Deep Learning Tutorial: What it Means

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  1. What is Deep Learning?
  2. Why is Deep Learning important?
  3. How does Deep Learning work?
  4. What is the difference between Deep Learning and Machine Learning?
  5. How to get started with Deep Learning?
  6. Top Open Source Deep Learning Tools
  7. Commonly-Used Deep Learning Applications

 What is Deep Learning?

Deep Learning is a subset of Artificial Intelligence – a machine learning technique that teaches computers and devices logical functioning. Deep learning gets its name from the fact that it involves going deep into several layers of network, which also includes a hidden layer. The deeper you dive, you more complex information you extract.

Deep learning methods rely on various complex programs to imitate human intelligence. This particular method teaches machines to recognise motifs so that they can be classified into distinct categories. Pattern recognition is an essential part of deep learning and thanks to machine learning, computers do not even need to depend on extensive programming. Through deep learning, machines can use images, text or audio files to identify and perform any task in a human-like manner. 


All the self-driving cars you see, personalised recommendations you come across, and voice assistants you use are all examples of how deep learning is affecting our lives daily. If appropriately trained computers can successfully imitate human performance and at times, deliver accurate results – the key here is exposure to data. Deep learning focuses on iterative learning methods that expose machines to huge data sets. By doing so, it helps computers pick up identifying traits and adapt to change. Repeated exposure to data sets help machines understand differences, logics and reach a reliable data conclusion. Deep learning has evolved in recent times to become more reliable with complex functions. It’s no wonder that this particular domain is garnering a lot of attention and attracting young professionals.

Why is Deep Learning Important?

To say Deep Learning is important is, to say nothing about its growing popularity. It contributes heavily towards making our daily lives more convenient, and this trend will grow in the future. Whether it is parking assistance through technology or face recognition at the airport, deep learning is fuelling a lot of automation in today’s world.

However, deep learning’s relevance can be linked most to the fact that our world is generating exponential amounts of data today, which needs structuring on a large scale. Deep learning uses the growing volume and availability of data has been most aptly. All the information collected from these data is used to achieve accurate results through iterative learning models.

The repeated analysis of massive datasets eradicates errors and discrepancies in findings which eventually leads to a reliable conclusion. Deep learning will continue to make an impact in both business and personal spaces and create a lot of job opportunities in the upcoming time. 

How does Deep Learning work?

At its core, deep learning relies on iterative methods to teach machines to imitate human intelligence. An artificial neural network carries out this iterative method through several hierarchical levels. The initial levels help the machines learn simple information, and as the levels increase, the information keeps building. With each new level machines pick up further information and combines it with what it had learnt in the last level. At the end of the process, the system gathers a final piece of information which is a compound input. This information passes through several hierarchies and has semblance to complex logical thinking.

Let’s break it down further with the help of an example –

Consider the case of a voice assistant like Alexa or Siri, to see how it uses deep learning for natural conversation experiences. In the initial levels of neural network, when the voice assistant is fed data, it will try to identify voice inundations, intonations and more. For the higher levels, it will pick up information on vocabulary and add the findings of the previous levels to that. In the following levels, it will analyse the prompts and combine all its conclusions. For the topmost level of the hierarchical structure, the voice assistant will have learnt enough to be able to analyse a dialogue and based on that input, deliver a corresponding action. 

Deep learning use in voice assistant

What is the difference between Deep Learning and Machine Learning?

Though often used interchangeably, deep learning and machine learning are both part of artificial intelligence and are not the same thing. Machine learning is a broader spectrum which uses data to define and create learning models. Machine learning tries to understand the structure of data with statistical models. It starts with data mining where it extracts relevant information from data sets manually after which it uses algorithms to direct computers to learn from data and make predictions. Machine learning has been in use for a long time and has evolved over time. Deep Learning is a comparatively new field which focuses only on neural networking to learn and function. Neural networking, as discussed earlier, replicates the human neurals artificially to screen and gather information from data automatically. Since deep learning involves end-to-end learning where raw data is fed to the system, the more data it studies, the more precise and accurate the results are. 

This brings us to the other difference between deep learning and machine learning. While the former can scale up with larger volumes of data, machine learning models are limited to shallow learning where it reaches a plateau after a certain level, and any more addition of new data makes no difference. 

