Showing posts with label Milind Tambe. Show all posts
Showing posts with label Milind Tambe. Show all posts

AI - Milind Tambe


Milind Tambe (1965–) is a pioneer in artificial intelligence research for social good.

Public health, education, safety and security, housing, and environmental protection are some of the frequent areas where AI is being used to solve societal issues.

Tambe has developed software that preserves endangered species in game reserves, social network algorithms that promote healthy eating habits, and applications that track social ills and community difficulties and provide suggestions to help people feel better.

Tambe was up in India, where the robot novels of Isaac Asimov and the first Star Trek series (1966–1969) inspired him to study about artificial intelligence.

Carnegie Mellon University's School of Computer Science awarded him his PhD.

His first study focused on the creation of AI software for security.

After the 2006 Mumbai commuter train attacks, he got interested in the possibilities of artificial intelligence in this subject.

His doctoral research revealed important game theory insights into the nature of random encounters and collaboration.

Tambe's ARMOR program generates risk assessment scores by randomly scheduling human security patrols and police checkpoints.

Following random screening processes, Los Angeles Airport police uncovered a vehicle carrying five rifles, ten pistols, and a thousand rounds of ammunition in 2009.

Federal air marshals and port security patrols utilize more latest versions of the program to arrange their flights.

Today, Tambe's group uses deep learning algorithms to aid wildlife conservation agents in distinguishing between poachers and animals captured by infrared cameras on unmanned drone aircraft in real time.

Within three-tenths of a second of their arrival near animals, the Systematic Poacher Detector (SPOT) can identify poachers.

SPOT was tested in Zimbabwe and Malawi park reserves before being deployed in Botswana.

PAWS, a successor technology that predicts poacher activities, has been implemented in Cambodia and might be used in more than 50 nations across the globe in the future years.

Tambe's algorithms can simulate population migrations and epidemic illness propagation in order to improve the efficacy of public health campaigns.

Several nonobvious patterns have been discovered by the algorithm, which will help to enhance illness management.

Tambe's team created a third algorithm to assist drug misuse counselors in dividing addiction rehabilitation groups into smaller subgroups where healthy social ties may flourish.

Climate change, gang violence, HIV awareness, and counterterrorism are among the other AI-based answers.

Tambe is the Helen N. and Emmett H. Jones Professor of Engineering at the University of Southern California's Viterbi School of Engineering (USC).

He is the cofounder and codirector of USC's Center for Artificial Intelligence in Society, and he has received several awards, including the John McCarthy Award and the Daniel H. Wagner Prize for Excellence in Operations Research Practice.

Both the Association for the Advancement of Artificial Intelligence (AAAI) and the Association for Computing Machinery have named him a Fellow (ACM).

Tambe is the cofounder and director of research of Avata Intelligence, a company that sells artificial intelligence management software to help companies with data analysis and decision-making.

LAX, the US Coast Guard, the Transportation Security Administration, and the Federal Air Marshals Service all employ his methods.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Predictive Policing.

References And Further Reading

Paruchuri, Praveen, Jonathan P. Pearce, Milind Tambe, Fernando Ordonez, and Sarit Kraus. 2008. Keep the Adversary Guessing: Agent Security by Policy Randomization. Riga, Latvia: VDM Verlag Dr. Müller.

Tambe, Milind. 2012. Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned. Cambridge, UK: Cambridge University Press.

Tambe, Milind, and Eric Rice. 2018. Artificial Intelligence and Social Work. Cambridge, UK: Cambridge University Press.

Artificial Intelligence - What Is Deep Learning?


Deep learning is a subset of methods, tools, and techniques in artificial intelligence or machine learning.

Learning in this case involves the ability to derive meaningful information from various layers or representations of any given data set in order to complete tasks without human instruction.

Deep refers to the depth of a learning algorithm, which usually involves many layers.

Machine learning networks involving many layers are often considered to be deep, while those with only a few layers are considered shallow.

The recent rise of deep learning over the 2010s is largely due to computer hardware advances that permit the use of computationally expensive algorithms and allow storage of immense datasets.

Deep learning has produced exciting results in the fields of computer vision, natural language, and speech recognition.

Notable examples of its application can be found in personal assistants such as Apple’s Siri or Amazon Alexa and search, video, and product recommendations.

Deep learning has been used to beat human champions at popular games such as Go and Chess.

Artificial neural networks are the most common form of deep learning.

Neural networks extract information through multiple stacked layers commonly known as hidden layers.

These layers contain artificial neurons, which are connected independently via weights to neurons in other layers.

Neural networks often involve dense or fully connected layers, meaning that each neuron in any given layer will connect to every neuron of its preceding layer.

