10 Business Functions That Are Ready to Use Artificial Intelligence

In the grand scheme of things, artificial intelligence (AI) is still in the very early stages of adoption by most organizations. However, most leaders are quite excited to implement AI into the company’s business functions to start realizing its extraordinary benefits. While we have no way of knowing all the ways artificial intelligence and machine learning will ultimately impact business functions, here are 10 business functions that are ready to use artificial intelligence. 

1. Marketing: 

If your company isn’t using artificial intelligence in marketing, it’s already behind. Not only can AI help to develop marketing strategies, but it’s also instrumental in executing them. Already AI sorts customers according to interest or demographic, can target ads to them based on browsing history, powers recommendation engines, and is a critical tool to give customers what they want exactly when they want it. Another way AI is used in marketing is through chatbots. These bots can help solve problems, suggest products or services, and support sales. Artificial intelligence also supports marketers by analyzing data on consumer behavior faster and more accurately than humans. These insights can help businesses make adjustments to marketing campaigns to make them more effective or plan better for the future.

2. Sales:

There is definitely a side of selling products and services that is uniquely human, but artificial intelligence can arm sales professionals with insights that can improve the sales function. AI helps improve sales forecasting, predict customer needs, and improve communication. And intelligent machines can help sales professionals manage their time and identify who they need to follow-up with and when as well as what customers might be ready to convert.

3. Research and Development (R&D):

What about artificial intelligence as a tool of innovation? It can help us build a deeper understanding in nearly any industry, including healthcare and pharmaceuticals, financial, automotive, and more, while collecting and analyzing tremendous amounts of information efficiently and accurately. This and machine learning can help us research problems and develop solutions that we’ve never thought of before. AI can automate many tasks, but it will also open the door to novel discoveries, ways of improving products and services as well as accomplishing tasks. Artificial intelligence helps R&D activities be more strategic and effective.

4. IT Operations:

Also called AIOps, AI for IT operations is often the first experience many organizations have with implementing artificial intelligence internally. Gartner defines the term AIOps as the “application of machine learning and data science to IT operations problems.” AI is commonly used for IT system log file error analysis, with IT systems management functions as well as to automate many routine processes. It can help identify issues so the IT team can proactively fix them before any IT systems go down. As the IT systems to support our businesses become more complex, AIOps helps the IT improve system performance and services.

5. Human Resources:

In a business function with “human” in the name, is there a place for machines? Yes! Artificial intelligence really has the potential to transform many human resources activities from recruitment to talent management. AI can certainly help improve efficiency and save money by automating repetitive tasks, but it can do much more. PepsiCo used a robot, Robot Vera, to phone and interview candidates for open sales positions. Talent is going to expect a personalized experience from their employer just as they have been accustomed to when shopping and for their entertainment. Machine learning and AI solutions can help provide that. In addition, AI can help human resources departments with data-based decision-making and make candidate screening and the recruitment process easier. Chatbots can also be used to answer many common questions about company policies and benefits.

6. Contact Centers:

The contact center of an organization is another business area where artificial intelligence is already in use. Organizations that use AI technology to enhance rather than replace humans with these tasks are the ones that are incorporating artificial intelligence in the right way. These centers collect a tremendous amount of data that can be used to learn more about customers, predict customer intent, and improve the “next best action” for the customer for better customer engagement. The unstructured data collected from contact centers can also be analyzed by machine learning to uncover customer trends and then improve products and services.

7. Building Maintenance:

Another way AI is already at work in businesses today is helping facilities managers optimize energy use and the comfort of occupants. Building automation, the use of artificial intelligence to help manage buildings and control lighting and heating/cooling systems, uses internet-of-things devices and sensors as well as computer vision to monitor buildings. Based upon the data that is collected, the AI system can adjust the building’s systems to accommodate for the number of occupants, time of day, and more. AI helps facilities managers improve energy efficiency of the building. An additional component of many of these systems is building security as well.

8. Manufacturing:

Heineken, along with many other companies, uses data analytics at every stage of the manufacturing process from the supply chain to tracking inventory on store shelves. Predictive intelligence can not only anticipate demand and ramp production up or down, but sensors on equipment can predict maintenance needs. AI helps flag areas of concern in the manufacturing process before costly issues erupt. Machine vision can also support the quality control process at manufacturing facilities.

