Author: Eric Walz

The emerging field of machine learning has important uses in a variety of fields, such as finance, healthcare and business. For self-driving cars, machine learning is an important tool that can be used to predict the behavior of other road users, such as other human drivers, pedestrians and bicyclists.

For Alphabet subsidiary Waymo, machine learning is one of its most important tools in its arsenal to build the world's best autonomous driving system it calls "Waymo Driver" that performs better than a human driver.

However, unlike a computer, human drivers have the ability to anticipate and predict what others on the road might do and can learn from past experiences, something that's difficult to train a computer to do and requires lots of processing power. So Waymo developed is own machine learning model that can do the same job and with less compute.

For example, when approaching an intersection a human driver might anticipate that another driver traveling in the opposite direction will make a left turn in their path or a pedestrian may enter the roadway. By anticipating this behavior the driver can mentally prepare to brake if needed. Predicting these types of behaviors for a computer is challenging for engineers working on self-driving vehicles.

That's where machine learning comes into play, allowing Waymo's autonomous vehicles to make better decisions. Machine learning is commonly used to model and reduce some of this complexity, thereby enabling the self-driving system to learn new types of behavior.

Waymo collects data from the real world from driving millions of miles and billions more miles in computer simulation built from data collected from its fleet. To navigate, Waymo's autonomous vehicles rely on highly complex, high-definition maps and vehicle sensor data. However, this data alone is not enough to make predictions, according to Waymo.

Simplifying a Complex Scene

The behavior of other road users is often complicated and difficult to capture with just map-derived traffic rules because driving patterns vary and human drivers often break the rules they're supposed to follow.

The most popular way to incorporate highly detailed, centimeter-level maps into behavior prediction models is by rendering the map into pixels and encoding all of the scene information, such as traffic signs, crosswalks, road lanes, and road boundaries, with a convolutional neural network (CNN).

However, this method requires a tremendous amount of processor power and takes time (latency), which is not ideal for a self-driving vehicle that needs to make decisions in a fraction of a second.

To address these issues and make better predictions Waymo developed a new model it calls "VectorNet", that provides more accurate behavior predictions while using less compute than CNNs, the company says. VectorNet essentially takes a highly complex scene and simplifies it using vectors so it can be processed with less computing power.

Map features can be simplified into vectors, which are easier for machine learning models to process.

This complex scene can be broken down into vectors to make is easier to process.

For example, an intersection crosswalk can be represented as a polygon defined by several points and a stop sign can be represented by a single point. Road curves can be approximately represented as polylines by "connected the dots." These polylines are then further split into vector fragments.

In this way, Waymo's engineers are able to represent all the road features and the trajectories of the other objects as a set of simplified vectors instead of a highly complex scene, which is much more difficult to work with. With this simplified view, Waymo designed VectorNet to effectively process its vehicle sensor data and map inputs.

The neural network is implemented to capture the relationships between various vectors. These relationships occur when, for example, a car enters an intersection or a pedestrian approaches a crosswalk. Through learning such interactions between road features and object trajectories, VectorNet's data-driven, machine learning-based approach allows Waymo to better predict other agents' behavior by learning from different behavior patterns.

Waymo proposed a novel hierarchical graph neural network. The first level is composed of polyline subgraphs. Then VectorNet gathers information within each polyline. In the second level called "global interaction graph", VectorNet exchanges information among polylines.

Here is the simplfied intersection with the input vectors that are converted to polyline subgraphs.

To further boost VectorNet's capabilities and understanding of the real world, Waymo trained the system to learn from context clues to make inferences about what could happen next around the vehicle to make improved behavior predictions.

For example, important scene information can often be obscured while driving, such as a tree branch blocking a stop sign. When this happens to a human driver, they can draw upon past experiences about the possibility of the stop sign being there, although they cannot see it. Machine learning makes these types of predictions using inference.

To further improve the accuracy of VectorNet, Waymo randomly masks out map features during training, such as a stop sign at a four-way intersection and requiring the CNN to complete it.

In this way, VectorNet can further improve the Waymo Driver's understanding of the world around it and be better prepared for any unexpected situations.

The intersection is broken down to create a global interaction graph.

Waymo validated the performance of VectorNet with the task of trajectory prediction, an important task for a self-driving vehicle that interacts with human drivers on the road. Compared with ResNet-18, one of the most advanced and widely used CNNs, VectorNet achieves up to 18% better performance while using only 29% of the parameters and consuming just 20% of the computation when there are 50 agents (other vehicles, pedestrians) per scene, Waymo reported.

Also this week, Waymo announced its latest funding round of $750 million. With the new funding, Waymo has raised $3 billion since March. The company is working on self-driving cars, commercial robotaxis and self-driving trucks that will all be powered by its advanced AI software.