This allows the network to learn increasingly intricate details or be trained by the data passing through each subsequent layer.

Part of what separates deep learning from other forms of machine learning is its ability to work with unstructured data.

There are no pre-arranged labels or characteristics in unstructured data.

Deep learning algorithms can learn to link their own features with unstructured inputs using several stacked layers.

This is done by the hierarchical approach in which a deep multi-layered learning algorithm offers more detailed information with each successive layer, enabling it to break down a very complicated issue into a succession of lesser ones.

This enables the network to learn more complex information or to be taught by data provided via successive layers.

The following steps are used to train a network: Small batches of tagged data are sent over the network first.

The loss of the network is determined by comparing predictions to real labels.

Back propagation is used to compute and transmit any inconsistencies to the weights.

Weights are tweaked gradually in order to keep losses to a minimum throughout each round of predictions.

The method is repeated until the network achieves optimum loss reduction and high accuracy of accurate predictions.

Deep learning has an advantage over many machine learning approaches and shallow learning networks since it can self-optimize its layers.

Machine or shallow learning methods need human participation in the preparation of unstructured data for input, often known as feature engineering, since they only have a few layers at most.

This may be a lengthy procedure that takes much too much time to be profitable, particularly if the dataset is enormous.

As a result of these factors, machine learning algorithms may seem to be a thing of the past.

Deep learning algorithms, on the other hand, come at a price.

Finding their own characteristics requires a large quantity of data, which isn't always accessible.

Furthermore, as data volumes get larger, so do the processing power and training time requirements, since the network will be dealing with a lot more data.

Depending on the number and kinds of layers utilized, training time will also rise.

Fortunately, online computing, which lets anybody to rent powerful machines for a price, allows anyone to run some of the most demanding deep learning networks.

Convolutional neural networks need hidden layers that are not included in the standard neural network design.

Deep learning of this kind is most often connected with computer vision projects, and it is now the most extensively used approach in that sector.

In order to obtain information from an image, basic convnet networks would typically utilize three kinds of layers: convolutional layers, pooling layers, and dense layers.

Convolutional layers gather information from low-level features such as edges and curves by sliding a window, or convolutional kernel, over the picture.

Subsequent stacked convolutional layers will repeat this procedure over the freshly generated layers of low-level features, looking for increasingly higher-level characteristics until the picture is fully understood.

Different hyperparameters may be modified to find different sorts of features, such as the size of the kernel or the distance it glides over the picture.

Pooling layers enable a network to learn higher-level elements of an image in a progressive manner by down sampling the picture along the way.

The network may become too computationally costly without a pooling layer built amid convolutional layers as each successive layer examines more detailed data.

In addition, the pooling layer reduces the size of an image while preserving important details.

These characteristics become translation invariant, which means that a feature seen in one portion of an image may be identified in a totally other region of the same picture.

The ability of a convolutional neural network to retain positional information is critical for image classification.

The ability of deep learning to automatically parse through unstructured data to find local features that it deems important while retaining positional information about how these features interact with one another demonstrates the power of convolutional neural networks.

Recurrent neural networks excel at sequence-based tasks like sentence completion and stock price prediction.

The essential idea is that, unlike previous instances of networks in which neurons just transmit information forward, neurons in recurrent neural networks feed information forward while also periodically looping the output back to itself throughout a time step.

Recurrent neural networks may be regarded of as having a rudimentary type of memory since each time step includes recurrent information from all previous time steps.

This is often utilized in natural language processing projects because recurrent neural networks can handle text in a way that is more human-like.

Instead of seeing a phrase as a collection of isolated words, a recurrent neural network may begin to analyse the mood of the statement or even create the following sentence autonomously depending on what has already been stated.

In many respects akin to human talents, deep learning may give strong techniques of evaluating unstructured data.

Unlike humans, deep learning networks never get tired.

Deep learning may substantially outperform standard machine learning techniques when given enough training data and powerful computers, particularly given its autonomous feature engineering capabilities.

Image classification, voice recognition, and self-driving vehicles are just a few of the fields that have benefited tremendously from deep learning research over the previous decade.

Many new exciting deep learning applications will emerge if current enthusiasm and computer hardware upgrades continue to grow.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Automatic Film Editing; Berger-Wolf, Tanya; Cheng, Lili; Clinical Decision Support Systems; Hassabis, Demis; Tambe, Milind.

Further Reading:

Chollet, François. 2018. Deep Learning with Python. Shelter Island, NY: Manning Publications.

Géron, Aurélien. 2019. Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Second edition. Sebastopol, CA: O’Reilly Media.

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2017. Deep Learning. Cambridge, MA: MIT Press.

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