9. Accounting and Finance:

Many organizations are finding the promise of cost reductions and more efficient operations the major appeal for artificial intelligence in the workplace, and according to Accenture Consulting, robotic process automation can produce amazing results in these areas for the accounting and finance industry and departments. Human finance professionals will be freed-up from repetitive tasks to be able to focus on higher-level activities while the use of AI in accounting will reduce errors. AI is also able to provide real-time status of financial matters to organizations because it can monitor communication through natural language processing.

10. Customer Experience:

Another way artificial intelligence technology and big data are used in business today is to improve the customer experience. Luxury fashion brand Burberry uses big data and AI to enhance sales and customer relationships. The company gathers shopper’s data through loyalty and reward programs that they then use to offer tailored recommendations whether customers are shopping online or in brick-and-mortar stores. Innovative uses of chatbots during industry events are another way to provide a stellar customer experience.

10 Fastest Growing Programming Languages

1. Go

 

Go, also known as Golang, is a programming language created at Google by Robert Griesemer, Rob Pike, and Ken Thompson. In 2009, Google released Go as an open source language, meaning it's free for anyone to use, download, or modify. Go was designed to be especially fast and easy to work with, especially for larger-scale systems (like Google's own).

2. Assembly

 

Assembly is a programming language that "speaks" as directly as possible to computers in their primary language: 0's and 1's. Developers use Assembly to write instructions for computers to access and process data at the lowest possible level. It can be tedious because each line of code must include even the simplest of instructions, but it can offers developers an unbeatable level of fine-tuning in their code.

3. Python

 

Python is both one of the fastest growing programming languages and the second-most popular one overall. It's open source, so anybody can download and get started with it, and it's high-level enough that it's easy for beginners to pick up. It's frequently used for tasks around web development, data science, and artificial intelligence.

 

"We often find that while Python isn't people's primary language, it was a lot of people's second language," Rachel Potvin, GitHub's vice president of engineering and data, said onstage at GitHub Universe on Wednesday. "There's something else behind the uptake in Python which is this explosion of the work in data science and machine learning."

4. Apex

 

Apex was first developed by Salesforce as a language for customizing and building its software, making it easier for developers to write code that automates tasks like updating customer records or running custom reports. It's designed to work well with large amounts od data, and shares some similarities with Java, one of the most popular programming languages.

5. PowerShell

 

PowerShell was developed by Microsoft, and built on its popular and prominent .NET. This open source language helps developers write instructions directly to their computer systems and manage their operating systems — especially useful for IT departments that need to automate tasks like onboarding new users or installing software updates across an entire network. It's now available on Linux, Apple's MacOS, and Microsoft's own Windows.

6. TypeScript

 

TypeScript is both one of the fastest-growing programming languages and one of the most popular. Developed by Microsoft, it's similar to JavaScript, and developers can mix and match both languages. However, compared to JavaScript, it has additional features that help developers create larger-scale software. It's also supported out of the box by Microsoft's free code editor Visual Studio Code, which is the top open source project on GitHub.

7. Kotlin

 

Kotlin, an open source language, is similar to the popular programming language Java (and programmers can mix and match between the two languages), but it has special features that help guard against bugs. It's also much more concise, helping developers do more with fewer lines of code. It's frequently used for building Android apps. Last year, Kotlin was the fastest growing programming language, and it's used by companies like Google, Square, and Atlassian. It's still growing fast, as it grew by 182% in the past year.

8. HCL

 

HCL, which stands for HashiCorp Configuration Language, was developed by developer startup HashiCorp, which creates tools that help programmers run and secure software running in the cloud. It's used to help set up HashiCorp's cloud tools, like Terraform, and it's designed to be easy for people to read. It grew 213% in the past year.

9. Rust

 

Rust was designed to be fast and more efficient with memory, with features that prevent developers from making common errors and introducing bugs. This open source programming language, which is sponsored by Mozilla of Firefox fame, is used in game engines, operating systems, virtual reality, and other systems-intensive tasks. Rust grew 235% in the past year.

10. Dart

 

Dart, which was developed by Google, was made specifically for designing user interfaces (UI), or how an app looks and feels to users. It's similar to JavaScript, the most popular programming language. It's also used with Google's Flutter, a UI toolkit for building mobile and web apps. Dart grew 532% in the past year.

Best Way to Learn Java for a Beginner

Here you will know what is the best way to learn java for a beginner.