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Waymo Develops a Machine Learning Model to Predict the Behavior of Other Road Users for its Self-Driving Vehicles - FutureCar

From navigating to a new place to picking out new music, algorithms have laid the foundation for large parts of modern life. Similarly, artificial Intelligence is booming because it automates and backs so many products and applications. Recently, I addressed some analytical applications for TensorFlow. In this article, Im going to lay out a higher-level view of Googles TensorFlow deep learning framework, with the ultimate goal of helping you to understand and build deep learning algorithms from scratch.

Over the past couple of decades, deep learning has evolved rapidly, leading to massive disruption in a range of industries and organizations. The term was coined in 1943 when Warren McCulloch and Walter Pitts created a computer model based on neural networks of a human brain, creating the first artificial neural networks (or ANNs). Deep learning now denotes a branch of machine learning that deploys data-centric algorithms in real-time.

Backpropagation is a popular algorithm that has had a huge impact in the field of deep learning. It allows ANNs to learn by themselves based on the errors they generate while learning. To further enhance the scope of an ANN, architectures like Convolutional Neural Networks, Recurrent Neural Networks, and Generative Networks have come into the picture. Before we delve into them, lets first understand the basic components of a neural network.

Neurons and Artificial Neural Networks

An artificial neural network is a representational framework that extracts features from the data its given. The basic computational unit of an ANN is the neuron. Neurons are connected using artificial layers through which the information passes. As the information flows through these layers, the neural network identifies patterns between the data. This type of processing makes ANNs useful for several applications, such as for prediction and classification.

Now lets take a look at the basic structure of an ANN. It consists of three layers: the input layer, the output layer, which is always fixed or constant, and the hidden layer. Inputs initially pass through an input layer. This layer always accepts a constant set of dimensions. For instance, if we wanted to train a classifier that differentiates between dogs and cats, the inputs (in this case, images) should be of the same size. The input then passes through the hidden layers and the network updates the weights and recognizes the patterns. In the final step, we classify the data at the output layer.

Weights and Biases

Every neuron inside a neural network is associated with parameters, weight and bias. The weight is an integer that controls the signals between any two neurons. If the output is desirable, meaning that the output is in proximity to the one that we expected it to produce, then the weights are ideal. If the same network is generating an erroneous output thats far away from the actual one, then the network alters the weights to improve the subsequent results.

Bias, the other parameter, is the algorithms tendency to consistently learn the wrong thing by not taking into account all the information in the data. For the model to be accurate, bias needs to be low. If there are inconsistencies in the dataset, like missing values, fewer data tuples, or erroneous input data, the bias would be high and the predicted values could be wrong.

Working of a Neural Network

Before we get started with TensorFlow, lets examine how a neural network produces an output with weights, biases, and input by taking a look at the first neural network, called Perceptron, which dates back to 1958. The Perceptron network is a simple binary classifier. Understanding how this works will allow us to comprehend the workings of a modern neuron.

The Perceptron network is a supervised machine learning technique that uses a binary classifier function by mapping a vector of binary variables to a single binary output. It works as follows:

Multiply the inputs (x1, x2, x3) of the network to their corresponding weights (w1, w2, w3).

Add the multiplied weights and inputs together. This is called the weighted sum, denoted by, x1*w1 + x2*w2 +x3*w3

Apply the activation function. Determine whether the weighted sum is greater than a threshold (say, 0.5), if yes, assign 1 as the output, otherwise assign 0. This is a simple step function.

Of course, Perceptron is a simple neural network that doesnt wholly consider all the concepts necessary for an end-to-end neural network. Therefore, lets go over all the phases that a neural network has to go through to build a sophisticated ANN.

Input

A neural network has to be defined with the number of input dimensions, output features, and hidden units. All these metrics fall in a common basket called hyperparameters. Hyperparameters are numeric values that determine and define the neural network structure.

Weights and biases are set randomly for all neurons in the hidden layers.

Feed Forward

The data is sent into the input and hidden layers, where the weights get updated for every iteration. This creates a function that maps the input with the output data. Mathematically, it is defined asy=f(x), where y is the output, x is the input, and f is the activation function.

For every forward pass (when the data travels from the input to the output layer), the loss is calculated (actual value minus predicted value). The loss is again sent back (backpropagation) and the network is retrained using a loss function.

Output error

The loss is gradually reduced using gradient descent and loss function.

The gradient descent can be calculated with respect to any weight and bias.

Backpropagation

We backpropagate the error that traverses through each and every layer using the backpropagation algorithm.

Output

By minimizing the loss, the network re-updates the weights for every iteration (One Forward Pass plus One Backward Pass) and increases its accuracy.

As we havent yet talked about what an activation function is, Ill expand that a bit in the next section.