Following are the key differences between the two domain:

  • Data Set Size – Deep Learning doesn’t perform well with a smaller data set. Machine Learning algorithms can process a smaller data set though (still big data but not the propensity of a deep learning data set) without compromising its performance. The accuracy of the model increases with more data, but a smaller data set may be the right thing to use for a particular function in traditional machine learning. Deep Learning is enabled by neural networks constructed logically by asking a series of binary questions or by assigning weights or a numerical value to every bit of data that passes through the network. Given the complexity of these networks at its multiple layers, deep learning projects require data as large as a Google image library or an Amazon inventory or Twitter’s cannon of tweets.
  • Featured Engineering – An essential part of all machine learning algorithms, featured engineering and its complexity marks the difference between ML and DL. In traditional machine learning, an expert defines the features to be applied in the model and then hand-codes the data type and functions. In Deep Learning, on the other hand, featured engineering is done at sub-levels, including low to high-level features segregation to be fed to the neural networks. It eliminates the need for an expert to define the features required for processing by making the machine learn low-level features as simple as shape, size, textures, and pixel values, and high-level features such as facial data points and a depth map.
  • Hardware Dependencies – Sophisticated high-end hardware is required to carry the heavyweight of matrix multiplication operations and computations that are the trademark of deep learning. Machine learning algorithms, on the other hand, can be carried out on low-end machines as well. Deep Learning algorithms require GPUs so that complex computations can be efficiently optimized.
  • Execution Time – It is easy to assume that a deep learning algorithm will have a shorter execution time as it is more developed than a machine learning algorithm. On the contrary, deep learning requires a larger time frame to train not just because of the enormous data set but also because of the complexity of the neural network. A machine learning algorithm can take anything from seconds to hours to train, but a deep learning algorithm can go up to weeks, in comparison. However, once trained, the runtime of a deep learning algorithm is substantially less than that of machine learning.

An example would make these differences easier to understand:

Consider an app which allows users to take photos of any person and then helps to find apparels that are the same or similar to the ones featured in the photo. Machine learning will use data to identify the different clothing item featured in the photo. You have to feed the machine with the information. In this case, the item labelling will be done manually, and the machine will categorise data based on predetermined definitions.

In the case of deep learning, data labelling do not have to be done manually. Its neural network will automatically create its model and define the features of the dress. Now, based on that definition, it will scan through shopping sites and fetch you other similar clothing items. 

How to get started with Deep Learning?

Before getting started with Deep Learning, candidates must ensure that their mathematical and programming language skills are in place. Since Deep Learning is a subset of artificial intelligence, familiarity with the broader concepts of the domain is often a prerequisite. Following are the core skills of Deep Learning:

  • Maths: If you are already freaking out at the sheer mention of maths, let me put your fears to rest. The mathematical requirements of deep learning are basic, the kind that’s taught at the undergraduate level. Calculus, probability and linear algebra are few of the examples of topics that you need to be through with. For professionals who are keen on picking up deep learning skills but do not have a degree in maths, there are plenty of ebooks and maths tutorials available online, which will help you learn the basics. These basic maths skills are required for understanding how the mathematical blocks of neural network work. Mathematical concepts like tensor and tensor operations, gradient descent and differentiation is crucial to neural networking. Refer to books like Calculus Made Easy by Silvanus P. ThompsonProbability Cheatsheet v2.0The best linear algebra booksAn Introduction to MCMC for Machine Learning to understand the basic concepts of maths.
  • Programming Knowledge: Another prerequisite of grasping Deep Learning is knowledge of various programming languages. Any deep learning book will reveal that there are several applications for Deep Learning in Python as it is a highly interactive, portable, dynamic, and object-oriented programming language. It has extensive support libraries that limit the length of code to be written for specific functions. It is easily integrated with C, C++, or Java and its control capabilities along with excellent support for objects, modules, and other reusability mechanisms makes it the numero uno choice for deep learning projects. Easy to understand and implement, aspiring professionals in the domain start with Python as it is open-sourced. However, several questions have been raised on its runtime errors and speed. While Python is used in several desktop and server applications, it is not used for many mobile computing applications. While Python is a popular choice for many, owing to its versatility, Java and Ruby are equally suitable for beginners. Books like Learn to Program (Ruby)Grasshopper: A Mobile App to Learn Basic Coding (Javascript)A Gentle Introduction to Machine Fundamentals and Scratch: A Visual Programming Environment From MIT are some of the online resources you can refer to pick up coding skills. Great Learning, one of India’s most premier ed-tech platforms, even has a python curriculum designed for beginners who want to transition smoothly from non-technical backgrounds into Artificial Intelligence and Machine Learning. Following is a breakdown of the course showcasing what it covers:

