Countries leading the way in AI

Merely one mention of artificial intelligence invokes the pictures of robots, chatbots, or self-driving cars. Artificial intelligence, a branch of computer science deals with devising technology and machines that are capable of performing tasks that require human intelligence. AI is everywhere and it has not stepped down since its invasion. The new decade will witness massive investments by global technology giants into AI technologies.

In the race to acquire and develop the most efficient and ingenious technologies, countries worldwide have been setting guidelines and investing immensely in the field of AI. Following are the statistics of the global AI readiness Index conducted by Oxford Insights and the International Development Research Centre (IDRC) in 2019. The index covers 194 countries and territories and ranks them according to their preparedness to implement AI in the delivery of public services and internal operations. Each country is allocated a score that considers 11 input metrics under 4 clusters: governance, infrastructure and data, skills and education, and government and public services. The report suggests that the rankings are dominated by countries that have robust economies, an abundance of data and information, and a government with a strong vision.

  • Singapore comes first in the list of artificial intelligence readiness with a score of 9.186.
  • The top 20 positions are acquired by Canada, New Zealand, Australia, and some European countries, and Asian countries. China, currently 21st in the global rankings will improve in the upcoming years because of the government’s inclination to optimize data and artificial intelligence.
  • Most of the countries in North America were awarded higher ranks in the list while the African and Asia-Pacific regions were the worst performers.

Country

 Rank Score
Singapore 1 9.186
United Kingdom 2 9.069
Germany 3 8.810
United States of America 4 8.804
Finland 5 8.772
Sweden 6 8.674
Canada 6 8.674
France 8 8.608
Denmark 9 8.601
Japan 10

8.582

 

  • India stands third in the Asia Pacific region after Singapore and Hong Kong and 19th in the global index overall. According to surveys, it is one of the most prepared economies with regard to government readiness to artificial intelligence. But despite this availability, it lacks creativity, innovation, and awareness which are the reasons behind Indian start-ups lagging behind those of the American and other developed countries. 76% of start-ups in India believe that there is a dearth of skilled professionals in the field of AI and hence hesitate from using these technologies in their business. But the established companies like Amazon, Google, and Apple are employing AI technologies like deep learning, language processing, and machine learning and are acquiring start-ups to provide new and improved experiences across their services.
Present and expected Scenario

China, USA, Japan, United Kingdom, and Germany are the leading countries in AI research. China recently announced its intention to become ‘a principal world center of artificial intelligence innovation’ by 2030. And America’s distinct pool of research technology knowledge and business market power will contribute to helping them achieve their AI development objectives.

Although the United States remains highly invested in AI and the latest technologies, it seems likely that China might win over America in the race. Despite having the 5 tech-giants – Amazon, Facebook, Apple, Google, and Microsoft, it looks as if the US will be lagging behind developing countries like China. In the US, the resources are available in ample amounts but are fragmented and the potential isn’t utilized efficiently. Also, the national leadership is quite weak and there is a lack of systematic vision.

On the other hand, China apart from having the latest computers, smartphones, gadgets and other electronic devices is now focusing on system-wide AI. This includes robotics, advanced medical equipment, and autonomous vehicles (AVs). China’s growing internet economy and shift of industries towards data analytics and AI trends will be a major contributing factor in its AI boom. Some examples of Chinese AI companies that are developing are UBTECH Robotics which is developing humanoid robots and SenseTime which is building facial recognition technology.

Thus, although the United States still remains ahead of others in the AI industry, if China implements the policies and strategies effectively, it will soon manage to replace the USA. Therefore, from the increasing demand and usage of AI-driven technologies, it can be concluded that AI, machine learning, and data science will thrive as the most in-demand skills in the near future.