Activation Functions

An activation function is a core component of any neural network. It learns a non-linear, complex functional mapping between the input and the response variables or output. Its main purpose is to convert an input signal of a node in an ANN to an output signal. That output signal is the input to the subsequent layer in the stack. There are several types of activation functions available that could be used for different use cases. You can find a list comprising the most popular activation functions along with their respective mathematical formulae here.

Now that we understand what a feed forward pass looks like, lets also explore the backward propagation of errors.

Loss Function and Backpropagation

During training of a neural network, there are too many unknowns to be deciphered. As a result, calculating the ideal weights for all the nodes in a neural network is difficult. Therefore, we use an optimization function through which we could navigate the space of possible ideal weights to make good predictions with a trained neural network.

We use a gradient descent optimization algorithm wherein the weights are updated using the backpropagation of error. The term gradient in gradient descent refers to an error gradient, where the model with a given set of weights is used to make predictions and the error for those predictions is calculated. The gradient descent optimization algorithm is used to calculate the partial derivatives of the loss function (errors) with respect to any weight w and bias b. In practice, this means that the error vectors would be calculated commencing from the final layer, and then moving towards the input layer by updating the weights and biases, i.e., backpropagation. This is based on differentiations of the respective error terms along each layer. To make our lives easier, however, these loss functions and backpropagation algorithms are readily available in neural network frameworks such as TensorFlow and PyTorch.

Moreover, a hyperparameter called learning rate controls the rate of adjustment of weights of a network with respect to the gradient descent. The lower the learning rate, the slower we travel down the slope (to reach the optimum, or so-called ideal case) while calculating the loss.

TensorFlow is a powerful neural network framework that can be used to deploy high-level machine learning models into production. It was open-sourced by Google in 2015. Since then, its popularity has increased, making it a common choice for building deep learning models. On October 1st, a new, stable version got released, called TensorFlow 2.0, with a few major changes:

Eager Execution by Default - Instead of creating tf.session(), we can directly execute the code as usual Python code. In TensorFlow 1.x, we had to create a TensorFlow graph before computing any operation. In TensorFlow 2.0, however, we can build neural networks on the fly.

Keras Included - Keras is a high-level neural network built on top of TensorFlow. It is now integrated into TensorFlow 2.0 and we can directly import Keras as tf.keras, and thereby define our neural network.

TF Datasets - A lot of new datasets have been added to work and play with in a new module called tf.data.

1.0 Support: All the existing TensorFlow 1.x code can be executed using TensorFlow 2.0; we need not modify any of our previous code.

Major Documentation and API cleanup changes have also been introduced.

The TensorFlow library was built based on computational graphs a runtime for executing such computational graphs. Now, lets perform a simple operation in TensorFlow.

Here, we declared two variables a and b. We calculated the product of those two variables using a multiplication operation in Python (*) and stored the result in a variable called prod. Next, we calculated the sum of a and b and stored them in a variable named sum. Lastly, we declared the result variable that would divide the product by the sum and then would print it.

This explanation is just a Pythonic way of understanding the operation. In TensorFlow, each operation is considered as a computational graph. This is a more abstract way of describing a computer program and its computations. It helps in understanding the primitive operations and the order in which they are executed. In this case, we first multiply a and b, and only when this expression is evaluated, we take their sum. Later, we take prod and sum, and divide them to output the result.

TensorFlow Basics

To get started with TensorFlow, we should be aware of a few essentials related to computational graphs. Lets discuss them in brief:

Variables and Placeholders: TensorFlow uses the usual variables, which can be updated at any point of time, except that these need to be initialized before the graph is executed. Placeholders, on the other hand, are used to feed data into the graph from outside. Unlike variables, they dont need to be initialized.Consider a Regression equation, y = mx+c, where x and y are placeholders, and m and c are variables.

Constants and Operations: Constants are the numbers that cannot be updated. Operations represent nodes in the graph that perform computations on data.

Graph is the backbone that connects all the variables, placeholders, constants, and operators.

Prior to installing TensorFlow 2.0, its essential that you have Python on your machine. Lets look at its installation procedure.

Python for Windows

You can download it here.

Click on the Latest Python 3 release - Python x.x.x. Select the option that suits your system (32-bit - Windows x86 executable installer, or 64-bit - Windows x86-64 executable installer). After downloading the installer, follow the instructions that are displayed on the setup wizard. Make sure to add Python to your PATH using environment variables.

Python for OSX

You can download it here.

Click on the Latest Python 3 release - Python x.x.x. Select macOS 64-bit installer,and run the file.

Python on OSX can also be installed using Homebrew (package manager).

To do so, type the following commands:

Python for Debian/Ubuntu

Invoke the following commands:

This installs the latest version of Python and pip in your system.

Python for Fedora

Invoke the following commands:

This installs the latest version of Python and pip in your system.

After youve got Python, its time to install TensorFlow in your workspace.

To fetch the latest version, pip3 needs to be updated. To do so, type the command

Now, install TensorFlow 2.0.