Great Learning Python Course structure

  • Cloud Computing: Since almost all kinds of computing are hosted by cloud today, basic knowledge of cloud is essential to master Deep Learning. Beginners can start by understanding how cloud service providers work. Dive deep into concepts like compute, databases, storage and migration. Familiarity with major cloud service providers like AWS and Azure will also give you a competitive advantage. Cloud computing also requires an understanding of networking which a concept closely associated with Machine Learning. Clearly, these techniques are not mutually exclusive and familiarising yourself with these concepts will help you each of the skills faster.

Now that we have covered the fundamentals of deep learning, it is time to dive deeper into the different ways in which Deep Learning can be put to use. 

Deep Learning Types

  • Deep Learning for Computer Vision: Deep Learning methods used to teach computers image classification, object identification and face recognition involves computer vision. Simply put, computer vision tries to replicate human perception and its various functions. Deep learning does that by feeding computers with information on: 

1. Viewpoint variation: This is where an object is viewed from different perspectives so that its three-dimensional features are well recognised.

2. Difference in illumination: This refers to objects viewed in different lighting conditions.

3. Background clutter: This helps to distinguish obscure objects from a cluttered background.

4. Hidden parts of images: objects which are partially hidden in pictures need to be identified.

  • Deep Learning for Text and Sequence: Deep learning is used in several text and audio classifications, namely like speech recognition, sentiment classification, Machine translation, DNA sequence analysis, video activity recognition and more. In each of these cases, sequence models are used to train computers to understand, identify and classify information. Different kinds of recurrent neural networks like many-to-many, many-to-one and one-to-many are used for sentiment classification, object recognition and more. 
  • Generative Deep Learning: Generative models are used for data distribution through unsupervised learning. Variational Autoencoder (VAE) and Generative Adversarial Networks (GAN) aims at distributing data optimally so that computers can generate new data points from different variations. VAE maximises the lower limit for data-log likelihood, whereas GAN tries to strike a balance between Generator and Discriminator. 

Top Open Source Deep Learning Tools

Of the various deep learning tools available, these are the top freely available ones:

  1. TensorFlow: one of the best frameworks, TensorFlow is used for natural language processing, text classification and summarization, speech recognition and translation and more. It is flexible and has a comprehensive list of libraries and tools which lets you build and deploy ML applications. TensorFlow finds most of its application in developing solutions using deep learning with python as there are several hidden layers (depth) in deep learning in comparison to traditional machine learning networks. Most of the data in the world is unstructured and unlabeled that makes Deep Learning TensorFlow one of the best libraries to use. A neural network nodes represent operations while edges stand for multidimensional data arrays (tensors) flowing between them.
  2. Microsoft Cognitive Toolkit: Most effective for image, speech and text-based data, MCTK supports both CNN and RNN. For complex layer-type, users can use high-level language, and the fine granularity of the building blocks ensures smooth functioning.
  3. Caffe: One of the deep learning tools built for scale, Caffe helps machines to track speed, modularity and expression. It uses interfaces with C, C++, Python, MATLAB and is especially relevant for convolution neural networks. 
  4. Chainer: A Python-based deep learning framework, Chainer provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs). It can also build and train neural networks through high-level object-oriented APIs. 
  5. Keras: Again, a framework that can work both on CNN and RNN, Keras is a popular choice for many. Built on Python, it is capable of running on TensorFlow, CNTK, or Theano. It supports fast experimentation and can go from idea to result without any delay. The default library for Keras is TensorFlow. Keras is dynamic as it supports both recurrent networks and Convolutional Neural Networks and can also work on a combination of the two. Keras is popular for its user-friendliness guaranteed by its simple API. It is easier to debug Keras models as they are developed in Python. The compact models provide ease of extensibility with new modules that can be directly added as classes and functions in a building blocks kind of configuration.
  6. Deeplearning4j: Also a popular choice, Deeplearning4j is a JVM-based, industry-focused, commercially supported, distributed deep-learning framework. The most significant advantage of using Deeplearning4j is speed. It can skim through massive volumes of data in very little time.