Programming is the new vogue and a vogue which can make you earn wonders and at the same time make you utilize the capabilities of your brain to the fullest. Learning new programming languages however can sometimes turn out to be challenging especially when the language is one which holds immense importance in various fields. Java is definitely one of those languages which most of the time can haunt the programmer during the initial phase. If you also feel that you belong to this domain, don’t worry, nothing is impossible and Java is definitely not. Here are the best possible ways through which you can easily master the art of programming in Java.

1. Make Your Weapons Ready

It is very necessary to get yourself geared up before you actually move towards learning the language. Give your laptop or computer the perfect IDE (Eclipse preferably) for the language. Also, install the JDK on your system and most importantly, make sure they are compatible to your system and are running on your machine.

2. Get the Right Resources to Learn

Before you begin, the most crucial step is the source from which you can start learning the lessons for java. For initial phase, the video lectures and practice assignments by Udacity or Udemy can be a great help. Once you start knowing the concepts and understanding things, you can move on to the book Java – The complete reference by Oracle. Going through this book is the master step one can take towards learning java. This book can also be downloaded and it contains all the concepts and relevant data you would need to entitle yourself as a “master in java”.

Also, if in any case you find any difficulty in going through the above mentioned data and prefer to choose some other methods of learning, be sure to verify the authenticity of your sources. Because wrong sources can mislead and will only turn matters worst for you.

3. Practical is Better

No programming can ever turn into a success until you actually start implementing and visualizing things. Till now, you definitely have your IDE installed. So, even if you learn a basic program of addition or like concept, make sure you practically implement it and get the results.

This is a healthy practice and it will keep you prepared for every challenging concept you will need to implement in the near future. Once you will start recognizing and correcting your errors, java won’t be much difficult for you. However, if you ignore the need of practically implement the codes from the very beginning itself; going through things later might turn difficult.

4. Concept is the Key

Java is an Object Oriented Language, the concepts this approach has gifted to the language are difficult if not understood properly and this difficulty will definitely aggregate into a larger one. It’s very necessary to understand every concept along with its practical worth. For instance if you learnt the concept of inheritance, make sure you actually know that this concept is used while deriving the subclass from the super class. Not only this, every concept you learn must be etched into your mind along with its practical implementation. Java has an ocean of concepts understanding each of which is a vital and foremost step towards learning the language. Unfortunately, most of the people don’t understand the importance of this point and trust me, they are the only ones who find it difficult to understand the language.

5. Train Your Brain

Things become drastically easy once your brain knows how to tackle them. When you start learning the language, make sure you gather enough intentions for the same. You cannot complete a language without letting your brain be aware about the same. So, when you are learning java, try to make it a “java-affair” – just between you and the language. Only then can every possibility be generalized and worked upon. Once your brain knows you need to do, it will do it for you. Trust me; this will make your job easier. It’s possible that you might also start having outputs and solutions on your fingertips.

6. Mark out the Weak Points

Marking out the weakness can strengthen your capabilities. When you start practicing things, you will come to know the spheres you are weak at. Mark them and make it a point to work on them consistently. Be it a small or a big concept or point, working on the weak ones will radically improve your knowledge on the language.

7. Dry Run

Not only for Java, but for every other language, dry run can work wonders. Before implementing the code on the system, try to write it on a paper and run it through you power. If you are getting the wrong output on the paper itself, you cannot expect your code to run successfully on the system.

Dry run of the code can also save the programmer from many logical errors also.

8. Relate

The most important point is that you start relating concepts and methods used in the language with the counterpart of the languages you have probably learnt previously. Many times, some of the basic concepts and constructs are exactly same or are just a bit variant. Relating the newly learnt concept with the previously learnt will reduce your workload easily and will also make learning much easier than thought it to be.

9. Never Feel Disappointed

Java, if learnt and understood is easy but it’s definitely not a child’s game. If you have just started struggling with the language, you will need to struggle at many other points when you don’t get the correct outcome or huge list erroneous messages are encountered. All you need to do is: “Keep the game going!”

Practice makes a man perfect and disappointments will definitely lead to failures. So, try as much as you can. Take a friend’s help or reach out to your mentor. Search about the problem online, there are many programming forums like stackoverflow where you may find solution. But, never lose the struggle. Make every possible attempt to beat the best out of your high spirits.

Java is a gift to the programming world! Learning such a diverse language and working on its various aspects is tempting given the advantages it offers, but most of the programmers find it difficult to learn java just because they are completely unaware of the simplicity with which it can be perceived easily.

