are a few basics you need to know:
R or Python:
Python and R are two of the most commonly used programming languages with algorithms in all fields dependent on them. While Python is used more in the field of machine learning, it is also easy to understand and learn. Organizations have already implemented it in places to develop applications on analytics. It makes it easier for users to implement any type of algorithms as well.
R is used to create better and more statistical processes. R is generally used to create and formulate statistical processes and companies dependent on data analytics tend to use R.
Statistics:
A basic understanding of Statistics is necessary to comprehend machine learning. While you might need to know what the algorithm does, knowing how the tools can be used for the end result is also necessary. With time, you’ll be able to implement your own algorithms as well and create inferential and descriptive statistical methods at some point.

Artificial Intelligence Course
What skills would you require?
If you’re looking to get a job in the field of artificial intelligence or machine learning, there are a few essential skills, including:

  1. Communication skills – An ability to communicate is crucial in addition to professionally having a good knowledge of spoken English
  2. Education – A good graduation degree or A.I. certificate is also required to begin a career in the field of education. This is needed to create your base in this particular field.
  3. Programming languages – A knowledge of programming languages – understanding of python string, variables, statement, operators, conditions, modules, and sense are super necessary.
  4. Machine learning techniques – Knowing all about Artificial Intelligence, especially Machine Learning as it is the most lucrative field. It has helped to create powerful websites, given realistic speech popularity and more
  5. TensorFlow – This is a software program that is periodical for the dataflow to be streamlined to execute different duties. It is generally used for gaining practice in the systems, including the relation to nerve networks.
  6. Deep Learning – Knowing about deep learning is necessary to use strategies that are rooted in the evaluation of learning statistics, in order to create a unique set of rules and processes.

Hence, with the time you will be able to understand everything about the field of machine learning and artificial intelligence. With Imarticus, you can get the machine learning certification and begin your journey right away!

What is the future of Artificial Intelligence?

Posted on by Imarticus Learning
Reading Time: 2 minutes

One of the biggest developments in the world of computer science has undoubtedly been Artificial Intelligence. The ability of your machine to learn and understand all about certain processes and then implement methods to improve the same is one of the most in-demand jobs today.

It becomes necessary to evaluate a company’s software and see how they can implement artificial intelligence methods.

There is so much that is possible while applying artificial intelligence in marketing. By 2020, more than 30% of the companies worldwide will use AI to help streamline their sales. This will help them increase efficiency and focus more on converting sales and rates.

Here are a few other places where AI will play a prominent role:

  1. Driverless vehicles:

Automated vehicles aren’t a dream anymore. The likes of Tesla have already started implementing driverless cars on the road. The U.S. Department of Transportation has gone ahead and released certain definitions and rules pertaining to the various levels of automation which can be implemented.

Uber was also acquired by Google in order to help scale their properties and capture the driverless market in time. AI could help save lives lost in accidents and potentially save close to 30,000 people in the United States every decade.

As it is a disruptive technology, it is expected to create some big changes. It can also automate many jobs which affect people. In the near future, it is expected to be used for opportunity more than threats.

       2. Process automation:
Robotic process automation refers to the use of machine learning to automate tasks dependent on rules. It will help individuals focus on certain crucial aspects of their work and leave the routine work to machines.

Automated projects will take up a bulk of the automation work in the world of machine learning and artificial intelligence. Companies are always looking to be cost-effective and automated machinery will help them achieve that goal over the long term.

     3. Sales and marketing:
Artificial Intelligence is also being employed in so many sales and marketing sectors. AI can be used as a useful tool to make repetitive tasks much easier. This includes tasks such as scheduling, paperwork and even timesheets to make it easier.

Marketing teams will also be able to weed out fake leads from genuine ones to make it easier to choose the right people to market to. They will be able to make the process simpler and allow everyone to get better at their daily tasks.