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Singapore’s artificial intelligence industry will be booming in the upcoming decade. Singapore is one of the first countries to announce a national AI strategy in 2017. One of the most effective ideas that were put into action by the government of Singapore to achieve this was the establishment of the AI Ethics Advisory Council. This council has been established to assist the government to develop standards and guidelines and issue practical guidance for the voluntary adoption of AI in businesses and organizations. (Everything you need to know about Singapore’s AI strategy)

Singapore is a trailblazer in the Asia-Pacific region when it comes to accepting new technologies. Singapore has chosen five big AI-driven development projects that address national challenges keeping into consideration the impact on society and economy with an aim to become the global hub of AI technologies. Also, educational institutions there have increased emphasis on fields such as mathematics, statistics, computer science, and information technology to allow students to understand the significance and trend of data science, machine learning, and artificial intelligence. Business and management organizations too are focusing on data trends as well apart from their regular curriculum (Analyze data and build stunning reports using our services). UAE and Singapore were trendsetters in bringing AI decision-making at the cabinet-level. They have justified the strengths and strategies that have the potential to make these countries the leading producers of AI technologies.

Present and expected Scenario

China, USA, Japan, United Kingdom, and Germany are the leading countries in AI research. China recently announced its intention to become ‘a principal world center of artificial intelligence innovation’ by 2030. And America’s distinct pool of research technology knowledge and business market power will contribute to helping them achieve their AI development objectives.

Although the United States remains highly invested in AI and the latest technologies, it seems likely that China might win over America in the race. Despite having the 5 tech-giants – Amazon, Facebook, Apple, Google, and Microsoft, it looks as if the US will be lagging behind developing countries like China. In the US, the resources are available in ample amounts but are fragmented and the potential isn’t utilized efficiently. Also, the national leadership is quite weak and there is a lack of systematic vision.

On the other hand, China apart from having the latest computers, smartphones, gadgets and other electronic devices is now focusing on system-wide AI. This includes robotics, advanced medical equipment, and autonomous vehicles (AVs). China’s growing internet economy and shift of industries towards data analytics and AI trends will be a major contributing factor in its AI boom. Some examples of Chinese AI companies that are developing are UBTECH Robotics which is developing humanoid robots and SenseTime which is building facial recognition technology.

Thus, although the United States still remains ahead of others in the AI industry, if China implements the policies and strategies effectively, it will soon manage to replace the USA. Therefore, from the increasing demand and usage of AI-driven technologies, it can be concluded that AI, machine learning, and data science will thrive as the most in-demand skills in the near future.

Latest Applications of Computer Vision

How fascinating would it be to extract hidden values from images and understand them even better! Computer vision is a subpart of artificial intelligence that provides AI and computer science enthusiasts the ability to modify and transforming images according to their imagination and creativity using algorithms.

Computer vision (CV) is defined as an interdisciplinary scientific field that deals with how computers can gain a high-level understanding from digital images or videos. It helps computers to understand the content and characteristics like shape, color, texture, and size of digital images and videos.

Following are some of the latest applications in computer vision.

General use cases-

  • Creating a 3D model using 2D images – Using computer vision to make 3D models from 2D images is called photogrammetry. Several images taken at 360 degrees around the object are supplied to an algorithm that returns the 3D model. Photogrammetry is used in fields such as topographic mapping, architecture, engineering, manufacturing and geology.

 

  • Human pose estimation – Pose estimation is the technique of understanding the pose of a person or an object from a digital photo or video. It is also sometimes referred to as the localization of human joints as it predicts the positions of a person’s joints in an image or video Pose estimation can be done in 2D as well as in 3D. Human pose estimation has many applications such as those in the field of augmented reality and motion capture. It is also used for training robots where instead of manually programming robots to follow trajectories, they are made to follow the trajectories of human pose skeletons.

 

  • Digital Image Processing – Many real-world problems in computer vision require integrating a large number of images to create computer vision systems. When approaching such computer vision problems, processing one image in the context of some other might be required. GAN (Generative Adversarial Networks) is a technique that comes under the machine learning domain and uses training set data to generate new data. There are many applications of GAN in computer vision with some of them being generating realistic photographs, generating cartoon characters and emojis, and editing photographs.