Commonly-Used Deep Learning Applications

  • Virtual Assistants – Amazon Echo, Google Assistant, Alexa, and Siri are all exploiting deep learning capabilities to build a customized user experience for you. They ‘learn’ to recognize your voice and accent and present you a secondary human experience through a machine by using deep neural networks imitating not just speech but also the tone of a human. Virtual assistants help you shop, navigate, take notes and translate them to text, and even make salon appointments for you.
  • Facial Recognition – The iPhone’s Facial Recognition uses deep learning to identify data points from your face to unlock your phone or spot you in images. Deep Learning helps them protect the phone from unwanted unlocks and making your experience hassle-free even when you have changed your hairstyle, lost weight, or in poor lighting. Every time you unlock your phone, deep learning uses thousands of data points to create a depth map of your face and the inbuilt algorithm uses those to identify if it is really you or not.
  • Personalization – E-Commerce and Entertainment giants like Amazon and Netflix, etc. are building their deep learning capacities further to provide you with a personalized shopping or entertainment system. Recommended items/series/movies based on your ‘pattern’ are all based on deep learning. Their businesses thrive on pushing out options in your subconscious based on your preferences, recently visited items, affinity to brands/actors/artists, and overall browsing history on their platforms.
  • Natural Language Processing – One of the most critical technologies, Natural Language Processing is taking AI from good to great in terms of use, maturity, and sophistication. Organizations are using deep learning extensively to enhance these complexities in NLP applications. Document summarization, question answering, language modelling, text classification, sentiment analysis are some of the popular applications that are already picking up momentum. Several jobs worldwide that depend on human intervention for verbal and written language expertise will become redundant as NLP matures.
  • Healthcare – Another sector to have seen tremendous growth and transformation is the healthcare sector. From personal virtual assistants to fitness bands and gears, computers are recording a lot of data about a person’s physiological and mental condition every second. Early detection of diseases and conditions, quantitative imaging, robotic surgeries, and availability of decision-support tools for professionals are turning out to be game-changers in the life sciences, healthcare, and medicine domain.
  • Autonomous Cars – Uber AI Labs in Pittsburg are engaging in some tremendous work to make autonomous cars a reality for the world. Deep Learning, of course, is the guiding principle behind this initiative for all automotive giants. Trials are on with several autonomous cars that are learning better with more and more exposure. Deep learning enables a driverless car to navigate by exposing it to millions of scenarios to make it a safe and comfortable ride. Data from sensors, GPS, geo-mapping is all combined together in deep learning to create models that specialize in identifying paths, street signs, dynamic elements like traffic, congestion, and pedestrians.
  • Text Generation – Soon, deep learning will create original text (even poetry), as technologies for text generation is evolving fast. Everything from the large dataset comprising text from the internet to Shakespeare is being fed to deep learning models to learn and emulate human creativity with perfect spelling, punctuation, grammar, style, and tone. It is already generating caption/title on a lot of platforms which is testimony to what lies ahead in the future.
  • Visual Recognition – Convolutional Neural Networks enable digital image processing that can further be segregated into facial recognition, object recognition, handwriting analysis, etc. Computers can now recognize images using deep learning. Image recognition technology refers to the technology that is based on the digital image processing technology and utilizes artificial intelligence technology, especially the machine learning method, to make computers recognize the content in the image. Further applications include colouring black and white images and adding sound to silent movies which has been a very ambitious feat for data scientists and experts in the domain.

Great Learning’s Deep Learning Certificate Program is a comprehensive course which teaches you the essentials of the domain and its industry applications. Its hands-on projects and live sessions help students pick up the key functionalities effectively, even if they have no prior technical knowledge. 

Your essential weekly guide to Artificial Intelligence – October 2019 Part I

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In this week’s Artificial Intelligence digest, we have included some of the latest developments in the field of Artificial Intelligence. These developments suggest a bright future for AI applications across industries and the increasing scope of these technologies in creating more jobs globally in the coming years. 

Unveiling Three Ways Artificial Intelligence is Disrupting the Media and Entertainment Sector

Media and entertainment companies face several challenges that can be attributed to factors such as live streaming of content, unpredictable traffic, personalization and publishing permission-based content. Companies in the media and entertainment sector are investing a significant portion of their budget to improve bandwidth for streaming content seamlessly.

Alibaba Unveils Chip Developed for Artificial Intelligence

The Chinese e-commerce group unveiled the Hanguang 800 on Wednesday, which is designed to carry out the type of calculations used by AI applications much faster than conventional chips.

