Machines have been an integral part of our lives for years now. And now with machine learning and AI, it has become possible for machines to work independent of human intervention. Progress in this space now allows machines to carry out tasks as diligently and intelligently as humans do, with high accuracy, if not completely perfect. We are quite literally trying to build a brain for machines, and that can be done only if machines are taught to learn the way we do. No wonder machine learning has found such a huge following in the world today, both by its creators and the users.
As with any domain, there is always a tussle and debate around the right way to build a particular software and the programming language which is the best fit for it. The domain of machine learning is not an exception either. And while the tide keeps shifting every once in a while, major support for Python has stayed in place consistently through the years.
Here’s why aMachine learning with Python course is absolutely essential for you if you’re looking to make your mark in the machine learning space and get a job that helps you make it happen.
1.Reusability of code and ease of configuration
Machine learning, in itself, is quite a complex challenge to begin with. Understanding the logic, recognizing patterns, and developing code that can help keep it all together is even more difficult than it might seem at the outset. The last thing any programmer would want is a language that makes things even more complex than they need to be.
Python comes with a good collection of libraries that serve a major purpose of code reusability. Not only are these libraries filled with instructions of how to use them in the best way, but simple tweaks in their modules can help them to be reused for different functionalities within the same model. Configuring them is as easy as breaking them down, testing them, making changes, and rebuilding them, all within a single space. Compare this with other languages and many programmers struggle with challenges like multiple structures, syntax differences, and so on.
2.Identifying and correcting errors quickly
We all know that machine learning is not going to be a simple task. There will be multiple iterations of building the algorithm, carrying out tests, and verifying results. What could be detrimental in this aspect is if there’s no way to figure out why the results are incorrect and trace them back to the code in order to understand the area responsible for the failure. Even popular languages like R, that boast extensive application capabilities, don’t make this an easy task, requiring hours and hours spent in simply detecting a small error.
Contrast that with Python that is extremely handy with error reporting which is specific and right on point. A few clicks can take you right to the module that caused the error and allow you to get down to the root cause in a considerably shorter period of time.
3.Machine learning, and not machine solving
The goal of machine learning is to create logic and algorithms that help the machine to learn by itself. We are not looking for results on what the machine is doing and what output it brings out. That enters the realm of research in evaluating machine performance.
Python helps programmers focus on the core aspect of machine learning. It is highly reliant on real-time processing and analysis of data, which allows the machine to make changes in its behaviour. This aspect of helping a machine gain insight into its own working is deftly achieved by using Python than any other language.
4.Wider applications that support machine learning
Picking up Python as one of the primary programming languages to be proficient in has a lot of advantages that go beyond the world of machine learning, but ultimately feed right back into it. It helps you be more aware of the different logic principles that govern code functionality and make you a better programmer. This helps pick up other languages speedily and code them in an effective manner.
A lot of other resources are also essential in understanding the nuances of machine learning, and these may make use of programming components that are independent of the domain. However, most of these are also built using Python, so knowing the language beforehand can always help you understand them better.
5.A high demand for Python in major job markets
Currently, the United States is one of the biggest markets where machine learning developments are at an all-time high. A look at job portals, makes it evident that many companies are on the lookout for good Python programmers that understand the nitty-gritty of machine learning and can develop excellent solutions.
In the coming years, this trend is just going to flow over to markets all across the world. So even if you don’t have immediate plans of going to the US and applying for a job, preparing for the inevitable boom in the sector is always in your benefit.
Learning other languages like SAS or R is a good decision if you have set your mind to enter a specific retail space that makes use of them widely. However, a good Machine learning with Python training course is your best choice for staying in step with the changing times and being future ready.
Since the development of computers or machines, their competence to enforce various tasks went on growing epidemically. Humans have advanced the power of computer systems in terms of their disparate working domains, their proliferating speed, and decreasing size with respect to time.
An arm of Computer Science named Artificial Intelligence goes after creating computers or machines as brilliant as human beings.
As we go ahead with the entire career prospective of Artificial Intelligence, let’s understand briefly about what Artificial Intelligence is.
A Brief Overview of Artificial Intelligence
The father of Artificial Intelligence, John McCarthy said, “Artificial Intelligence is the science and engineering that makes intelligent machines, exceptionally intelligent computer programs”.
AI makes a computer, a computer-controlled robot, or a software think intelligently, the same way an intelligent human would think.