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Industry-Specific Use Cases

  • Computer vision in healthcare and medicine- Computer vision has a wide range of applications in the medical sector.
    • Computer vision techniques are used in the healthcare sector for diagnoses like CT scans, X-rays, and MRIs. These technologies are used to convert images obtained on scans to 3D models for better understanding and detect anomalies like tumors and neurological illnesses. Images are extracted from scans and used to train models that detect anomalies.
    • Gaze tracking and eye-area analysis are also widely used applications of computer vision to detect cognitive impairments.

 

  • Automotive industry– Tesla is one of the few automotive companies which uses Autopilot features in its cars. These self-driving vehicles have cameras attached to them that can record live footage and allow computer vision to create 3D visualizations. Using these 3D maps and visualizations, accidents can be prevented.

 

  • Agriculture– There can be numerous ways to use computer vision techniques in agriculture for purposes like crop monitoring, livestock management, and forestry management. Cameras installed in drones click pictures of agricultural fields which can be used later to train computer vision models to enable them to do work like spraying adequate amounts of pesticides and hence monitor crop growth. Also, a well-trained drone can identify livestock, count, and monitor them. They can also be used to check the growth and health of trees in forests and take into account disruptive activities if any like deforestation.
Computer Vision in COVID times

  • Development of social-distancing tools- One of the precautionary measures to prevent infection from coronavirus is social distancing. In order to ensure whether people in an area are following the social distancing norms, computer vision techniques are being used. One of these techniques is using object detection and tracking. Each person appearing in the video being recorded in real-time is detected using a bounding box. Further, the movement of the boxes is tracked and distances between two adjacent boxes are calculated. If there is any kind of violation detected, the boxes are consequently highlighted.

 

  • In the medical sector – The most recent use of computer vision in the medical sector is identifying Covid-19 infections. A common symptom of computer vision is pneumonia. Analyzing chest X-rays can help in detecting Covid-19.

 

In a nutshell, it can be concluded that computer vision as a subfield of artificial intelligence has found its applications anywhere and everywhere in the present scenario, and in the near future, it will effectively harbinger AI technologies that are as human as us.

Latest developments in Natural Language Processing (NLP)

Ever wondered how robots and machines perceive a command given to them?

Well, Natural language processing (NLP) gives them the ability to read, understand, and deduce useful information them from human languages. There are many applications of natural language processing including this.

As defined by Wikipedia, Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language. It talks about how to program computers to process and analyze large amounts of natural language data. NLP finds numerous applications in today’s world with major ones being in chatbots, sentiment analysis, and market intelligence.

Following are some of the major trends and advancements and some NLP examples that have dominated AI and the tech world in recent years-

  • Business Intelligence– There is a parallel connection between business intelligence and NLP. NLP facilitates the user’s interaction with complicated databases.
    • Using NLP companies gain product information like marketing and sales information, customer service, brand notoriety and the present talent pool of a company.
    • Another popular method of NLP used in BI is opinion mining. It uses NLP to extract customer sentiments from their reviews and ratings.
  • Semantic modelling- Semantic analysis is the process of relating syntactic structures, from the levels of phrases, clauses, sentences, and paragraphs to the level of the writing as a whole, to their language-independent meanings. Its goal is to draw the exact meaning from a text. It processes the logical structure of the text and identifies the most relevant elements in the text. It also understands the relationships between the elements of the text which is used in further analysis.
  • Chatbots– Natural Language Processing in AI is very popular. Atleast one-fourth of the organizations will have chatbots or visual customer assistants or some other type of NLP included in their customer service system by 2020. Chatbots learn the semantic relations, understand the objective of the questions asked, and then automatically perform the filtration and organization necessary to serve a relatable and significant answer, rather than simply showing the data. For instance, Microsoft’s Cortana is helping many small and large-scale businesses do research and process data by voice.
  • Human-machine interaction- One of the most common examples of usage of NLP in human-machine interaction is spam detection where emails undergo a process of getting filtered administered under NLP algorithms based on whether it is spam or not.
  • Deep learning for NLP– Deep learning techniques like Recurrent Neural Networks are used to get accurate results after analyzing the data.
  • Supervised and unsupervised learning– Natural Language Processing in machine learning is used for text analytics where statistics identify sentiments, expressions, and aspects of speech.