In this article, attempts have been made to display the best way to learn java. I hope it will help you in the learning journey.

Programming languages for Machine Learning

The increasing demand for experts in machine learning during last few years has increased curiosity to know the programming languages which one can use in machine learning. But before discussing the best programming languages for machine learning you must have brief information about the concept of machine learning.

What is machine learning?

In the field of computer science, machine learning is a part of artificial intelligence that provides your computer an ability to learn to improve its performance with data, without being programmed exclusively. In today’s technical world machine learning is one of the fastest progressing concepts on which most of the tech giant companies are heavily investing to improve their merchandise.

Best programming languages for machine learning

In order to understand and work on machine learning, you will have to learn few programming languages. Some of the best programming languages used for machine learning are briefly discussed hereunder for your guidance.

Python

Python has become a popular programming langue because it can be used flexibly for various purposes. For machine learning, it contains specific libraries like numpy and scipy which enable your computer to learn linear algebra and kernel methods for machine learning. While working on algorithms of machine learning then this language is greatly used as it has relatively easy syntax. It is the best programming language for beginners.

R Programming

This programming language was developed in Bell Labs as a modern version of S language. In order to provide flexibility in producing statistical models, R language is combined with lexical scoping. Several GNU packages available in R language make it a really powerful language for machine learning.

One can use R language to create useful algorithms and easy statistical visualization of those algorithms by using R studio. The industry has recently recognized the importance of R language even if it is popular with academic researchers since long.

Lisp

Lisp is the best language for the programmers who want to learn about the history and facts about the craft and practices used in programming just for the sake of fun. Some consider it as a miraculous tool for programming whereas some are of the opinion that no language has ever been invented for machine learning like this.

In fact, artificial intelligence was also used before the invention of programming languages like Scala, Python and Haskell etc. lisp is mostly loved by hackers. It is used more by compilers than interpreters as supports efficient and fast coding. It collects garbage as it includes automatic memory manager.

Prolog

It is one of the oldest languages that were developed for programming Artificial Intelligence. It is another primary language like Lisp that is used for artificial intelligence of the computer. The developers working on flexible frameworks enjoy working with the mechanism of this language. This directive and rule-based language contain rules and facts to dictate the coding language of artificial intelligence. It supports some of the basic mechanisms like tree-based data structuring, automatic backtracking and pattern matching etc, essentially required for programming artificial intelligence.

While representing tree-based data structures this programming language has an inbuilt list to handle essential things. It can be used efficiently for prototyping artificial intelligence programs as fast as it releases modules frequently. It allows running the program simultaneously while creating a database.

JavaScript

Most of the web developers, new and seasoned, today use JavaScript. For a long time, JavaScript used to be limited to the only web development but now it’s everywhere. Tensorflow, Google’s framework for machine learning is built on the top of JavaScript. JavaScript falls into the category of Python and Java for its wide applications.

How to Give powerful presentations in office

Giving a presentation doesn’t mean stand on a platform and give a boring lecture. The key that comes to presenter is to stay calm and be in control. If you actually want to be successful then you must learn the art of giving powerful and memorable presentation. It is not that much difficult that you might be thinking. Follow few tips and you will be good at it. Here we go.

1. Know your words

Delivering presentation means you have to deliver your words in the right way. So, think about how your words will sound as no one will see your slides. Speaking is more finite so your speech must end with sounding monotonous.

2. Don’t just listen

If ever anytime don’t understand or ask you any question in between, don’t make any mood. Just smile as if you are damn interested in their respective question even if you don’t know their solution. Don’t just listen, understand what they are saying or want to know about.

3. Use examples for support

Use some real-life data or any example so that they take interest and find your presentation actually worthy. Also let them know why they should believe it.

4. Involve your audience

It is just a way to make presentation more interactive. Don’t tell listeners what they want to know. Ask them too or grab a volunteer to answer a question.

5. Convey with humor

No one likes boring lectures, so make sure your presentation doesn’t sound boring and you like book-worm. Make it funny as people take more interest in funny delivering speech. But remember don’t plan ahead too much. If you would plan too much your audience will find it stiff and that all would go wrong. Your humor must go with the flow and according to your work.

6. Keep it simple

Keep your presentation simple and understandable to all the people. Don’t behave as if they are aware of each and every little details or things.

7. Stay Focused on your keys

Make your objective and don’t go in different direction. Stick to your objective. You can write everything on paper just to be in sequence and consider that order while delivering your speech.

