AI News, 5 Important Artificial Intelligence Predictions (For 2019) Everyone ... artificial intelligence
What Is Artificial Intelligence? Examples and News in 2019
Coined in 1955 by John McCarthy as 'the science and engineering of making intelligent machines,' artificial intelligence (or AI) is software that is able to use and analyze data, algorithms and programming to perform actions, anticipate problems and learn to adapt to a variety of circumstances with and without supervision.
Neural networks (often called artificial neural networks, or ANN) essentially mimic biological neural networks by 'modeling and processing nonlinear relationships between inputs and outputs in parallel.'Machine learning generally uses statistics and data to help improve machine functions, while deep learning computes multi-layer neural networks for more advanced learning.
The original seven aspects of AI, named by McCarthy and others at the Dartmouth Conference in 1955, include automatic computers, programming AI to use language, hypothetical neuron nets to be used to form concepts, measuring problem complexity, self-improvement, abstractions, and randomness and creativity.
And while AI is generally a blanket term for these different kinds of functions, there are several different kinds of AI that are programmed for different purposes - including weak and strong AI, specialized and general AI, and other software.
Weak AI is designed to be supervised programming that is a simulation of human thought and interaction - but is ultimately a set of programmed responses or supervised interactions that are merely human-like.
Siri and Alexa are a good example of weak AI, because, while they seemingly interact and think like humans when asked questions or to perform tasks, their responses are programmed and they are ultimately assessing which response is appropriate from their bank of responses.
For this reason, weak AI like Siri or Alexa don't necessarily understand the true meaning of their commands, merely that they comprehend key words or commands and their algorithms match them up with an action.
However, on a basic level, unsupervised learning goes into problems without any pre-programmed answers, and is able to use a mixture of logic and trial and error to learn the answers or categorize things.
In general, much of the cutting-edge, boundary-pushing AI developments of recent years have been general AI - which are focused on learning and using unsupervised programming to solve problems for a variety of tasks and circumstances.
AI has been used in business for various purposes including process automation (by transferring email and call data into record systems, helping resolve billing issues and updating records), cognitive insight (for predicting a buyer's preferences on sites, personalizing advertising and protecting against fraud) and cognitive engagement (used primarily in a customer service capacity to provide 24/7 service and even answers to employee questions regarding internal operations).
For the 2016 year, the global chatbot market was reportedly worth $190.8 million - and could potentially comprise about 25% of customer service interactions by 2020, according to Gartner (IT) .
As far back as the mid 1600s, French scientist and philosopher Rene Descartes hypothesized about two divisions - machines that could one day think and learna specific task, and those that could adapt to perform a variety of different tasks as humans do.
Although ELIZA didn't actually speak (and communicated via text instead), the technology was the first that was developed to relay messages in language (or natural language processing) as opposed to using computer code and programming.
AImoved from a largely cutting-edge technological development to useful applications in business by 1980, when Digital Equipment Corporation's XCONwas able to save the company around $40 million in 1986 through its learning system.
And just six years later, IBM's cognitive computing engine Watson beat Jeopardy's champion, winning the $1 million prize money - further indicating AI's capabilities in successfully navigating language-based problems.
The research paper Building High-Level Features Using Large Scale Unsupervised Learningwas published in 2012 by researchers at Google (GOOG) and Stanford, and explained advances in unsupervised learning through deep neural networks that allowed AI to learn to recognize different pictures of cats without labeling the pictures.
'There are plenty of great things you can do with AI that save lives, including in a military context, but to openly declare the goal is to develop autonomous weapons and have a partner like this sparks huge concern,' Toby Walsh,professor atUniversity of New South Walesand organizer of the boycott, told The Guardian earlier this year.
It just opens up Pandora's box of psychology and science,' McMullen told Forbes earlier this year.'It's been evident that when you are using a very lifelike robot as a conduit for the AI and for the conversation, people tend to talk to that in a different way than they would, say, something on a computer screen.
We're living in a culture where we have a surplus of human beings, we don't have any problems with the amount of human beings that we have in the world, but we're creating this culture and this climate where we're trying to encourage people to form relationships with commercial goods, basically,' Richardsontold Forbes in September.
The principle concern seems to revolve around how exports of AI may boost the industries in other countries like China, potentially to the detriment of the U.S. 'The number of cases where exports can be sufficiently controlled are very, very, very small, and the chance of making an error is quite large,' Jack Clark, head of policy at OpenAI, told The New York Times.