 

 

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Let us discuss the supervised and unsupervised learning aspects of NLP in more detail below since they house some of the most popular techniques.

In supervised learning, a set of text documents are tagged with examples with the machine of what is to be searched. These supplied examples are used to train the model which is later supposed to analyze the unlabelled or untagged text. Some of the most popular supervised learning NLP techniques are Support Vector Machines and Neural Networks. (A good read – Natural Language Processing (NLP) for Machine Learning)

In unsupervised learning, the model is trained without pre-tagging. Clustering, Latent Semantic Indexing (LSI), and Matrix Factorization are some popular techniques in unsupervised learning NLP.

  • In clustering, a similar types of data is grouped into sets and later sorted based on relevance using algorithms.
  • LSI on the other hand involves identifying words and phrases that frequently occur in the given text.
  • Matrix Factorization is different from the other two as it deals with breaking larger matrices into smaller ones using latent factors i.e., similarities between two or more items.
  • Reinforcement learning- Reinforcement learning along with supervised and unsupervised learning forms the three basic paradigms for ML. It allows machines and software applications to determine the ideal behaviour within a specific context. Tasks such as summarization of a text are performed by reinforcement learning algorithms.
  • Company monitoring- The impact of social media is irreplaceable. It has become an integral part of the normal life of every individual and perhaps this is the reason why companies and organisations have started focusing on social media interactions for promotions and growth of their business and reach more than ever before And social media monitoring tools such as Buffer and Hootsuite have been built using the latest algorithms of NLP. Tools like these help in monitoring company’s engagement in the market.

 

General Natural Language Processing Tasks

Now as we have seen various applications of NLP, let us walk through the general NLP tasks that are followed when NLP systems deal with a language-

  • Content categorization– Includes summarization along with content indexing, duplication, and content alerts.
  • Topic discovery and modelling- Deducing meaning from the text and applying analytics
  • Contextual extraction- Extracting information from text-based sources
  • Sentiment analysis-Includes identifying specific moods and opinion mining.
  • Speech-to-text and text-to-speech conversion-Transforming voice commands into written text, and vice versa.
  • Document summarization-Generating structures from large bodies of text.
  • Machine translation- Automatic translation of text or speech from one language to another.

NLP has gained popularity since its inception. Devices like Amazon’s Alexa are being used widely all across the globe today. And for enterprises, business intelligence and customer monitoring are fast becoming popular and will dominate the sector in the coming years.

Artificial Intelligence in Cloud Computing

What is cloud? Does it have anything to do with artificial intelligence? How is cloud computing and AI affecting the fast growing technological and business world? Let us dive in deep into exploring these technical terms and their dependence on each other.

Wikipedia defines cloud computing as on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. In simpler words, it is the use of hardware and software to deliver services or access and store data over a network, generally the Internet. Google Drive is a very common example of cloud computing service that works with storage apps like Google Docs, slides and sheets. Now the question arises, how can AI, which is the future of technology, add value to the cloud computing services? According to subject matter experts, AI might just be the technology to revolutionize cloud computing solutions.

Merging AI and cloud computing

Merging of cloud computing and AI will allow users not just to store data, but also to analyse it and draw conclusions from it. Over the years companies like Microsoft, IBM, Google and Amazon have invested enormously in AI, especially in the cloud software solutions. In the present scenario cloud-AI is identified as the following major sub-groups:

Machine learning cloud services: The integration of machine learning in the cloud is known as intelligent cloud. Using ML cloud services, apart from storing and networking users can apply machine learning algorithms efficiently and in comparatively less time. Following are some of the prominent AI applications in cloud:

  • IoT cloud: IoT (Internet of Things) cloud architectures are the cloud services that power IoT. They are used to store and work on data that is generated by IoT. Amazon Web Services IoT platform and Microsoft’s Azure platform are examples of IoT cloud services.
  • Business Intelligence: BI services are using cloud based AI services to get deep insights into the behaviour of their target audience. They use cloud services to store and manage large amounts of customer data and the machine learning algorithms are used for analysis and solutions. (Read – Lets talk AI in BI)
  • Chatbots: Chatbots which are AI based software that can simulate conversations with a user in defined natural language are in developmental phase. Cloud based services can allow storage of very large amounts of data which the chatbots can use to learn and evolve.  (Read – How Conversational AI Works?)
  • Cognitive cloud: Cognitive cloud computing is the use of computerized models to simulate the human thought process in complicated situations where the answers may be ambiguous. It refers to the services that are designed to work on AI and signal processing. It includes machine learning, natural language processing and human-computer interaction.
  • AI-as-a-service platforms: AI as a service allows individuals and companies to experiment with AI for various purposes without large initial investment and with lower risk. It is a third party that offers AI outsourcing thus it is considered as a very cost-effective model for developing businesses.

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Cloud AI platforms: Cloud AI platforms like Google Cloud and AWS, are used to train machine learning models, host trained models and use the model to make predictions and deliver insights.

Implementation of AI in cloud computing can enable the latter to make smart decisions on its own. Following are the major advantages of using AI in cloud computing:

  • Improved decision making: Cloud can only be used to store and process data. With the use of machine learning and artificial intelligence algorithms, one can identify patterns and extract valuable insights from datasets. These insights indicating customer behaviour can be used by organizations to promote their growth.
  • Minimizing costs and obtaining better output: The combination of AI and cloud computing aims at reducing the overall costs. The AI cost is reduced significantly as when the cloud is used, there is no requirement for a separate data centre. By moving to cloud computing, businesses and AI based services can significantly save capital costs through a reduction in expenditure on equipment, infrastructure and ML solutions.
  • Analytics: A large amount of data that is stored in the cloud requires sorting and analysis. AI can do this task efficiently. On the contrary, even if skilled analysts take this job of analysis, it would require a longer time and would be very tedious. So, using AI with cloud for analysis saves time, human effort, and cost.
  • Applications in the field of data mining: Cloud can store the data and update it on regular basis and AI can be useful in extracting useful information from it.
  • Seamless data access: In simple language, it refers to unlimited data access privileges. Implementing AI in cloud computing resolves the issues of delay inaccessibility of data and enables the cloud to solve potential problems in advance.
Nowadays, almost every technology comes with cloud backup services. So it can be concluded that the demand of cloud computing services will seemingly increase in the years to come. AI on the other hand has already started to dominate the tech-world and can be seen in every work domain. Though their usage together is in primary stage, companies are still investing enormously in cloud computing AI services.  This is because when both of these are merged, the quality and benefits of each are enhanced tremendously especially when it comes to their usage the booming commerce world.  This combination can be looked at as one of the most remarkable innovations in the future.

How Analytics is helping small businesses survive Covid-19?

Analytics trends in the business world

Data is power for small-scale businesses.  Ensuring the compatibility of adopted strategies with the business goals and objectives is supremely important for any business to grow. With ample insights into the usage of their products and services, smaller businesses can learn about customer interests, predict trends and align their marketing and product management approach. And this is when analytics comes into the picture.

What is analytics?

Analytics is the systematic analysis of data using statistical, computer programming and operations research techniques to derive patterns and meaningful insights from it and utilise them to make decisions effectively. It helps obtain a comprehensive analysis to understand what is working and what needs to be improved. It is time that small & medium businesses start owning data analytics and use it to their advantage.

For small scale businesses with limited resources and assets, social media pages and company websites can be tracked efficiently to gather customer data (Read: Image Analytics and AI in social media marketing). But merely gathering this data doesn’t complete the job. The data obtained requires sorting and visualisation. Tools like Google Analytics and Tableau provide analytics reports free of cost, hence are considered as the most feasible options for data analysis for smaller companies. R and Apache Spark are a few of the other popular business analytics tools.