8. Repeat impressive Keywords

Make your speech powerful by repeating good keywords. And these points will help audience to understand as well as if you go wrong then surely your good keys will be treated as backup.

9. Remember your content

Make sure what you have written in your slides, you have complete knowledge of that words or even phrases. Remember all your key points.

10. Take some Break

Always allows break for natural responses and even laughter. Never run in any presentation, it will give negative impact on your performance.

11. Make it about the audience

Deliver your presentation as if you are talking about audience, that is you. Make sentence very clear like how would you feel if this goes from right to left? You just need to replace me, myself and I with you.

12. Practice

Make sure you must leave a good impact on your audience, so first audience is you. Try practicing on voice recorder or in the mirror. Learn from your mistakes and it will help you to gain confidence.

13. Facial expression

Deliver your speech with good facial expression. Don’t show as if you are stresses or depresses or in hurry to complete the topic. Put a smile and stay calm.

14. Pave your introduction

Write your intro and provide it as host to your audience. Don’t just start with topic itself.

15. Scope out the venue

Get familiar with the room where you are about to give presentation. So, visit to the location early.

16. Open with a bang

Well, you won’t get hours, the time would be limited or I should say merely seconds. So, make your body language full with energy and gets your audience interaction immediately.

17. No need to apology

If something goes wrong, like any technical issue or anything like this. Don’t stop, just go with the flow and behave as if nothing happened. Don’t allows anything to make interrupt.

18. Keep it short

Don’t try to extend until it is necessary as you know people gets bored so fast. Keep it up to point and try to make your audience happy.

19. Pick the right time

Time also matters, time at which you will give your presentation. If possible, try avoiding presentation before or after lunch as at this audience will be felling lazy and it might possible your presentation would go in vain.

20. Emotional connection

Just like others, don’t just focus on delivering presentation, try to make a connection with audience by asking question or asking for feedback and let them know you are quite interesting in your thoughts and ideas.

21. Use visuals

If you need visuals to prove your point, don’t hesitate just add them to your slides and if possible, try to explain them with visuals only.

22. Quote someone

Quoting someone is great way to start any presentation but make sure whatever quote you are presenting make sense with your objective and purpose of your presentation.

23. Use words, image or think of it

Words like Imagine, think of a situation or scenario or close your eyes make sense and actually forces audience to image or think of something. This is another way to make a quick interaction with audience.

24. Make Eye to eye contact with audience

Don’t get nervous of someone ask you any question and it is quite obvious to get nervous in large audience but try to avoid so. Develop confidence enough to deal with large audience. Speak to audience directly and make eye contact with them, no matter if anyone laughs or not listen to lecture.

25. End your presentation nicely

When it come to the end of presentation, tell them Thank You or make any joke just to stay connected with them. Or you can ask for feedback that would be great.

Difference between AI, Machine Learning and Deep Learning

As we reached the digital era, where computers became an integral part of the everyday lifestyle, people cannot help but be amazed at how far we have come since the time immemorial. The creation of the computers, as well as the internet, had led us into a more complex thinking, making information available to us with just a click. You just type in the words and information will be readily available for you.

However, as we approached this era, a lot of inventions and terms became confusing. Have you heard about Artificial intelligence? How about Deep Learning? Moreover, Machine Learning? These three words are familiar to us and can be used interchangeably, however, the exact meaning of this becomes uncertain. The more people used it, the more confusing it gets.

Difference between AI, Machine Learning, and Deep Learning

Deep Learning and Machine Learning are words that followed after Artificial Intelligence was created. It is like breaking down the function of AI and naming them Deep Learning and Machine Learning. But before this gets more confusing, let us differentiate the three starting off with Artificial Intelligence.

1. Artificial Intelligence

AI is the like creating intelligence artificially. Artificial Intelligence is the broad umbrella term for attempting to make computers think the way humans think, be able to simulate the kinds of things that humans do and ultimately to solve problems in a better and faster way than we do. The AI itself is a rather generic term for solving tasks that are easy for humans, but hard for computers. It includes all kinds of tasks, such as doing creative work, planning, moving around, speaking, recognizing objects and sounds, performing social or business transactions and a lot more.

Digital era, brought an explosion of data in all forms and from every region of the world. This data, known simply as Big Data, is drawn from sources like social media, internet search engines, e-commerce platforms, online cinemas, etc. This enormous amount of data is readily accessible and can be shared through various applications like cloud computing. However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to Artificial Intelligence (AI) systems for automated support.