5-Step Guide You Should Follow To Start A Career In Artificial Intelligence In 2019
As artificial intelligence (AI) continues to invade different verticals, it is getting obvious that the future of tech is already here.
AI is a sound career choice for a while now and as the adoption of AI in various verticals continues to grow, the demand for trained professionals to do the jobs created by this growth is also skyrocketing.
Even though many AI pundits have prophesied that this technology will wipe out a massive amount of human jobs, there other pundits too who have said this will offers many unique and viable career opportunities.
Before you dive in AI, build up a base in these areas: One step ahead: If you’re already a software engineer, it becomes a bit easy for you to enter the AI industry compared to those who are just getting started.
Whether you are a newbie or programmers, or someone with some relevant experience, apart from different skill sets required by different industries working in AI one should also possess great communication skills.
Learn with Google AI This platform by Google provides a course that starts from a basic introduction to machine learning to getting started with TensorFlow, to designing and training neural nets.
it is designed in such a way that whether you’re just starting with coding or you’re a seasoned machine learning practitioner, you’ll find information and exercises to help you develop your skills and advance your projects.
Aiming at the people who are looking to put machine learning, neural network technology to work as data analysts, data scientists or machine learning engineers.
However, the way AI is evolving, it seems the innovations in the coming years are going to be marvellous and those innovations will be successful only when there are people who are trained and working in the field of AI.
If you have a dream of working with amazing technology, then it is high time that you should start paving your path towards a career in artificial intelligence.
Post: Predictions Scorecard, 2019 January 01
On January 1st, 2018, I made predictions (here) about self driving cars, Artificial Intelligence and machine learning, and about progress in the space industry.
I have changed the header of the third column in each case to “2018 Comments”, but left the comments exactly as they were, and added a fourth column titled “Updates”.
I have started highlighting the dates in column two where the time they refer to has arrived, and I am starting to put comments in the updates fourth column.
will tag each comment in the fourth column with a cyan colored date tag in the form yyyymmdd such as 20190603 for June 3rd, 2019.
in each case, back on January 1st of 2018, have the following forms: NIML meaning “Not In My Lifetime, i.e., not until beyond December 31st, 2049, the last day of the first half of the 21st century.
With regards to academic rumblings about deep learning, in 2017 there was a new cottage industry in attacking deep learning by constructing fake images for which a deep learning network gave high scores for ridiculous interpretations.
But other clues, like the size of the person standing in front of it immediately get us to understand that it is a school bus on its side across the road, and we are looking at its roof.
In all their images a human can easily see that an object (e.g., an elephant, say, and hence the very clever title of the paper, “The Elephant in the Room”) has been pasted on to a real scene, and both understand the real scene and identify the object pasted on.
Other academics took to more popular press outlets to express their concerns that the press was overhyping deep learning, and showing what the limits are in reality.
As for stories in the technical press there were many that sounded warning alarms about how deep learning was not necessarily going to the greatest most important technical breakthrough in the history of mankind.
A national security newsletter quotes a Nobel prizewinner on AI: Intuition, insight, and learning are no longer exclusive possessions of human beings: any large high-speed computer can be programed to exhibit them also.
Windows by the numbers: Windows 10 finally dethrones Windows 7
Nearly three and a half years after its release, Windows 10 last month surpassed its enterprise predecessor, Windows 7, as the most popular operating system on the planet.
The first number - the projected user share for Windows 7 at its retirement - fell from the month-ago forecast (which pegged it at a record 40%) because the 2009 OS returned to a large decline in December.
Microsoft will offer a temporary solution to businesses running Windows 7 Professional or Windows 7 Enterprise - the post-retirement Windows 7 Extended Security Updates (ESU) announced in September 2018 - for an escalating cost each year for up to three years.
Even with Microsoft's free Windows 10 upgrade offer for the first 12 months after launch, the operating system was never able to keep up with the adoption pace set by Windows 7 six years earlier.
In the first three complete calendar years after Windows 7's debut - 2010, 2011 and 2012 - the OS added 16, 15 and 8 percentage points to its total, respectively, for a total of 39 points of user share.
And compared to Windows 7, Windows 10 was unable to sustain high volumes of growth: Windows 10's slowed dramatically in the second year and its third year also fell short of Windows 7's.
Bottom line: Net Applications' data points to a mad dash to rid systems of Windows 7 just before, at and after the support deadline, one even more frantic than took place in the months before and after Windows XP's end of support.
- On 3. marts 2021
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