While capitalizing the competence of the computer systems, the eagerness of human, lead him to doubt, “Whether a machine thinks and behave as humans do?”
Hence, the advancement of AI started with the objective to create similar intelligence in machines that we humans could. Artificial Intelligence aims to create expert systems. These systems present intelligent behavior, learn, display, explain, and advice their users. It implements human intelligence in machines by creating systems that understand, think, learn, and act like humans.
Career Prospects of Artificial Intelligence
A big core of AI is in the advancement of computer functions associated with human intelligence, such as reasoning, learning, and problem-solving.
AI has been assertive in various fields such as −
Gaming − It plays an important role in critical games such as chess, poker, tic-tac-toe, etc., where a machine can think of a large number of possible positions based on probing knowledge.
Natural Language Processing − It helps in interacting with the computer that understands the natural language which humans speak.
Speech Recognition − Some intelligent systems are able to hear and comprehend the language in terms of sentences and their meanings while a human talks to it. They can handle different accents, slangs, background noises, change in human’s tone due to cough, etc.
Intelligent Robots − Robots perform tasks which are assigned by humans. They can sense physical data from the real- world such as light, heat, temperature, movement, sound, bump, and pressure. They have enough processors, various sensors and vast memory, to display intelligence. Also, they are able to learn from their mistakes and they can adapt to the new environment.
Domains which target Artificial Intelligence are as follows:
Medicine: includes identification of medical images, diagnosis, expert systems to aid GPs, monitors, and controls in ICU, designing of prosthetics, and drug designing.
Robotics: includes vision, motor control, learning, planning, linguistic communication, cooperative behavior.
Engineering: identification of fault diagnosis, intelligent control systems, intelligent manufacturing systems, intelligent design aids, integrated systems for sales, design, production, maintenance, expert configuration tools (e.g. ensuring sales staff don’t sell a system that won’t work.
Space: It controls space vehicles and autonomous robots too far from earth to be directly manipulated by humans on earth, because of transmission delays.
Marketing: AI is being used to develop more targeted, relevant, and timely marketing programmes to enhance customer attrition rates. Examples of typical jobs held by AI professionals include:
1.Software analysts and developers.
2.Computer Scientists and computer engineers.
4.Research scientists and engineering consultants.
5.Surgical technicians working with robotic tools.
6.Medical health professionals working with artificial limbs, prosthetics, hearing aids, and vision restoration devices.
7.Military and aviation electricians working with flight simulators, drones, and armaments.
Salary Scale: The average salary of an Artificial Intelligence Engineer is approximately $93,625 per year depending upon the domain you choose.
Advance information technologies and the onset of machines enhanced by Artificial I
Intelligence (AI) have already influenced the world of work in the 21st century. Computers, algorithms, and software cut down everyday tasks, and it is absurd to imagine how most of our life could be managed without them. Nonetheless, is it also futile to imagine how most process steps could be managed without human effort. If they are then Artificial Intelligence will be an assured short movement in IT industry?
Artificial intelligence, machine learning and deep learning are three terms you’re going to hear a lot in the coming years. The artificial intelligence market is gaining ground at an explosive annual compound growth rate of 57.2 percent and will be worth $35.87 billion by 2025. Meanwhile, the $1.41 billion machine learning market is growing at an impressive CAGR of 44.1 percent and will be worth $8.81 billion by 2022. At the same time, the deep learning market is increasing at a CAGR of 65.3 percent and will be worth $1.7229 billion by 2022.
As these high CAGR rates indicate, all three of these markets are growing simultaneously, a reflection of their close relationship. But at the same time, the differing values for the size of these markets illustrate that they are not interchangeable, with AI representing the largest and most general of the three categories and deep learning representing the smallest and most specialized. Here’s a closer look at what these three technologies are, how they relate to each other and how they differ.
Artificial Intelligence: Automating Routine Tasks
Artificial intelligence is the broadest of the three terms, including machine learning and deep learning as subsets. AI is a branch of computer science that uses computers to copy and automate human cognitive tasks such as logical deduction, mathematical computation, language processing and sensory perception.
An ultimate goal of artificial intelligence research is general AI, which envisions an artificial brain that could replicate all human cognitive functions, like androids in movies. But most AI research focuses on specific applications of AI. A familiar example of AI is intelligent personal assistants such as Apple’s Siri or Amazon’s Alexa. Other examples include computers that play chess, spam filters and self-driving cars and drones.
