AI, Neural Networks, Machine Learning and other buzzwords are not new; they are with us from late 50s, but why did they become so much of a trend only now? The business focus changed from investing into so-called "artificial intelligence" to development of systems that could work with already gathered data, process and re-structurize it. Bayes was widely used in anti-spam, Markov's chains predicted criminal structure behavior, search engines developed decision trees to predict user input, speech and image recognition was no miracle anymore, and it was good. Basically, we returned to 50s -- we are trying to create universal structures, mimic human brain, and create entities that can process mixed data as our brains do.
Earlier this year, IBM scientists collaborated with researchers at the University of Alberta and the IBM Alberta Centre for Advanced Studies (CAS) to publish new research regarding the use of AI and machine learning algorithms to predict instances of schizophrenia with a 74 percent accuracy. Using AI and machine learning, 'computational psychiatry' can be used to help clinicians more quickly assess – and therefore treat – patients with schizophrenia. In this schizophrenia research, we have learned that powerful technology can be used to predict the likelihood of a previously-unseen patient having schizophrenia. This kind of innovative collaboration is just one example of the work being done between IBM and the University of Alberta through the IBM Alberta Centre for Advanced Studies.
Use cases for this first kind of AI include autonomous cars, robots, chatbots, trading systems, facial recognition, and virtual assistants. To be clear, any apocalyptic scenario involving autonomous weapons systems would be initiated by humans. True intelligence moves past simple ideas like goal-seeking, which is often considered another cornerstone of varying levels of AI and as a potential control mechanism. The basic ideas of self-defense and self-preservation combined with a knowledge of human history seem to inevitably lead to a bad situation for humans.
This article discusses how the cognitive capabilities of deep learning could be applied to various audit procedures to enable audit automation and improve decision making. Although the idea of artificial neural networks dates back to the 1950s, such networks could not be called real artificial intelligence until recent advances in computational power and data storage enabled the development of deep neural networks that model the structure and thinking process of the brain. The hidden layers of a deep neural network automatically "learn" from massive amounts of data (especially semi-structured or unstructured data) received by the input layer (e.g., millions of images, years' worth of speeches, tera-bytes of text files), recognize data patterns in more and more abstract representations as the data is processed and transmitted from one hidden layer to the next, and classify the data into predefined categories in the output layer. While the challenges of big data analysis require a willingness to adopt more advanced data analytical technologies, such as deep learning, the availability of massive amounts of financial data facilitates the implementation and improvement of this technology in auditing.
Ford researchers developed and implemented, in mass-produced cars, an innovative misfire detection system--a neural-net-based classifier of crankshaft acceleration patterns for diagnosing engine misfire (undesirable combustion failure that has a negative impact on performance and emissions). In our supply chain, neural networks are the main drivers behind the inventory management system recommending specific vehicle configurations to dealers, and evolutionary computing algorithms (in conjunction with dynamic semantic network-based expert systems) are deployed in support of resource management in assembly plants. We can expect in the near future a wide range of novel deep-learning-based features and user experiences in our cars and trucks, innovative mobility solutions, and intelligent automation systems in our manufacturing plants. Building centers of excellence in AI and ML was not too challenging since, as I mentioned earlier, we had engineers and researchers with backgrounds and experience in conventional neural networks, fuzzy logic, expert systems, Markov decision processes, evolutionary computing, and other main areas of computational intelligence.
I had to learn about some basic concepts related to neural networks to be able to understand the basics at a conceptual level at first. After experimenting with different tuning parameters, high accuracy was achieved with a perceptron with more hidden layers and much more computational nodes than the first baby net. As for the activation and loss functions, my choices were ReLu and softmax activation for the output layer along with cross-entropy loss. For this, a mailing list classification task with data collected from Mailchimp.
In a previous post "Deep Learning and Artificial Intuition", I introduced the idea that there are two distinct cognitive mechanisms, one based on logical inference and another based on intuition. At least 6 decades have been spent exploring cognitive mechanisms based on logical inference without making much progress towards AGI. Kahneman's book explores human cognitive biases and employs the dual cognitive processes as a root cause of these biases. In fact, Kahneman's research points out that human cognitive biases exists because of flawed reasoning in our intutitive system 1 inference.
This will typically learn in 100 epochs fairly good recommendations for movies. Companies are starting to offer hardware that can be situated close to the data production (in terms of network speed) for machine learning. It is for this reason that companies are starting to offer hardware that can be situated close to the data production (in terms of network speed) for machine learning. To get an idea of its speed, a researcher loaded up the Imagenet 2012 dataset and trained a Resnet50 machine learning model on the dataset.
Understanding key technology requirements will help technologists, management, and data scientists tasked with realizing the benefits of machine learning make intelligent decisions in their choice of hardware platforms. Deep learning is a technical term that describes a particular configuration of an artificial neural network (ANN) architecture that has many'hidden' or computational layers between the input neurons where data is presented for training or inference, and the output neuron layer where the numerical results of the neural network architecture can be read. Each step in the training process simply applies a candidate set of model parameters (as determined by a black box optimization algorithm) to inference all the examples in the training data. The reason is that numerical optimization requires repeated iterations of candidate parameter sets while the training process converges to a solution.
AI functions wired into free or discounted Internet services work because the companies profit by selling user data; the Pentagon is probably not eligible for this discount. These leaders will provide "adoption capacity" for eventually fielding unilaterally developed defense systems that will form the core of the Third Offset. Though the military, most notably DARPA, has dabbled with AI in things like the cyber and self-driving car'grand challenges;' fielding a variety of functional technologic solutions will provide proven ground before attempting unilateral projects. Small, short timeline endeavors like Project Maven, recently created to use machine learning for wading through intelligence data, must provide the network integration experience needed for building larger programs of record.