Software Robots Are Guiding us Into Intelligent Automation, With Less Stress

The big-bang AI and machine learning projects may be stuck in pilots and proofs of concepts, but a less glamorous form of intelligent automation may be percolating its way through processes and channels across enterprises — software bots associated with robotic process automation (RPA). These bots usually take on single-purpose tasks, such as pulling data for a purchase order or delivering an email confirming with a transaction. A majority of enterprises surveyed by Deloitte last year, 53 per cent, report they have put RPA in place, with 72 per cent expecting to do so within the next two years.

Artificial Intelligence Has Become a Tool For Classifying And Ranking People

AI’s ability to classify and rank people, to separate them according to whether they’re “good” or “bad” in relation to certain purposes. At the moment, Western civilization hasn’t reached the point where AI-based systems are used en masse to categorize us according to whether we’re likely to be “good” employees, “good” customers, “good” dates and “good” citizens. Nonetheless, all available indicators suggest that we’re moving in this direction.

Meanwhile, if you are interested to know more about the field of artificial intelligence, we have created a complete tutorial for you to know all about the different concepts and subjects under AI, along with its applications and career scope across industries. You can read the full article here: Artificial Intelligence Tutorial: Everything you Need to Know


If you found these articles to be interesting then head over to our Artificial Intelligence blog to get more updates.

Your Essentials Guide to Artificial Intelligence – September Part III

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Artificial Intelligence is an often overused yet little understood term in popular culture, evoking visions of a machine dominated world. The reality, however, is far from that – AI is not a disruptive element as it is frequently portrayed to be. It’s creating more job opportunities than it is taking away. Most importantly, it is aiding a wide variety of technological advances. The following news articles reflect how tech giants are pushing AI research forward and how different industries are benefiting from it.

Google Opens an AI Research Lab in Bengaluru to Advance Computer Science Research in India

At its recent annual event, Google for India announced that it is setting up a research lab in India which will focus on Artificial Intelligence and its applications in India. This research lab will be based in India’s tech capital, Bangalore and will be headed by a renowned scientist and ACM (Association for Computing Machinery) fellow, Dr Manish Gupta. The research activities will centre around applying AIML technologies in healthcare, agriculture and education. This lab is expected to help millions of people in the country who do not have access to quality healthcare or education, apart from taking the AI pledges forward.

Samsung Electronics is Actively Investing in Artificial Intelligence in Seven Countries

In a Samsung Newsroom interview recently, Gary Geunbae Lee, Senior Vice President and Head of Samsung Research’s AI Center in Seoul, revealed AI insights that are driving their centres in South Korea (Seoul), the U.S. (Silicon Valley and New York), the U.K. (Cambridge), Canada (Toronto and Montreal) and Russia (Moscow). These centres focus on various facets of AI, ranging from computer vision to language understanding, data analysis, robotics and more. The company aims to fuse AI functionalities with its products to present consumers with more smart solutions. AI ethical compliance is also a key focus area for Samsung, as it believes that AI can cause serious social problems if mishandled and abused.

Microsoft And Qualcomm Debut Their Vision AI Developer Kit  

Microsoft and Qualcomm have come together to build their debut Vision AI Developer Kit for computer vision applications. This platform uses Microsoft Azure ML and Azure IoT Edge platforms to run AI models locally or in the cloud. The hardware uses Qualcomm Snapdragon 603 chip and LDDR4X memory. 

Artificial Intelligence Used to Recognize Primate Faces in The Wild

Studying primitive species like Chimpanzees who have complex social lives can lead to many sociological discoveries but long-term research on primates is often hindered by low lighting, poor image quality and motion blur. Scientists at the University of Oxford are using AI software to recognise and monitor wildlife activities. These AI inputs are expected to aid research and cut down on expenses.

AI Technology is Creating Real Value Across Industries

“AI—like any technology in history—is neutral, it’s what we do with it that counts, so it’s our responsibility, as an AI ecosystem, to drive it in the right direction.” – Says Affectiva CEO Rana el Kaliouby. Affectiva is one of the 50 companies featured in the Forbes list of promising artificial intelligence companies. The founders of all of these companies think that AI has been held in a bad light for way too long, whereas in reality, it’s only as beneficial as we make it. On the other hand, founders also feel that we have inflated expectations from AI, which is after all, not as intelligent as a pattern-matching system. 

