Artificial intelligence (AI) can be defined as a series of software technologies that work differently from
the way that a normal human being’s nervous system is used for the actions of sensing, learning, reasoning
and for taking action. The development of AI has been inconsistent, but there has been considerable
progress in the last 60 years. For example, present day video games are programmed by using computer
vision and AI planning. Deep learning is a type of machine learning built on layered demonstration of
variables which are called neural networks. This is used in the speech recognition feature in modern
phones and in various kitchen and home appliances. Such algorithms have a wide variety of applications
which depend upon recognition of patterns.
These technologies are adapted to suit various tasks such as: –
1. Transportation: – Self-driving cars.
2. Home service robots: – Taking care of cleaning, security and delivery jobs.
3. Healthcare: – Assisting as medical staff and caring for patients and elderly.
4. Education: – Teaching kids and military personnel, helping children with learning disabilities.
5. Public safety and security: – Will support in law enforcement.
6. Low resource communities: – Machine learning can assist in dealing with health risks.
7. Employment and work place: – AI can create jobs and make services more affordable.
8. Entertainment: – AI will maintain the data about the customers in the media companies.
Deep learning has permitted numerous technological companies and industries to meet business and
consumer needs in the present-day society. Researchers have decided that using deep-learning models to
process vast amounts of data has benefitted artificial intelligence by amounts unimagined before.
Although deep learning is a very efficient way to enhance AI, it is a piece of technology that is not very
advanced. But experts and scholars like Mohak Shah thinks that “acceleration (in deep learning) is going to
come when we put all the pieces together.” However, Adam Coates, director of Baidu Research Silicon
Valley AI Lab, thinks that to advance deep learning “we need huge amounts of annotated data”. To acquire
this for a feature that uses deep learning, for example the speech recognition feature, he thinks “We don’t
just need the audio for the speech, we need a human to give us a transcription. That can be very expensive.”
But after all, this system of accelerated and more enhanced machine learning can be carried out
unsupervised which is a big advantage over normal machine learning. But there are of course limitations of
this new type of learning. Li Deng, chief AI officer, Citadel explores some off these limits. The “big problem”
according to her, is that “how to have an agent, an intelligent agent, who is able to converse with a human.
That is the kind of problem that not just big data can solve and not just the deep depth of the neural network
can solve.” This is a problem that cannot be solved by even the most cutting-edge technology, deep
learning. Another expert focuses on a way of learning invented upon the principles of deep learning, deep
reinforcement learning. Gary Bradski, chief technology officer, Arraiy, explains deep reinforcement
learning: – “If you’re doing a maze, you went left-right- left-right- left-right, found food. That’s when you get
your signal, “Hey, I got food.” You have to remember and propagate that back in time so that you know,
“Oh, I should take a left here and a right here.” So that’s what reinforcement learning is—it’s learning from
these end rewards, and being able to assign that signal, that learning signal, back through that trace in time.”
Some of the most important applications of quantum computing will be in the realm of complex logistics
and optimization problems-including everything from scheduling, to cybersecurity, supply chain
management and financial services. Quantum computers can simplify problems that are typically too
complex, or have too many variables, for classical computers to solve.
Quantum computers will help investment professionals and financial institutions optimize returns while
minimizing risk by analysing an unimaginable range of scenarios. Quantum computers could also help
businesses with massive supply chains to optimize their processes by more efficiently organizing logistical
variables. Enhanced processing power will also help cybersecurity companies detect threats earlier and
respond more quickly.
Quantum computers also have the ability to develop Artificial Intelligence (AI) and machine learning
systems with higher efficacy and efficiency than conventional computers. Optimized computing abilities
will drive exponential improvements in Artificial Intelligence and machine learning. D-Wave Systems has
already built quantum hardware for Google’s researchers studying machine learning powered image
recognition. This technology could dramatically improve the performance of many technologies, such as
Alan Turing was one of the greatest mathematicians and scientists in British history. He was one of the
fathers of computer science and he helped defeat Germany in World War II by breaking the German codes.
In 1936 Turing did some of the basic work on today’s computers. Machines were then becoming
increasingly important in British industrial life, such as those in mass production factories or calculators or
typewriters. But they were operating separately from each other. Turing said that it would be possible (in
today’s language) a system to “programme” machines so that one machine (a computer) could do a variety
of different tasks, such as write documents, calculate numbers or play games. This was the “Universal
Turing machine” because one machine could do a variety of tasks. He was describing a computer well
before the technology of his day could build one. He also wrote about artificial intelligence-the way that
computers could ‘think’. He was also faced with the question, “Would it be possible to create a computer
that could be as smart as humans?”. He predicted that such a machine could exist by the year 2000. His
‘Turing Test’ will assess when a machine has become as smart as a human. A human being is in one room
and communicates with a machine in another room, such as by playing chess or asking questions. If the
human cannot from the responses whether the other room contains a human or a machine, then the
machine is as smart as a human. However, Turing was too optimistic about the rate of computer progress.
The world’s best chess player is now a computer (“Big Blue” created by IBM) but in many other respects
humans are still smarter than computers. The ‘Matrix’ movies are based on the era when the computers
are much closer to Turing’s prediction of closing the gap.
Artificial Neural Networks (ANNs) were invented by keeping a real-life example in mind: – the biological
human neural network. ANNs are normally drawn on paper as separate systems, much like the human
biological neural network, which are interconnected as neurons and can send messages between each
system. Actually, ANNs contain layers of neurons- a neuron, in computational terms, is a computational
unit which calculates some piece of information based on weighted input parameters. The bonds between
each system have weights as the variable ‘W’ that changes according to the experience, creating in the
process, neural nets that are adapted to numerous inputs and can continue learning.
Each layer or system of neurons detects some additional information that forms the analysis of whole
picture, for example one layer of neurons detects edges of things in a picture (e.g. recognise tumours in a
brain scan). This technology will help many professions such as doctors, to analyse scans to recognise
problems at an early stage. There can be multiple layers of neurons to detect extra additional information
about the input parameters. Recurrent neural networks were invented based upon this technology and
they create a reverse input loop to assist in certain cases, for example in sentence recognition- because the
meaning of a word is dependent on the prior word or set of words. This helps the computational device to
analyse the situation the word or set of words is placed in and decide on its right meaning to use for the