Machine Learning: Making AI Flexible
Machine learning is a specialized application of artificial intelligence that enables computer programs to behave more flexibly than traditional AI. Traditional AI instructs programs to run set routines following predetermined rules that yield predictable outcomes. In contrast, machine learning uses probability to analyze patterns in data, testing a range of mathematical models that fit the same set of data until settling on a pattern than represents a best approximation. By “learning” from data in this way, machine learning can yield a more flexible range of outcomes than traditional AI.
Machine learning is typically used to analyze trends in data, make future predictions based on those trends, or suggest decisions based on how probable trends match up against a desired outcome. Qualcomm’s Artificial Intelligence platform uses machine learning for applications such as comparing scans of your fingerprints or face with those of a user trying to access your mobile device in order to verify that it’s really you. It also can interpret the audio in your surrounding area to detect and eliminate background noise and predict voice patterns.
Another common application of machine learning is when Netflix makes movie recommendations based on your previous viewing. Machine learning can also be used to adjust website options to user preferences, prevent credit card fraud or teach robots how to navigate.
Deep Learning: Using Data to Discover Patterns
Deep learning is an even more specialized application of AI and machine learning that uses artificial neural networks to find patterns in big data. Modeled on the way the human nervous system accepts and processes sensory input, neural networks process digital input through a series of layers before generating output. In each layer, a digital signal is assigned a logical or mathematical value that measures how closely the input corresponds to a desired outcome, such as how closely an image matches a target image. Each layer transforms the initial signal and passes it on for further assessment by another layer until a final output is generated. This multiple layering provides the “deep” aspect of deep learning.
Deep learning is extremely useful for pattern matching. A pioneering application of deep learning was Google teaching a neural network to identify images of cats by browsing YouTube videos. Other deep learning applications include computer vision, speech recognition and natural language processing.
Artificial intelligence, machine learning and deep learning are closely related but distinct technologies. AI, the most general of the three, automates routine tasks, while machine learning opens up more flexible probability-based analysis, predictive modeling and decision-making; and deep learning applies artificial neural networks to machine learning in order to train computers to find patterns in data. All three technologies are poised for explosive growth over the next five years, and they will become increasingly common in the home, at work and where we shop.
From self-driving cars to virtual assistants, Artificial Intelligence has impacted our lives in every possible way and this technology is becoming invincible day by day. According to a study conducted by Oxford University, AI may replace up to 45% of the human jobs in next 20 years. Chat bots have already replaced their human counterparts in Customer service and intelligence systems may soon eliminate low-level manual jobs that involve repetitive tasks.
As computational intelligence keeps improving and gets faster due to enhanced processor speeds, AI is getting better and is also able to defeat humans in various tasks. Google’s DeepMind labs developed an AI application named AlphaGo which has already defeated one of the best human players Lee Sedol in the ancient Chinese game of Go and has also recently defeated the current world champion Ke Jie. Another AI named Libratus that was developed by the scientists at Carnegie Mellon University was able to defeat the world’s best poker players in a 3-week tournament of Texas Holdem’ Poker. This is a milestone achievement in the field of Artificial Intelligence since poker is a game of incomplete information and not just a normal repetitive task.
Autonomous cars are being used for ride-hailing services in Singapore by a start-up named nuTonomy and various companies are exploring this area. AI is also revolutionizing healthcare and deep learning algorithms are used in the analysis of complex medical data. Artificial Intelligence is already used for diagnosis of X-rays and CT scans to detect early stages of lung cancer by a Chinese start-up named Infervision.
AI based health care assistants use natural language processing and machine learning algorithms to analyse the symptoms of the patients in diagnosing their diseases and suggesting appropriate medications. For example, Your.MD is an AI-powered mobile App which can suggest remedies for your ailments and will caution you when you should consult the doctor. Similarly, IBM’s Watson is an AI whose oncology platform can detect cancer symptoms and offer dynamic care by guiding patients on the right treatment path. There will lot more AI applications developed in the future which will redefine the healthcare industry altogether and help in faster treatment and recovery of complex diseases.