Machine Learning

More and more plans to try different approaches to use AI leads to the most promising and relevant area which is the Machine Learning. The most common way to process the Big Data is called Machine Learning. It is a self-adaptive algorithm that gets better and better analysis and patterns with experience or with newly added data.

For example, if a digital payments company wanted to detect the occurrence of or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set, and point out any anomaly detected by the pattern. 

Deep Learning

Deep learning, on the other hand, is a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The Artificial Neural Networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach.

A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensues, while a deep learning non-linear technique to weeding out a fraudulent transaction would include time, geographic location, IP address, type of retailer, and any other feature that is likely to make up a fraudulent activity.

Conclusion

Thus, these three are like a triangle where the AI to be the top that leads to the creation of Machine Learning with a subset of Deep Learning. These three had made our life easier as time goes by and helped make a faster and better way of gathering information that cannot be done by humans because of the enormous amount of information available.

Humans will take forever just to get a single information while these AI will only take minutes. As we become more and more comfortable using technology, the better humans can develop them into a better version of itself.

6 best programming languages for AI development

Which programming language should you pick for your machine learning or deep learning project? These are your best options:

AI (artificial intelligence) opens up a world of possibilities for application developers. By taking advantage of machine learning or deep learning, you could produce far better user profiles, personalization, and recommendations, or incorporate smarter search, a voice interface, or intelligent assistance, or improve your app any number of other ways. You could even build applications that see, hear, and react to situations you never anticipated.

Which programming language should you learn to plumb the depths of AI? You’ll want a language with many good machine learning and deep learning libraries, of course. It should also feature good runtime performance, good tools support, a large community of programmers, and a healthy ecosystem of supporting packages. That’s a long list of requirements, but there are still plenty of good options. 

Here are my picks for the six best programming languages for AI development, along with two honorable mentions. Some of these languages are on the rise, while others are slipping. Still others you only need to know about if you’re interested in historical deep learning architectures and applications. Let’s see how they all stack up. 

Python

At number one, it’s still Python. How could it be anything else, really? While there are maddening things about Python, if you’re doing AI work, you almost certainly will be using Python at some point. And some of the rough spots have smoothed a little.

As we head into 2020, the issue of Python 2.x versus Python 3.x is becoming moot as almost every major library supports Python 3.x and is dropping Python 2.x support as soon as they possibly can. In other words, you can finally take advantage of all the new language features in earnest.

And while Python’s packaging nightmares—where every different solution is broken in a slightly different way—are still present, you can use Anaconda about 95% of the time and not worry about things too much. Still, it would be nice if the Python world would fix this long-standing issue once and for all.

That said, the math and stats libraries available in Python are pretty much unparalleled in other languages. NumPy has become so ubiquitous it is almost a standard API for tensor operations, and Pandas brings R’s powerful and flexible data frames to Python. For natural language processing (NLP), you have the venerable NLTK and the blazingly-fast SpaCy. For machine learning, there is the battle-tested Scikit-learn. And when it comes to deep learning, all of the current libraries (TensorFlow, PyTorch, Chainer, Apache MXNet, Theano, etc.) are effectively Python-first projects.

If you’re reading cutting-edge deep learning research on arXiv, then you will find the majority of studies that offer source code do so in Python. Then there are the other parts of the Python ecosystem. While IPython has become Jupyter Notebook, and less Python-centric, you will still find that most Jupyter Notebook users, and most of the notebooks shared online, use Python. As for deploying models, the advent of micro service architectures and technologies such as Seldon Core mean that it’s very easy to deploy Python models in production these days.

There’s no getting around it. Python is the language at the forefront of AI research, the one you’ll find the most machine learning and deep learning frameworks for, and the one that almost everybody in the AI world speaks. For these reasons, Python is first among AI programming languages, despite the fact that your author curses the white space issues at least once a day.

Breaking through the hype around machine learning and artificial intelligence, our panel talks through the definitions and implications of the technology.

C++

C++ is unlikely to be your first choice when developing an AI application, but when you need to wring every last bit of performance from the system—a scenario that becomes more common as deep learning comes to the edge and you need to run your models on resource-constrained systems—it’s time to step back into the terrifying world of pointers once more.

