# AI News, Artificial Intelligence, Deep Learning, and Neural Networks Explained artificial intelligence

## Explaining a Black-box Using Deep Variational Information Bottleneck Approach

Over the past decade, artificial intelligence (AI) has achieved remarkable success in many fields such as healthcare, automotive, and marketing.

In May 2016, ProPublica claimed that one of the most widely used risk-assessment tools, COMPAS, was biased against black defendants while being more generous to white defendants [link].

The need for interpretability is especially urgent in fields where black-box decisions can be life-changing and have significant consequences, such as disease diagnosis, criminal justice, and self-driving cars.

The information bottleneck principle (Tishby et al., 2000) provides an appealing information theoretic view for learning supervised models by defining what we mean by a &#8216;good&#8217;

The principle says that the optimal model transmits as much information as possible from its inputto its outputthrough a compressed representation called the information bottleneck.

We introduce the variational information bottleneck for interpretation (VIBI),a system-agnostic information bottleneck model that provides a brief but comprehensive explanation for every single decision made by a black-box.

Using the information bottleneck principle, VIBI learns an explainer that favors brief explanations while enforcing that the explanations alone suffice for an accurate approximation to the black-box.

Cognitive chunk is defined as a group of raw features that work as a unit to be explained and whose identity is recognizable to a human, such as a word, phrase, sentence or a group of pixels.

\beta~\mathrm{I} ( \mathbf{x}, \mathbf{t} )$$where $$\mathrm{I} ( \mathbf{t}, \mathbf{y} )$$ represents the sufficiency of information retained for explaining the black-box output $$\mathbf{y}$$, $$-\mathrm{I} ( \mathbf{x}, \mathbf{t} )$$ represents the briefness of the explanation $$\mathbf{t}$$, and $$\beta$$ is a Lagrange multiplier representing a trade-off between the two. Variational ApproximationtoInformationBottleneckObjective The mutual informations$$\mathrm{I} ( \mathbf{t}, \mathbf{y} )$$and$$\mathrm{I} ( \mathbf{x}, \mathbf{t} )$$are computationally expensive to quantify (Tishby et al., 2000; In order to reduce the computational burden, we use a variational approximation to our information bottleneck objective:$$\mathrm{I} ( \mathbf{t}, \mathbf{y} )~-~\beta~\mathrm{I} ( \mathbf{x}, \mathbf{t} ) \geq \mathbb{E}_{\mathbf{y} \sim p(\mathbf{x})} \mathbb{E}_{\mathbf{y} |

The keywords such as “waste,” and “horrible,” are selected for the negative-predicted movie review, while keywords such as “most fascinating,” explain the model’s positive-predicted movie review.

VIBI also provides instance-specific key patches containing $$4 \times 4$$ pixels to explain a CNN digit recognition model using the MNIST image dataset.

In the first example, the CNN characterizes ‘1’s by straightly aligned patches along with the activated regions although ‘1’s in the left and right panels are written at different angles.

The last two examples show that the CNN catches a difference of ‘7’s from ‘1’s by patches located on the activated horizontal line on ‘7’ (see the cyan circle) and recognizes ‘8’s by two patches on the top of the digits and another two patches at the bottom circle.

We asked humans to directly score the explanation on a 0 to 5 scale (0 for no explanation, 1-4 for insufficient or redundant explanation and 5 for concise explanation).

## Having Been Realized Within the Body of Near East University, the Course on Artificial Intelligence and Its Apps Attracted Intensive Attention&#8230;

The basics of deep learning, cross entropy and loss, activation functions, optimization of weights and prejudices with back propagation and gradient descent, how to create (deep) neural networks with Keras and TensorFlow, how to record and upload models and model weights, how to make prediction on test data were discussed via practical applications.

By using machine learning, the latest developments in the fifth generation (5G) wireless technology, the use of 5G as the next generation mobile internet connectivity in internet operating system (IOS), and the crucial role of the Internet of Things (IoT) in ensuring significant advancement in educational sciences such as medicine were addressed within the frame of the course covering advancements in technology.

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