In 2019, the following data analytics trends were prominent in the business world-

1. Deep learning- Deep learning, a part of machine learning has advanced techniques that are helping companies and firms to improve their decision-making abilities and facilitate operational work. It is based on artificial neural networks with representation learning. These are capable of sorting and structuring a large amount of data. An example of application of deep learning for many businesses is content recommendation. Businesses should start using deep learning to analyse the user’s taste and recommend content accordingly thus improving customer experience and easing marketing efforts.

2. Machine learning- The adoption of machine learning is increasing by leaps and bounds, and that’s not surprising given its benefits, from eliminating manual tasks to deducing useful insights from data. A subset of artificial intelligence, machine learning is the study of algorithms that improve on their own. Customer Segmentation & Lifetime Value Prediction are some of the most popular applications of machine learning in business.

3. Dark data- Dark data is the data that is acquired through computational operations but is not used to predict trends and draw conclusions from. Unused email correspondences and raw survey data are examples of dark data. This data obtained is analysed to obtain information like the customer’s preferred mode of communication. The traditional unstructured data that inundates the data centres in firms can be interpreted easily using dark data analytics and can be used to promote the growth of the company.

Small-scale businesses can use data analytics and machine learning algorithms to track customers throughout the sales and transaction cycle and can use analytics reports to find answers to questions like at what stage products and services are brought most frequently, thus improving customer service.

Analytics techniques provide detailed insights into customer behaviour and this knowledge is remarkably valuable to developing businesses. Analysing customer reviews, frequently asked questions and conversations between sales assistants and customers can contribute in setting standards to improve customer service hence boost growth. Analytics can also be used to manage budget and expenditure efficiently. Businesses with abundant resources can invest in multiple marketing strategies and opt for business intelligence solutions but smaller businesses can neither afford nor apply these on them. In such cases, analytics plays a crucial role. By focusing on a limited number of established and tested sales strategies the smaller companies can manage to increase the RoI.

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Data Analytics and Business during the pandemic

While the global health professionals and scientists are joining hands to accelerate the research and development process and devise standards to curb the spread of the coronavirus, a major section of the business world comprising of the small-scale businesses is struggling to survive the economic and global supply chain disruptions. But on one hand, as this crisis complicated the lives of businessmen, on the other it opened newer dimensions and perspectives for them specifically in merging with the field of data science and data analytics.  A recent survey conducted by Sisence, a Business Intelligence software company, suggests that businesses are shifting massively to data analytics to help them sustain the pandemic and expand their business.

Analytics is booming since and before the rise of the Covid-19 pandemic. Encountering the sudden downfall in revenues, businesses are using analytics reports to cater to the changing demands and needs of their customers and search for ways for better performance in their respective ventures (Read: Stories of small business: resilience amid COVID-19)

Some insights into the survey:

  • The survey demonstrates that 55% businesses, during the pandemic are using analytics techniques to improve efficiency and 47% and 45% of the total that were surveyed are using the same to support customers and predict future outcomes respectively.

  • The usage of analytics has catapulted significantly in the smaller firms and companies. 68% small businesses are using analytics for operational purposes, 56% in finance, 50% in sales and 45% in products.

Most importantly, this survey reveals that the data professionals remain optimistic about the business opportunities and the importance of analytics in businesses despite the growing economic challenges. 79% of those surveyed believe we’ll survive this global crisis and there are no chances of data analytics trends getting diminished in the near future.

Why did the small-scale businesses suddenly observe an ardent spike in the usage of data analytics during Covid-19 pandemic?

Small businesses are focusing on improving their efficiency and building a strong customer base. They are certainly not established enough to opt for a single streamlined business intelligence solution, like other larger companies. Therefore, to draw insights and convert them into actions, data analytics is the most suitable tool for them. And this is why the businesses that have employed less than 200-300 people have shown the maximum increase in the usage of data analytics during pandemic.

By 2021, 265% growth is expected in e-commerce sales which implies huge amount of production of data. And with two out of every three companies opting for data analytics in the present to manage their data, the increasing demand for analytics skills seems justified.

Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence, Machine Learning, and Deep Learning are one of the most prominent topics in the domain of technology at the present. Although the three terminologies Artificial Intelligence, Machine Learning, and Deep Learning are used interchangeably, are they really the same? Every technophile is stuck at least once in the beginning whenever there is a mention of artificial learning vs machine learning vs deep learning. Let us try to find out how actually these three terms differ.

The easiest way to think of the relationship between the above terms is to visualize them as concentric circles using the concept of sets with AI — the idea that came first — the largest, then machine learning — which blossomed later, and the most recent being deep learning — which is driving today’s AI explosion — fitting inside both.

Graphically this relation can be explained as in the picture below.

As you can see in the above image consisting of three concentric circles, Deep Learning is a subset of ML, which is also a subset of AI. This gives an idea that AI is the all-encompassing concept that initially erupted, which was then followed by ML that thrived later, and lastly, Deep Learning that is promising to escalate the advances of AI to another level.

Starting with AI, let us have a more in-depth insight into the following terms.

Artificial Intelligence

Intelligence, as defined by Wikipedia, is “Perceiving the information through various sources, followed by retaining them as knowledge and applying them with real-life challenges.” Artificial intelligence is the science that deals with machines that are programmed to think and act like humans. By Wikipedia, it is defined as the simulation of human intelligence in machines using programs and algorithms.

Machines built on AI are of two types – General AI and Narrow AI

General AI refers to the machines capable of using all our senses. We’ve seen these General AI in Sci-Fi movies like The Terminator. In real life, a lot of work has been done on the development of these machines; however, more research is yet to be done to bring them into existence.

What we CAN do falls in the hands of “Narrow AI”. These refer to the technologies that can perform specific tasks as well as, or better than, we humans can. Some examples are – classifying emails as spam and not spam and facial recognition on Facebook. These technologies exhibit some facets of human intelligence.

Where does that intelligence come from? That brings us to our next term -> Machine Learning.

Machine Learning

Learning, as defined by Wikipedia, is referred to as “acquiring information and finding a pattern between the outcome and the inputs from the set of examples given.” ML intends to enable artificial machines to learn by themselves using the provided data and make accurate predictions. Machine Learning is a subset of AI. More importantly, it is a method of training algorithms such that they can learn to make decisions. (Read – AI and ML. Are they one and the same?)

Machine learning algorithms can be classified as supervised and unsupervised depending on the type of problem being solved. In Supervised learning the machine is trained using data which is well labelled that is, some data is already tagged with the correct answer while in unsupervised learning the machine is trained using the information that is neither classified nor labelled and the algorithm is supposed to find a solution to it without guidance. Also, a term called semi-supervised learning exists in which the algorithm learns from a dataset that includes both supervised and unsupervised data.

Training in machine learning requires a lot of data to be fed to the machine which then allows the machine (models) to learn more about the processed information.

Deep Learning 

Deep Learning is an algorithmic approach for the early machine-learning crowd. Neural Networks from the base for Deep Neural Learning and is inspired by our understanding of the biology of the human brain. However, unlike a biological brain where any neuron unit can connect to any other neuron unit within a certain physical distance, these artificial neural networks (ANN) have discrete layers, connections, and directions of data propagation.

For a system designed to recognize a STOP sign, a neural Network model can come up with a “probability score”, which is a highly educated guess, based on the algorithm. In this example, the system might be 86% confident the image is a stop sign, 7% convinced it’s a speed limit sign, and 5% it’s a kite stuck in a tree, and so on.

A trained Neural Networks is one that has been analyzed on millions of samples until it is sampled so that it gets the answer right practically every time.

Deep Learning can automatically discover new features to be used for classification. Machine Learning, on the other hand, requires to be provided these features manually. Also, in contrast to Machine Learning, Deep Learning requires high-end machines and considerably significant amounts of training data to deliver accurate results.

Wrapping up, AI has a bright future, considering the development of deep learning. At the current pace, we can expect driverless vehicles, better recommender systems, and more in the forthcoming time. AI, ML, and Deep Learning (DL) are not very different from each other; but are not the same.