Elon Musk, the CEO of Tesla and SpaceX has launched a start-up named NeuraLink which will be developing devices that can be implanted in human brains to act as a brain-computer interface. As we know, Tesla and SpaceX are companies which are already working on innovative ideas and this new venture of Elon Musk is a completely mind-blowing concept which can make humans smarter than computers. Musk while addressing a gathering at Dubai told that, “Over time I think we will probably see a closer merger of biological intelligence and digital intelligence.”
Electrode arrays and several implants have been already used in medical industry to improve the effects of neurodegenerative disorders like epilepsy and Parkinson’s disease. Musk’s idea of NeuraLink is developing a brain computer interface by using a network of tiny electrodes that can be implanted in the human brain. Musk believes this technology will help humans to communicate with others wirelessly without using any spoken or written language. If this technology is realized, and it can alter our lifestyle completely as human beings will be able to communicate more effectively and swiftly. This technology will create a biological connection to the Internet and human mind and this is something which we have seen only in the science fiction movies so far.
AI has also disrupted the design industry with many start-ups coming up with machine learning powered branding platforms like Tailor Brands. Even website creation is automated using machine learning algorithms and start-ups like The Grid and Wix have already launched AI-powered website builders that can design websites for you with very little manual effort. Smart chat bots are already being used for servicing customers online by various businesses. With new applications getting developed almost every day, AI has become ubiquitous and plays an inevitable role in altering human life.
Google’s artificial intelligence (AI) DeepMind AlphaGo program beat the world Go champion, South Korean Lee Se-dol, in the first of a series of games in Seoul.
Last year, AlphaGo beat the European Go champion, an achievement that was not expected for years.
A computer has beaten the world chess champion, but the Chinese game Go is seen as significantly more complex.
Throughout most of the game Lee Se-dol seemed to have the upper hand but in the last 20 minutes, AlphaGo took an unassailable lead.
After that, Lee Se-dol forfeited, handing victory to his opponent.
The two sides will play a total of five games over the next five days for a prize of about $1 million.
The five-day battle is being seen as a major test of what scientists and engineers have achieved in the sphere of artificial intelligence.
Go is a 3,000-year old Chinese board game and is considered to be a lot more complex than chess where AI scored its most famous victory to date when IBM’s Deep Blue beat grandmaster Gary Kasparov in 1997.
However, experts say Go presents an entirely different challenge because of the game’s incomputable number of move options which means that the computer must be capable of human-like “intuition” to prevail.
Go is thought to date back to ancient China, several thousand years ago.
Using black-and-white stones on a grid, players gain the upper hand by surrounding their opponents pieces with their own.
The rules are simpler than those of chess, but a player typically has a choice of 200 moves compared with about 20 in chess.
There are more possible positions in Go than atoms in the universe, according to DeepMind’s team.
It can be very difficult to determine who is winning, and many of the top human players rely on instinct.
Google’s AlphaGo was developed by British computer company DeepMind which was bought by Google in 2014.
Mark Zuckerberg has announced he is planning to build artificial intelligence (AI) to help him around the house and with his work.
In a Fecebook post, the social media site founder said his personal challenge in 2016 would be to build a “simple AI” similar to the butler Jarvis from Iron Man.
Mark Zuckerberg says he plans to share his progress over the course of the year.
In December 2015, he made headlines for plans to give away 99% of his Facebook stake.
Mark Zuckerberg had to defend his philanthropic venture – launched to celebrate the birth of his daughter Maxima Chan Zuckerberg – after critics argued that it could provide a way for the founder to avoid paying tax on the sale of his shares.
On January 4, Mark Zuckerberg said he would start to build the AI with technology that is already out there and teach it to understand his voice to control everything in his home from music and lights to temperature.
“This should be a fun intellectual challenge to code this for myself,” he said.
“I’ll teach it to let friends in by looking at their faces when they ring the doorbell,” Mark Zuckerberg added.
“I’ll teach it to let me know if anything is going on in Max’s room that I need to check on when I’m not with her.”
For Facebook, Mark Zuckerberg added that the system would help him visualize data in virtual reality and help him build better services, as well as lead his company.
His announcement comes as Facebook is in the midst of AI initiatives such as building an assistant through its Messenger app for users.
The Facebook founder said a part of the motivation behind 2016 challenge was the reward of building things yourself.
Mark Zuckerberg’s previous personal challenges have included learning Mandarin, reading two books a month and meeting a new person every day.
According to CNN, actress Susan Bennett says her voice was used for Apple’s virtual assistant Siri.