Overall, artificial intelligence is on the route to make the world a better and easier place to live and work in. It is a disruptive technology which will create dramatic changes. It can also be used to automate a multitude of jobs, especially in the production sector and make it easier for companies to become cost-effective.

Over the long run, where this will head to cannot be predicted but by the looks of it, it seems like a good place to be in. With Imarticus, you will be able to take up an artificial intelligence course that makes it simpler for you to succeed. In the battle between machine learning vs artificial intelligence, you are the real winner!

Deep Learning and its Application for Facial Recognition

Posted on by Imarticus Learning
Reading Time: 3 minutes

Deep Learning, ML and AI are all used to support facial recognition and used traditionally the Eigenvalues for vectors and spaces defining the features of the space projected by the face. In 2012 AlexNet tweaking and deep learning technologies like the DeepID, DeepFace, FaceNet, and VGGFace went beyond the human capacity to recognize faces by aligning, using feature extraction, detection, and recognition techniques. Thereby the use of verifying faces in a photograph under various lighting conditions, an aged face, with glasses or without facial hear was made possible by leveraging deep learning of face datasets and model representations.
The recognition software is biometric in nature and can accurately identify, authenticate and verify a face just by comparing the facial features and contours against very large databases.
It is widely used for: 

  • The enforcement of the law by the police and detection agencies.
  • In businesses for biometric logging in and out.
  • In banking to ensure KYC and restricted access to lockers.
  • In AR and VR applications for animated film making.

Authentication through facial recognition:
The most useful advantage of facial recognition is that facial contours do not change and can be captured from a distance. It never fails since faces cannot be replicated or imitated successfully. The technology itself is of a non-contact biometric type and has been successful in restricting entry, ensuring attendance, for crime prevention, law enforcement and as a security measure. The technology is also inexpensive and infallible when compared to other methods like fingerprinting, Retinal scans and such biometric methods which are contractual in nature needing the voluntary provision of data for further process.
Many devices need and work on authentication based on face photograph verification either taken from videos or still photos. Human beings are very good at this task and deep learning simulates the same process.
Deep Learning and ML use ConvNets for the analysis and identification processes. Such neural networks are highly intelligent, self-taught and have other applications sewn in like the NLP processor, video analyzer, recommender modules and such.
The four essential steps involved are:
1. Detection which involves detection and using a boundary box for the image face. It generally falls into two categories namely
Based on features and using hand-work filters based on knowledge of the domain.
Based on images and ML where neural networks work on extraction and location of the image.
2. Alignment tasks normalize the photometry, geometry and such parameters with the database since most photographs contain more than one face and need to be aligned. The alignment output depends on the following task categories.

  • Binary labels for class and probability.
  • Similarity parameters.
  • Category labels.

3. Extraction of facial features is used for the task of recognition. The tasks can be further classified as tasks for

  • Matching and finding the best results.
  • Similarity analysis for faces.
  • Feature transformation and generation of new similar face images.

4. Face Recognition itself consists of two main tasks to identify any given image. Namely,

  • Verification where features of the identified face are mapped to the given image.
  • Identification where a given image is mapped against the database.

ML has proved to be invaluable to Deep Learning solutions. The present-day technological advancements make facial recognition and such issues easy. One has to choose the algorithm and feed in the given face image or data. The built-in neural network and trained dlib models will then take care of analyzing the face, comparing it against its databases and giving us an accurate match of the face against it. Further, the face recognition software on Github is easy to use, has a great library and is a rapid install.
Conclusion:
Deep learning machine algorithms and neural networks can currently manipulate, detect and identify facial contours from very large databases very quickly and this ability is far beyond human capacities.
If you are interested in such specific applications you will need to do courses that are skill-oriented in ML, Neural networks, Deep Learning, handling databases and applications, AR, VR, and such futuristic technology. Most of these courses are offered by Imarticus Learning where learning is practically based and you are job-ready from day one. Who does not like able-mentorship from certified trainers, a widely accepted global certification and assured placements when looking to transition careers? Don’t wait too long. The route and opportunities are just right at the moment.