If you found these articles to be interesting then head over to our Artificial Intelligence blog  to get more updates. 


Your Weekly Guide to Artificial Intelligence – September Part II – GL

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Artificial Intelligence is paving the way to the future, one breakthrough at a time. On the other hand, there are debates on the ‘pros and cons’ and the ‘perceived vs. actual intelligence’ of AI. Here are a few recent articles that highlight this notion. Read on to learn more.

A Breakthrough for A.I. Technology: Passing an 8th-Grade Science Test

The Allen Institute for Artificial Intelligence, a prominent lab in Seattle, unveiled a new system that passed the test with room to spare. It correctly answered more than 90 per cent of the questions on an eighth-grade science test and more than 80 per cent on a 12th-grade exam. The system, called Aristo, is an indication that in just the past several months researchers have made significant progress in developing A.I. that can understand languages and mimic the logic and decision-making of humans.

Artificial Intelligence Aid Fight Against Global Terrorism

Although terrorists have become skilled at manipulating the Internet and other new technologies, artificial intelligence or AI, is a powerful tool in the fight against them, a top UN counter-terrorism official said this week at a high-level conference on strengthening international cooperation against the scourge. Read more to know how AI technologies are being used to counter global terrorism.

AI is Not as Smart as You Think

‘Computers won’t cause the end of civilisation.’

Speaking at a recent artificial intelligence seminar, Dr Mariarosaria Taddeo, a research fellow at the Oxford Internet Institute, said AI will never think for itself. There is not a shred of proper research that supports the idea that AI can become sentient. This is a technology that behaves as if it were intelligent, but that is nothing to do with creating or deducing.

Top Highlights From The World Artificial Intelligence Conference

With the theme of “Intelligent Connectivity, Infinite Possibilities”, the World Artificial Intelligence Conference 2019 concluded recently in Shanghai. During the opening ceremony, Alibaba Group Chairman Jack Ma and Elon Musk, CEO of Tesla had a 45-minute debate on the impact of AI on human civilisation, future of work, consciousness and environment. The event saw innovative applications related to industrial ecology, AI urban application, autonomous driving and other cutting-edge technologies, and about 400 companies which participated.

How Artificial Intelligence is Creating Jobs in India, Not Just Stealing Them

There is a growing demand for data-labelling services that are “localised”- both linguistically and culturally relevant to India. From an opportunity point of view, there are about a lakh jobs posted on various portals currently. 

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For more roundups on AI, watch this space!

If you are interested in upskilling with Artificial Intelligence, read more about Great Learning’s PG program in Artificial Intelligence and Machine Learning.

Your Weekly Guide to Artificial Intelligence – August Part IV – GL

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Artificial Intelligence is marching forward at a quick pace and organizations have started reaping results and revenue from their AI initiatives. There’s no doubt that AI is here to stay, but with these advanced technologies becoming a part of day to day human life, many concerns are arising around the success and ethics of Artificial Intelligence. We try to highlight a few in this week’s AI digest.  

AI Can Read Your Emotions. Should it?

There are obvious advantages to our machines being more sensitive to our needs. It should mean a better, more intuitive service with less time wasted. Also, probably a billion things we haven’t thought of yet. But emotion AI raises concerns, too. Do we want our emotions to be machine-readable? How can we know that this data will be used in a way that will benefit citizens? …. [Read More]

Will China Lead The World in AI by 2030?

China not only has the world’s largest population and looks set to become the largest economy — it also wants to lead the world when it comes to artificial intelligence (AI). In 2017, the Communist Party of China set 2030 as the deadline for this ambitious AI goal, and to get there, it laid out a bevy of milestones to reach by 2020. As this first deadline approaches, researchers note impressive leaps in the quality of China’s AI research…. [Read More]  

What Does an AI Ethicist do?

Microsoft was one of the earliest companies to begin discussing and advocating for an ethical perspective on artificial intelligence. Nadella’s primary focus was on Microsoft’s orientation toward using AI to augment human capabilities and building trust into intelligent products. With these foundations laid, in 2018, Microsoft established a full-time position in AI policy and ethics. Tim O’Brien, who has been with Microsoft for 15 years as a general manager, first in platform strategy and then global communications, took on the role…. [Read More]

Five Experts Share What Scares Them The Most About AI

Futurism asked five AI experts at the conference about what they fear most about a future with advanced artificial intelligence. Their responses, below, have been lightly edited. Hopefully, with their concerns in mind, we’ll be able to steer society in a better direction — one in which we use AI for all the good stuff, like fighting global epidemics or granting more people education, and less of the bad stuff…. [Read More]

To read more about Artificial Intelligence and it’s career prospects, visit:

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Your essential weekly guide to Artificial Intelligence – August Part I

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Artificial Intelligence has come a long way from theoretical discussions and assessing the scope and consequences of it, to real-life applications. Today, applications of Artificial Intelligence can be witnessed across industries globally. Here is a glimpse of this notion as we list out some of these applications in different sectors.   