“I wasn’t sure that I wanted the notoriety,” Susan Bennett told CNN, explaining her delay in coming forward, “and I also wasn’t sure where I stood legally.”
Susan Bennett says the Siri voice was recorded in 2005 at GM Voices on behalf of ScanSoft, a software company that was working on an undisclosed project.
Apple did not confirm Susan Bennett’s story, but an audio-forensics expert says he is “100 percent certain” she is the voice, and Bennett’s lawyer, who cannot confirm the details of confidential contracts, notes he’s had “substantial negotiations” with “parties along the economic food chain” regarding hiring Bennett as the voice of Siri.
Susan C. Bennett is one of the busiest and most versatile voice-over artists/vocalists working today.
Susan Bennett says her voice was used for Apple’s virtual assistant Siri
She is a graduate of Brown University; member of the band Laugh & Cry, affiliated with The Berklee School of music, and featuring renowned bassist, Abe Laboriel; backup vocalist on tour with Burt Bacharach and Roy Orbison.
Susan Bennett has an experience of more than 20 years doing voice-overs and singing both live and in studio for such clients as Ford, Coca-Cola, Fisher Price, McDonald’s, The Home Depot, Goodyear, VISA, Macy’s, Club Med, Delta Airlines, and The Cartoon Network.
She is a member of SAG-AFTRA, The American Federation of Musicians, and Women in Film.
Along with her husband, guitarist and composer Rick Hinkle, Susan Bennett is co-owner of Audiocam Music, a full service recording studio.
Scientists at the Massachusetts Institute of Technology (MIT) have unveiled M-Blocks, cube-shaped robots that can flip, jump and assemble themselves into different shapes.
The small robots M-Blocks have no external parts but can move using an internal flywheel mechanism.
They stick together using magnets.
The scientists envisage miniaturized “swarmbot” versions self-assembling like the “liquid steel” androids in the Terminator films.
M-Blocks, cube-shaped robots that can flip, jump and assemble themselves into different shapes
More realistically, the researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), believe armies of such cubes could be used for making temporary repairs to bridges or buildings, or as self-assembly, re-configurable scaffolding.
Modular robots have the advantage of being able to adapt to whatever task or terrain is presented to them.
John Romanishin, one of the research scientists at CSAIL leading the project, said: “We want hundreds of cubes, scattered randomly across the floor, to be able to identify each other, coalesce, and autonomously transform into a chair, or a ladder, or a desk, on demand.”
The M-Blocks are currently controlled by computer instructions sent over wireless radio, but in future the researchers hope algorithms can be loaded on the blocks directly, making them entirely autonomous and capable of adapting to different environments.
Blocks equipped with sensors and cameras would be able to work out how to accomplish specific tasks in combat or emergency situations, the scientists hope.
The vehicle of the future, the shrinking car that can be adjusted to fit into a small parking space, was shown at the world’s biggest IT fair on Tuesday, March 6, 2012.
About 4,200 exhibitors from 70 countries display the latest technology at the CeBIT, which runs until March 10 in Hanover, Germany.
The shrinking car is a two-seater “pod“, it has only 2.10 meters (seven feet) length and a cobalt-blue color.
Its top speed is 55 kilometers (35 miles) per hour and it has a range of 100 kilometers when its two batteries are fully charged. It creates additional energy from the turning of its wheels.
The vehicle can “dock” with other similar cars to create “road trains” of up to 20 cars, driven by just the person at the front. In that configuration, automatically all the cars share the energy available.
The car’s length can be reduced by 50 centimeters with a push of a button whenever is necessary. Its wheels can turn in a full circle, in this way the driver can pull up to a space and then move sideways into it.
Shrinking car concept has appeared over a decade ago. A complex prototype was unveiled at the CeBIT on March 6, 2012.
The futuristic automobile also has built-in sensors to avoid collisions, can drive itself and can be summoned by smartphone.
“If you are in the office, you can press a button on your smartphone and it will come and pick you up. We already have the technology to do this. It will be happening in five to six years,” said Timo Birnschein, the project leader, from the German Centre for Artificial Intelligence.
The team led by Timo Birnschein has been working on the shrinking car for fifteen months. They hope to make it roadworthy soon.
Shrinking car concept and prototype, Rinspeed Presto convertible, powered by 1.7-liter 120-horsepower Mercedes turbodiesel, has appeared almost a decade ago, but there were no plans for production.