3 Ways in Which AI is Transforming Business Operations

Posted on by Imarticus Learning
Reading Time: 3 minutes

The business scenario today has evolved and kept pace with technological developments. And AI has been at the helm of the change experience impacting literally every area that affects growth and development. The changing economic, geopolitical and social environments are in a state of constant flux and need businesses to adapt very quickly to tide over the changes in organizational dynamics, critical business glitches like employee retention and hiring or landscape requirements like being scalable and Agile.
Artificial intelligence can help bridge over troubled waters in many areas where human intelligence and limitations fail. Let us explore some of these critical areas where AI has and still has the potential to improve the business scenario.
The successful customer and user experience:
The experience of the customer is what tells brands apart and this differentiator is best exploited through successfully harvesting of the data and changes brought about by AI. Research and use of Walker data suggest that large multinationals like Adobe, Intuit, and EMC have benefitted greatly by entwining the customer experience into their operational daily routines of marketing, sales, and operational routines. And AI makes it possible to offer those great user-experiences crafted from forecasts and gleanings of data on why the customer buys, when and for how much, how the competition fares and their latest parleys, or what the customer wants from you.
The arsenal of data forecasts and insights can personalize an individual’s experience to match his needs, budget, etc, through a more seamless integrated process that offers high satisfaction and customer loyalty. The results are most helpful in rapidly predicting markets, changing products, forecasting customer- behavior, and staying up to date with the latest offers of technology. Thus AI is the one tool that has immense potential in accumulating, understanding and changing the fortunes of business enterprises by forecasting touch-points, trends, brand preferences, pricing strategies and more.
Bettering the hiring process:
The acquisition of skilled talent is critical to all businesses. However, most processes like recruitments, interviews, talent hunting, employee-referrals, and assessments are subject to very many biases, nepotism, controls, and flaws.
For bettering the hiring process certain tasks are all important. Firstly, one has to cast the net wide. Secondly, the talents need to be matched to the job requirements and the process of pivoting in on the right candidate needs to be free of human errors and bias. Lastly, the holistic use of data using the latest developments needs to be deployed. Not surprisingly, AI aided assistants today can make short work of the recruitment process while ensuring a great supply database for recruitments and keeping in mind the specifics of talent growing into higher roles and reducing the pitfalls of employee migration and retention issues.
Retaining and engaging the employees:
Skill and talent lie at the core of the hiring process. With increased demand comes the problem of retention and employee engagement turning into a competitive minefield. Poor management practices, lack of growth on the job and employee engagement have turned into major contributors for lack of retention of employees as is evident from surveys conducted by SalesForce and Gallup.
AI has enabled cutting-edge technologies like analysis of employee sentiments, biometric trackers, and such AI-empowered techniques can aid in effective retention through timely motivation, employee empowerment, continued learning opportunities and ensuring deserving rewards, career growth, skill up-gradation and more. More engaged employees mean better retention, employee loyalty, and engagement.
Conclusion:
In parting, it is valid to note that AI helps the new operations in business which in turn can change the dynamics of a beyond satisfying customer-experience, growing engagement with employees, hiring and retention. People are assets to the company and the twist that AI and technology have brought in can easily transform companies through efficient dynamics, change and people management.
To learn all about futuristic technologies like adaptations of artificial intelligence, powering AI through effective Machine Learning, scouring the growing volumes of data through Deep Learning and beyond to futuristic technology like blockchains for fintech industries try the Imarticus Learning experience.
The Agile Scrum Tutorial are succinct with due emphasis on the practical applications of knowledge and concepts coupled with invaluable modules of self-development and soft-skill training. Besides, one gets the mentorship of certified and industry-drawn mentors and instructors. Go ahead and make the most of opportunities and jobs on offer in their placement program too. Why wait then?