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Tag: deep learning

10 Interesting Facts About Artificial Intelligence!

Posted on by Imarticus Learning

Artificial Intelligence has received a lot of focus and attention in the last couple of years. There has been a boom in the innovations that have artificial intelligence at its base. Obviously, the internet has played a crucial role in the development of artificial intelligence-enabled services.

Machine learning essentially an artificial intelligence technique, has been stirring new developments by creating new algorithms that mimic or support human behavior or decision-making capabilities, which are already in use, like Apple’s Siri, or the email servers which eliminate junk or spam emails. You can also see the use of machine learning in e-commerce websites that use it to personalize the search or use of the web experience of their customers.

It is interesting to comprehend the capabilities of machines. Very soon machines will have the capability to perform advanced cognitive functions, processing language, human emotions, the machines will be proficient in learning, planning, or performing a task as intelligent systems.

There is also a definite possibility that the tasks performed will be or can be more accurate than humans, thus artificial intelligence can boost productivity and accuracy, and impact economic growth. Imagine the impact it can have on medical procedures, the continued support it could lend to the disabled, increasing their life expectancy.

Artificial intelligence is a technology that can improve the world for the better, however, it also comes along with some challenges such as machine accountability, security, displacement of human workers, etc.


But right now before the possible alarming impact of artificial intelligence, we could in the today, the now, enjoy learning about some interesting facts.

 

Interesting Facts About Artificial Intelligence

  1. It is interesting to note that research on artificial intelligence is not only a few years ago, but the inception of AI also goes back to the 1950s. Alan Turning is coined as the father of AI, back in the day he invested a test based on natural language conversation with a machine.
  2. Did you know that a lot of video games that engage humans over time are based on a technique of artificial intelligence and is called Expert System? This technique is knowledge-based and can imitate areas of intelligent behavior, with a goal to mimic the human ability of senses, perception, and reasoning.
  3. Autonomous vehicles are no longer a thing of the far future. The knight rider might actually become a reality in as close as the next 2-3years or less. These cars are based on artificial intelligence to recognize the driving conditions and adapt the behavior. These cars are in the test phase, already developed and almost ready to hit the road.
  4. There is a race that is warming up between social media corporations over perfecting the use of artificial intelligence to enhance the customer experience. Facebook and Twitter are two companies essentially applying AI to match relevant content to the people. Leading this race is Google, coming across as one of the most preferred and reliable search engines.
  5. IBM has created a supercomputer based on AI, called Watson. One of the major challenges of creating Watson was the programming that needed to be done so that it could understand questions in most of the common languages and the ability to attend to those questions in real-time. The development is such that currently Watson is not only applied in various industries but was recently successful in teaching people how to cook.
  6. Sony created a robotic dog called Aibo, one of its first toys that could be bought and played with. It could express emotions and could also recognize its owner. This was the first of its kind, however, today you will find more expensive and evolved versions of the same.
  7. At the rate at which Artificial Intelligence is being adopted in various areas of our lives, it is predicted that it will replace 16% of our jobs over the next decade.
  1. Artificial Intelligence Training CoursesIt is a fact that with increased intelligence and ability to perform tasks with accuracy, over the next few years it is predicted that close to three million workers will be reporting to or will be supervised by “Robot-bosses”.

    With Machine learning and language recognition, it is no surprise that 85% of telephonic customer service jobs will be performed by computers and will not need human interaction.  By the dawn of 2020, it will be possible for all customer digital assistant to recognize people by face and voice.

Organizations and private sectors have recognized the opportunity that AI investments can have on the future of their businesses. Hence have set up major investments in the development of the same.

Finally, one must remember the anticipated impact of AI is on calculated assumptions and predictions.
However, one thing is clear, that AI in the future will impact the internet, its citizens, and economies.

Read More 
The Promises of Artificial Intelligence: Introduction

How to Work on Deep Learning programming?

Posted on by Imarticus Learning

Learning Algorithms

Algorithms are at work all around us. Right from suggestions displayed in a text box while using Whats App to time boxing traffic signals, algorithms greatly improve the quality of human life these days. The more efficient the algorithm, the better the quality of service. Imagine an elevator system for a skyscraper with a thousand floors.

An adaptive machine learning algorithm can change the way it works depending on the demand and timetable of people going to different floors and dramatically reduce the waiting time for a person taking the elevator when compared to a static algorithm with no feedback loop.

