LU 5 | Additional Resources
- G 13 | KMF1014
- Dec 10, 2019
- 4 min read
Updated: Dec 16, 2019
Additional Resources 1
Artificial Intelligence (AI) is an area of computer science that emphazises the creation of intelligent machines that work and react like human. Some of activities computers with artificial intelligence are designed for include speech recognition and learning. While the rate of progress in AI has been patchy and unpredictable, there have been significant advances since the field’s inception sixty year ago. Once a mostly academic area of study, twenty-first century AI enables a constellation of mainstream technologies that are having a substantial impact on everyday lives. Computer vision and AI planning, for example, drive the video games that are now a bigger entertainment industry than Hollywood. Deep learning, a form of machine learning based on layered representations of variables referred to as neural networks, has made speech-understanding practical on our phones and in our kitchens, and its algorithms can be applied widely to an array of applications that rely on pattern recognition. Natural Language Processing (NLP) and knowledge representation and reasoning have enabled a machine to beat the Jeopardy Champion and are bringing new power to Web searches.
While impressive, these technologies are highly tailored to particular tasks. Each application typically requires years of specialized research and careful, unique construction. In similarly targeted applications, substantial increases in the future uses of AI technologies, including more slef-driving cars, healthcare diagnostics and targeted treatments, and physical assistance for elder care can be expected. AI and robotics will also be applied across the globe in industries strugging to attract younger workers, such as agriculture, food processing, fulfilment centers, and factories. They will facilitate delivery of online purchases through flying drones, self-driving trucks or robots that can get up the stairs to the front door.
Through a network of billions of neurons, the brain gives a human power that scientists find amazingly intricate when they try to duplicate them in the sequence and parallelism necessary for programming a computer. The human intellect can store much information and then find relations among seemingly disparate bits of information in order to arrive at conclusions that fit ever-changing circumstances. Computer scientists face the challenge of creating artificial intelligence that is adaptable to such variance.
Why is AI important? The capacity to benefit from, and adapt to, the challenge and opportunities presented by this cognitive revolution is arguably one of the greatest strategic issues facing and Australia at large. In addition to the threat of job losses the issues of mass data collection and privacy are also of concern. AI is arguably one of the most important issues that facing us in the future. AI offers many benefits and opportunities, as well as many challenges. We now need a prominent and informed public debate on AI.
Due to enormous advance in computing and wide availability of data the processing power of computers continues to develop exponentially, rather than linearly, which has profound implications for the pace of AI technology. The opportunities associated with AI include significantly enhanced economic productivity, improved public services, as well as scientific and technological breakthroughs in areas such as medicine, education, transport and finance. In addition to the potential loss of jobs and disruption to employment markets, concerns about accountability and privacy will intensify, decision-making raises important legal and ethical questions.
Contrary to the more fantastic predictions for AI in the popular press the Study Panel found no cause for concern that AI is an imminent threat to humankind. No machines with self-sustaining long-term goals and intent have been developed nor are they likely to be developed in the near future. This is because technology is now developing exponentially. Unlike linear growth, which results from repeatedly adding a constant, exponential growth is the repeated multiplication of a constant.
Additional Resources 2
This video on “What is Neural Network” delivers an entertaining and exciting introduction to the concepts of Neural Network. We will learn the different layers present in a Neural Network and understand how these layers process data. We will get an idea of the the different parameters used in a neural network such as weights, bias and activation functions. We will also understand how to train a neural network using forward propagation and then adjust to the errors in the network using backpropagation method. This video also covers a few popular Neural Network applications. Now, let us jump straight into learning what is a Neural Network.
A Neural Network is a series of algorithms that endeavors to regonize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural Networks can adapt to changing input so the network generates the best possible result without needing to redesign the output criteria. Most Neural Network are fully connected, which means each hidden unit and each output unit is connected to every unit in the layers either side. The connections between one unit and another are represented by a number called a weight, which can be either positive or negative. The higher the weight, the more influence one unit has on another.
Information flows through a neural network in two ways. When it’s learning or operating normally, patterns of information are fed into the network via the input units, which trigger the layers of hidden units and these in turn arrive at the output units. This common design is called a “feedforward network”. Not all units “fire” all the time. Each unit receives inputs from the units to its left, and the input are multiplied by the weights of the connection they travel along. For a neural network to learn, there has to be an element of feedback involved such as children learn by being told what they’re doing right or wrong.in fact,we all use feedback all the time.
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