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Artificial Intelligence and Machine Learning

Artificial Intelligence is creating more impact than the invention of the personal computer and the spread of mobile phones. The most promising approach is Machine Learning. Rather than trying to embody machines with everything they need to know upfront, we want to allow them to learn. So they can learn from their observations of the world.


In 2011, TV game-show Jeopardy had two male contestants playing against a third, an Artificial Intelligence type computer system IBM created named Watson. The two former Jeopardy champions, Ken Jennings and Brad Rutter, went head to hard-drive with the IBM supercomputer. Both were beaten by Watson. The win (over three days) showcased IBM’s expertise in advanced science and computing.

At the end of 2016, Uber users in San Francisco hailed a self-driving car to pick them up and take them to their destination. The cars were fitted with sensors and cameras, and through Machine Learning, were taught to steer, accelerate, brake and change lanes.

In the last few years, Artificial Intelligence and Machine Learning technologies have moved us forward in technology stakes faster than we have ever imagined. Artificial Intelligence is creating more impact than the invention of the personal computer and the spread of mobile phones. The most promising approach is Machine Learning. Rather than trying to embody machines with everything they need to know upfront, we want to allow them to learn. So they can learn from their observations of the world.


Everything in the world is now powered by a machine. Through the advancement of technology, engineers today can develop a machine that can learn on its own. This is Machine Learning. It seeks to create predictive models and algorithms. Giving computers the ability to carry out tasks without being explicitly programmed. Examples of Machine Learning we use on a day-to-day basis are Google search engines, recommendations from Amazon, Netflix and YouTube, and even suggested friends on Facebook. Another way Machine Learning has enmeshed in our lives is through face recognition. At the airport, you are staring at the camera ahead, feet planted exactly as marked, waiting for the gates to open.

Engineers are able to implement Machine Learning with Artificial Intelligence, which consists of systems that enable computers to perform intelligent human tasks without being explicitly programmed. Examples of day-to-day common uses of Artificial Intelligence include Apple’s Siri, computer games, fraud detection on credit cards, online customer support using Chatbots, and security surveillance.

Since computer hardware and software are rapidly advancing, Artificial Intelligence surpasses the capabilities of human experts, such as the win at Jeopardy or the Japanese robot that beat humans at market forecasting. There is great potential in the use of this technology, such as fewer errors in medical practice or fewer road accidents. Artificial Intelligence has features such as faster speed, integration of cameras, and precise speed recognition that allows machines to perform some tasks better than humans.


The underlying technologies of Machine Learning and Artificial Intelligence, are very complex. Machine Learning has three parts – The Model, The Parameters and The Learner.

The Model is a system that makes the predictions and identifications. The Parameters are the signals and factors the model uses to make its decisions, and the learner is the system that adjusts the parameters by looking at the differences, the predictions and the actual outcomes.


Artificial Intelligence is more complex. Each program depends greatly on the purpose of the product. They all have three components, Data Structure, Inputs and Outputs, and Learning System.

There are two types of Data Structures needed, one for long-term storage and the other for short term storage. Inputs and Outputs are the core sources of data. Some examples of Inputs are sensors and downloaded data. An associate of Learning System is the most crucial component of the Artificial Intelligence system. This presents us with a Learning System of the machine and tests boundaries. It gives the ability to perceive and learn new information and allows for cooperation and social intelligence upon human interaction.


Although Machine Learning and Artificial Intelligence are useful technologies, they could pose certain ethical dilemmas. By 2034, one analysis concluded that 47 percent of all jobs in the United States could become automated, which means robots could take over human employment. This could be appealing to companies since robots do not require salaries. However, some in the tech world think Artificial Intelligence could allow people to enjoy their lives and use the intelligence as an enabler to accomplish more. To eliminate tedious and repetitive tasks and allow more time on creative and other fun endeavours, but still, have an income from other pursuits.

Many in the world of Artificial Intelligence and Machine Learning are wondering what happens if the machine fails? Who would be at fault, the programmers or the end-users? Since machines do not have advanced social intelligence (yet), how would they make complex and moral decisions?

As Max Tegmark, the President of the Future of Life Institute had said, “Everything we love about civilisation is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilisation flourish like never before – as long as we manage to keep the technology beneficial.”


Conversational Systems are the voice interfaces many science fiction writers and technologists had only dreamed of. However, thanks to the advances in Artificial Intelligence and Machine Learning, voice-operated conversational systems have become more practical. There are many platforms, such as Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa and Amazon’s Echo. These voice interfaces have the ability to translate voice into search commands. They also have the skills to manage song playlists, shopping lists and to look up information quickly. The future of these conversational systems is to control appliances, which is happening already with some connected whitegoods. In business, these conversational voice interfaces can simplify business practices, where it can enable users and systems to have interactions that are meaningful. Companies are always finding new and innovating ways to increase the brand-to-consumer communication. There are new touch points with consumers that are relevant, highly personal and conversational. Powered by a combination of Machine Learning, natural language processing, and live operators, retailers and some tech firms are extending on the conversational systems with chatbots. They are to provide customer service, sales support and other commerce-related functions. With the popularity of mobile messaging, voice-operated conversational systems and the advances in Artificial Intelligence and Machine Learning, the new generation of tools can enable companies/brands to communicate with customers faster, better and cheaper.

About ASI Solutions

ASI Solutions has been a provider of innovative and pioneering business technology solutions to Australian private and public sector organisations for over 30 years.  We choose innovative technology solutions which are matched to each client¹s unique business needs, taking a solutions oriented approach and working to deliver a clear return on investment.

Our global technology offerings and professional implementation model provides greater efficiency and returns for all customers. We make this happen with real insight into the external forces impacting IT environments, and we balance the needs of business to help our customers’ transition to the operating challenges of tomorrow.

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