Nvidia’s Autonomous Robocar Replaces The Combustion Engine For Good

This July, the first racing competition for self-driving cars –Roborace – took place at the Goodwood Festival of Speed. The race features the pairing of man and machine as they test their automated driving systems (ADS) and show off some of the autonomous industry’s latest tech. Nvidia was on the track in July 2019 with their autonomous concept race car – Robocar. 

AI-Based FaceApp Takes The Internet by Storm as Users Rush to See Their Old-Age Looks

FaceApp, the AI-based photo editor has taken the internet by storm — all over again. The app, developed by Russian company Wireless Lab, is actually two years old but was recently updated with an improved old age filter. According to a Forbes report, the viral app already has access to more than 150 million names and faces.

Aetna Enlists AI to Settle Health Insurance Claims

Aetna has created artificial intelligence (AI) software to settle claims, a solution that could provide a blueprint for broader automation of complex processes at the health insurance giant. The software rapidly parses complex healthcare provider contracts, whose blend of information about medical conditions and financial data often tax the patience of the trained humans who process them.

Swiggy’s Tech Head on How The Food-Tech Startup is Using AI to Go Beyond Food Deliveries

The food-tech startup has unveiled a bold vision of transforming itself into an artificial intelligence-first product as it has started to leverage this tech to add more services that go beyond food delivery.

This Artificial Intelligence ‘Sees’ Cells From Within

Greg Johnson and his coworkers at Seattle’s Allen Institute have trained a deep learning system to label known cell parts, and then discover unknown components. the team believes the program can be used to build the most accurate models ever of cells’ inner workings. 

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Your essential weekly guide to Artificial Intelligence – July 24

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Artificial Intelligence has spanned its wings to the vast majority of industries, but its impact on the Healthcare industry has many life-altering implications. Here are a few articles that showcase how Artificial Intelligence is being harnessed to enhance medical procedures and treatments. 

Elon Musk’s Neuralink Says It’s Ready for Brain Surgery

The startup, Neauralink just unveiled its plan to implant paralyzed patients with electrodes that’ll let them work computers with their minds. The company will seek U.S. Food and Drug Administration approval to start clinical trials on humans as early as next year, according to President Max Hodak.

Could Artificial Intelligence be The Future of Cancer Diagnosis?

Doctors have access to high-quality imaging, and skilled radiologists can spot the telltale signs of abnormal growth. Once identified, the next step is for doctors to ascertain whether the growth is benign or malignant. Some scientists are investigating the potential of artificial intelligence (AI). In a recent study, scientists trained an algorithm with encouraging results.

My Robot Surgeon: The Past, Present and Future of Surgical Robots

As robots used for surgeries become increasingly common, we trace the journey of surgical robots in India. 


Apart from Healthcare, these are some interesting applications of Artificial Intelligence in other sectors: 

Expressway Operators to Use AI to Forecast Traffic Jams

Plans are afoot to enlist artificial intelligence in anticipating traffic snarls in the year-end and New Year holidays. Central Nippon Expressway Co. said it will start trials of an AI-assisted forecasting system to predict congestion on the Tomei, Chuo and other expressways in Japan during the Bon holiday season next month.

Kitchen Disruption: Better Food Through Artificial Intelligence

Players in the food industry are embracing artificial intelligence to better understand the dynamics of flavour, aroma and other factors that go into making a food product a success.

Earlier this year, IBM became a surprise entrant to the food sector, announcing a partnership with McCormick to “explore flavour territories more quickly and efficiently using AI to learn and predict new flavour combinations”.

This AI is Helping Scientists Develop Invisibility Cloaks

The general idea behind an invisibility cloak is that it gives the wearer the ability to move through the world undetected. The first step is to engineering a material that can do that. A team of South Korean researchers has developed an AI capable of designing new metamaterials with specific optical properties.

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