Thankfully, modern C++ can be pleasant to write (honest!). You have a choice of approaches. You can either dive in at the bottom of the stack, using libraries like Nvidia’s CUDA to write your own code that runs directly on your GPU, or you can use TensorFlow or PyTorch to obtain access to flexible high-level APIs. Both PyTorch and TensorFlow allow you to load models generated in Python (or PyTorch’s TorchScript subset of Python) and run them straight in a C++ runtime, getting you closer to the bare metal for production while preserving flexibility in development.

In short, C++ becomes a critical part of the toolkit as AI applications proliferate across all devices from the smallest embedded system to huge clusters. AI at the edge means it’s not just enough to be accurate anymore; you need to be good and fast.

Java and other JVM languages

The JVM family of languages (Java, Scala, Kotlin, Clojure, etc.) continues to be a great choice for AI application development. You have a wealth of libraries available for all parts of the pipeline, whether it’s natural language processing (CoreNLP), tensor operations (ND4J), or a full GPU-accelerated deep learning stack (DL4J). Plus you get easy access to big data platforms like Apache Spark and Apache Hadoop.

Java is the lingua franca of most enterprises, and with the new language constructs available in Java 8 and later versions, writing Java code is not the hateful experience many of us remember. Writing an AI application in Java may feel a touch boring, but it can get the job done—and you can use all your existing Java infrastructure for development, deployment, and monitoring.

JavaScript

You’re unlikely to learn JavaScript solely for writing AI applications, but Google’s TensorFlow.js continues to improve and offer an intriguing way of deploying your Keras and TensorFlow models to your browser or through Node.js using WebGL for GPU-accelerated calculations.

However, one thing we haven’t really seen since the launch of TensorFlow.js is a huge influx of JavaScript developers flooding into the AI space. I think that might be due to the surrounding JavaScript ecosystem not having the depth of available libraries in comparison to languages like Python.

Further, on the server side, there’s not really much of an advantage to deploying models with Node.js as opposed to one of the Python options, so we may see JavaScript-based AI applications remain mainly browser-based in the near future. But that still creates plenty of interesting opportunities for fun like the Emoji Scavenger Hunt.

Swift

In last year’s version of this article, I mentioned that Swift was a language to keep an eye on. This year, it breaks into my top six. What happened? Swift For TensorFlow. A fully-typed, cruft-free binding of the latest and greatest features of TensorFlow, and dark magic that allows you to import Python libraries as if you were using Python in the first place.

The Fastai team is working on a Swift version of their popular library, and we’re promised lots of further optimizations in generating and running models with moving a lot of tensor smarts into the LLVM compiler. Is it production ready right now? Not really, but it may indeed point the way to the next generation of deep learning development, so you should definitely investigate what’s going on with Swift.

R language

R comes in at the bottom of our list, and it’s trending downward. R is the language that data scientists love. However, other programmers often find R a little confusing, due to its data frame-centric approach. If you have a dedicated group of R developers, then it can make sense to use the integrations with TensorFlow, Keras, or H2O for research, prototyping, and experimentation, but I hesitate to recommend R for production use or for greenfield development, due to performance and operational concerns. While you can write performant R code that can be deployed on production servers, it will almost certainly be easier to take that R prototype and recode it in Java or Python.

Other AI programming options

Of course, Python, C++, Java, JavaScript, Swift, and R aren’t the only languages available for AI programming. Here are two more programming languages you might find interesting or helpful, though I wouldn’t count them as top priorities for learning.

Lua

A few years ago, Lua was riding high in the world of artificial intelligence due to the Torch framework, one of the most popular machine learning libraries for both research and production needs. If you go delving in the history of deep learning models, you’ll often find copious references to Torch and plenty of Lua source code in old GitHub repositories.

To that end, it may be useful to have a working knowledge of the Torch API, which is not too far removed from PyTorch’s basic API. However, if, like most of us, you really don’t need to do a lot of historical research for your applications, you can probably get by without having to wrap our head around Lua’s little quirks.

Julia

Julia is a high-performance programming language that is focused on numerical computing, which makes it a good fit in the math-heavy world of AI. While it’s not all that popular as a language choice right now, wrappers like TensorFlow.jl and Mocha (heavily influenced by Caffe) provide good deep learning support. If you don’t mind the relatively small ecosystem, and you want to benefit from Julia’s focus on making high-performance calculations easy and swift, then Julia is probably worth a look.