Machine Learning is nothing but the improvement in performing a task with experience.The more the experience, the better is the performance of a machine learning algorithm. It can also be used for predicting the outcome of an event based on the historical data available. Filtering spam from your mailbox, Commute time predictions, Suggestions in social media, digital assistants are a few examples of the applications of machine learning algorithms.

Deep Learning and the Complexities involved

The fundamental rule in computer science is the use of abstractions. All concepts act as building blocks to another seemingly advanced concept, which is nothing but a layer of abstraction added over the older concept.

Algorithms, data structures, machine learning, data mining are the building blocks of Deep learning which is Machine learning and the concept of feature wise classification. Deep learning defines which feature characterizes a pattern and then uses data mining to classify, compare and define a feature.

Deep learning algorithms typically take more time to train but are more accurate and dependable as experience increases. They are used for speech recognition. NLP. Computer vision, Weather pattern analysis etc. They are usually implemented using neural networks. Deep learning is a subset of machine learning.

How to Learn Deep Learning programming

Below are few ways to understand and work on Deep learning:

  1. There are several machine learning courses, and deep learning courses available online,mostly in Python and R. Python training is usually a prerequisite for these courses. Some of the best ones are available in Udemy, Course Era, edX etc. These courses can be completed online and are prepared by the best minds in the field.  

  2. Understanding the inbuilt Python libraries: The future of machine learning and deep learning depends greatly on the inbuilt library support python provides. Tensor Flow, Thea nos, Pandas etc. are a few powerful libraries which it provides for programmers to explore deep learning concepts.

  3. Knowledge of Machine Learning or doing a machine learning course is generally preferred before diving into deep learning because conceptually machine learning is a general form of learning compared to the more specific deep learning. But based on the programmers understanding of the basic concepts, exposure to Python and R libraries, deep learning can also be started directly.

  4. However, the classic order is, do a python course -> Do a machine learning course -> Do a deep learning course and then contribute to the deep learning community after practice and execution.

  5. All the tools involved are opensource, so with sufficient interest, programming expertise and Python knowledge, cracking Deep Learning should be an easy task. Take part in the community and practice, practice, and practice to excel.

All the very best for your journey into Deep Learning..!!

Top 5 Data Science Trends in 2018!

Posted on by Imarticus Learning

Data Science in today’s world is a combination of various functions – AI, Deep Learning (real and hyped progress), Quantum Computing, Big Data, IoT, and many more such applications which are used together as a network. 2017 was dominated by advances in the AI space which had taken over from Big Data. Data has become popular due to the open-source regime which is slowly chipping away at the market and technology shares of established names like Oracle and  Microsoft. With the ever-increasing popularity of newer and scalable programs, let us see the top trends to expect in 2018.

Also Read: How to Become A Data Scientist?

Regulation

The awaited impact-event will be GDPR (European General Data Protection Regulation) which will become enforceable on May 25, 2018. This regulation will affect data science practice in three areas – limits to be applied on data processing and consumer profiling, “automated decision making” and the right to an explanation for that, and feeding in biases and discrimination in automated decisions.

The measures under this act were approved by the European Parliament on April 27, 2016, and will go into effect on May 25, 2018. The law will focus on the new rules on the collection and management of personally identifiable information (PII) of EU citizens. Implementing these rules will bring broad changes in the big data modeling and in creating predictive models.

Artificial Intelligence

According to Garter’s list of Top 10 tech trends in Big Data, it is laying the foundation of AI across organizations. It will remain a major challenge and work plan to follow through till at least 2020 as significant investment in skills, processes and tools will be required to exploit these techniques.

Intelligent Apps

These will be created and used with an aim to enhance human activity and effort and mostly not replace it. Augmented analytics is a strategic growth area in which machine learning will be widely used to automate data preparation, insight discovery, and sharing for a large range of business users, operational workers, and citizen data scientists.

Virtual Representations of Real-World Objects or Systems

Digital representations of the real-life objects will be a common reality and their inter-linkages will help in checking the cause and effect changes for improving the operations and value. It is predicted that over time digital twins of every physical reality will be available and infused with AI capabilities to enable simulation, operation, and analysis. This will particularly help in fields of city planning, digital marketing, healthcare, and industrial planning.

Cloud to the Edge

Edge computing works to maintain the closeness of processing, content collection and delivery close to the source of information. This helps in reducing issues to latency, bandwidth, connectivity. Garter predicts that pairing this strategy with cloud computing will give the best of both the worlds to create a service-oriented model and a centralized model and coordination structure.

While many trends will take a long while to cultivate from its conceptual stage to a working philosophy, these trends will lead the way for future innovations.
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