 

5 Mistakes That Every Engineering Student Must Avoid

You might have heard and probably watched all the above movies’, especially if you are a student of engineering college but why I am listing out all the above movies’ names…can you guess?? . If you haven’t heard these movies’ names or didn’t catch my point then just open a new tab in your system or phone and search about all these movies. You guessed it right… all the above movies have a common thing and that is programming or hacking, programmer or hacker. Now we want to ask a few questions…how many of you have taken some positive lessons from these movies, get inspired and tried to spend the countless night doing coding or inventing something new? We bet that most of the students haven’t tried it or if they have tried then just for a short time.

College Life is awesome for engineering students and they get a lot of advice from others to do this and do that when they enter in a college, some are good and some are bad. If you are an engineering student and reading this blog we request you to search for the unemployment rate of engineers in India or the placement rate of engineers. Please try to read the article 80% Engineers Are Unemployed.

Yes, this is truth that engineers are unemployed and either they are depressed or trying to sustain their life anyhow from other resources. For most of the students, the reason is very simple and that is… mistakes they have made during their college period. We want you to carefully read the below points, keep your eyes open and see the truth so that you don’t repeat the same mistake and you put your efforts to secure your career and future.

1. Focusing Too Much on CGPA: CGPA is important in engineering we all agree but focusing too much on CGPA and ignoring the rest of the skill is not going to help a student in getting a job. When a student starts focusing only getting good grades in engineering their overall development lack somewhere and they face rejection in jobs and interviews when they apply for it. Always remember your practical knowledge is much more important than theoretical knowledge for companies.

Getting good grades can help you to get the interview calls but there is no guarantee of getting a job. You need to become a complete package for companies and in that case, your overall development is very important. There are so many students who are able to secure a good job even they have low grades and there are so many students who are a topper but unemployed. We are not telling you to completely ignore the grades, maintain a decent grade during your semester exams so you don’t face any problem when a company sets eligibility criteria for your grades.

2. Not Developing Communication Skills: Good communication skill is very important to stay in the job market. People who are better in delivering their ideas, visionaries effectively and able to communicate they are more successful and in demand for jobs in the market.

Expressing yourself and interaction with someone is very important and this is the common and important advice to all the students to improve their communication skills during their college period by giving a presentation, talking to a lot of people, making videos, participating in a group discussion or extra curriculum activities.

3. Rote Learning and Last Night Study: There are so many students who get advice during their college period that last night studies or just memorizing few questions are helpful to get good grades and that’s the blunder mistake students can do ever. You might get decent grades using this technique but that won’t help you in your future from the job perspective.

We want to give a clear picture to those students that technology is reaching at Artificial Intelligence, Big data Analytics, Machine Learning and a lot more so developing this habit won’t work when you need to work in a real-time environment on these cutting edge technologies. So make a habit of understanding the basics and fundamentals instead of rote learning and last night study. The more you understand, the less you have to remember.

4. Not Doing Internship and Projects: Maximum students get rejected in job interviews due to the lack of practical skills. Every company is looking for candidates who have experience in working real-time environments and have exposure for the same or at least have done some projects by themselves. Companies have basically three bars to hire a candidate…

• What do you know?
• How well you know?
• What will the company gain?

Your grades fulfill the first requirement but your practical skills, your technical knowledge or high-end skills reveals how well you know something and what’s the benefit they will get if they hire you. You can check the importance of internship from the link Why Internships are Important for Engineering Students or Freshers?  A lot of students do mistakes which they really need to avoid downloading the project from the internet or getting it from other resources. It’s painful for them to make projects but giving yourself a little bit of pain can help you to land up in a job later. So we highly recommend students to apply for internships as much as they can, explore the industries, check what skills or technologies they need to develop and also complete projects by themselves because hands-on-exposure helps a lot in getting a job.

5. Not Using Internet Resources Effectively: Internet is one of the big sources to learn something valuable and enhance the skills. Information is overloaded there and if a student wants to learn they can get a lot of useful materials over there and in that case, they need less help from seniors or professors as well. A lot of courses, resources and valuable knowledge are available there. Search for some online educational sites, watch YouTube video tutorials, learn some new technologies and take advantage of free resources available on the internet. Also, use social media effectively to join some group or community and take help from there.

Life is a roller coaster ride and it’s unpredictable. No one excelled great in life without failures. The master has failed more times than the beginner has tried. So, it doesn’t matter how many times you see failure, the important thing is how many times you pick yourself up and always try to give your best.