This automatically installs the latest version of TensorFlow onto your system. The same command is also applicable to update the older version of TensorFlow.

The argument tensorflow in the above command could be any of these:

tensorflow Latest stable release (2.x) for CPU-only.

tensorflow-gpu Latest stable release with GPU support (Ubuntu and Windows).

tf-nightly Preview build (unstable). Ubuntu and Windows include GPU support.

tensorflow==1.15 The final version of TensorFlow 1.x.

To verify your install, execute the code:

Now that you have TensorFlow on your local machine, Jupyter notebooks are a handy tool for setting up the coding space. Execute the following command to install Jupyter on your system:

Now that everything is set up, lets explore the basic fundamentals of TensorFlow.

Tensors have previously been used largely in math and physics. In math, a tensor is an algebraic object that obeys certain transformation rules. It defines a mapping between objects and is similar to a matrix, although a tensor has no specific limit to its possible number of indices. In physics, a tensor has the same definition as in math, and is used to formulate and solve problems in areas like fluid mechanics and elasticity.

Although tensors were not deeply used in computer science, after the machine learning and deep learning boom, they have become heavily involved in solving data crunching problems.

Scalars

The simplest tensor is a scalar, which is a single number and is denoted as a rank-0 tensor or a 0th order tensor. A scalar has magnitude but no direction.

Vectors

A vector is an array of numbers and is denoted as a rank-1 tensor or a 1st order tensor. Vectors can be represented as either column vectors or row vectors.

A vector has both magnitude and direction. Each value in the vector gives the coordinate along a different axis, thus establishing direction. It can be depicted as an arrow; the length of the arrow represents the magnitude, and the orientation represents the direction.

Matrices

A matrix is a 2D array of numbers where each element is identified by a set of two numbers, row and column. A matrix is denoted as a rank-2 tensor or a 2nd order tensor. In simple terms, a matrix is a table of numbers.

Tensors

A tensor is a multi-dimensional array with any number of indices. Imagine a 3D array of numbers, where the data is arranged as a cube: thats a tensor. When its an nD array of numbers, that's a tensor as well. Tensors are usually used to represent complex data. When the data has many dimensions (>=3), a tensor is helpful in organizing it neatly. After initializing, a tensor of any number of dimensions can be processed to generate the desired outcomes.

TensorFlow represents tensors with ease using simple functionalities defined by the framework. Further, the mathematical operations that are usually carried out with numbers are implemented using the functions defined by TensorFlow.

Firstly, lets import TensorFlow into our workspace. To do so, invoke the following command:

This enables us to use the variable tf thereafter.

Now, lets take a quick overview of the basic operations and math, and you can simultaneously execute the code in the Jupyter playground for a better understanding of the concepts.

tf.Tensor

The primary object in TensorFlow that you play with is tf.Tensor. This is a tensor object that is associated with a value. It has two properties bound to it: data type and shape. The data type defines the type and size of data that will be consumed by a tensor. Possible types include float32, int32, string, et cetera. Shape defines the number of dimensions.

tf.Variable()

The variable constructor requires an argument which could be a tensor of any shape and type. After creating the instance, this variable is added to the TensorFlow graph and can be modified using any of the assign methods. It is declared as follows:

Output:

tf.constant()

The tensor is populated with a value, dtype, and, optionally, a shape. This value remains constant and cannot be modified further.

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A Lightning-Fast Introduction to Deep Learning and TensorFlow 2.0 - Built In

A detailed study on the Global AI and Machine Learning Operationalization Software Market is used for the understanding the strategies, which is used by the manufacturers for increased in changes for the growth of the market in the estimated forecast period. Market research is one of the methods for the determination and estimation of the growth of the Global keyword Market in the estimated forecast period.

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2020 Global AI and Machine Learning Operationalization Software Market to Witness a Pronounce Growth by 2025 - News Distinct

Northern Trust has deployed machine learning models within its FX currency management solutions business, designed to enable greater oversight of thoughts of daily data points.

The solution has been developed in partnership with Lumint, an outsourced FX execution services provider, and will help buy-side firms reduce risk throughout the currency management lifecycle.

The technology utilised by the Robotic Oversight System (ROSY) for Northern Trust systematically scans newly arriving, anonymised data to identify anomalies across multi-dimensional data sets. It is also built on machine learning models developed by Lumint using a cloud platform that allows for highly efficient data processing.

In a data-intensive business, ROSY acts like an additional member of the team working around the clock to find and flag anomalies. The use of machine learning to detect data outliers enables us to provide increasingly robust and intuitive solutions to enhance our oversight and risk management, which can be particularly important in volatile markets, said Andy Lemon, head of currency management, Northern Trust.

Northern Trust announced astrategic partnership with Lumint in 2018to deliver currency management services with portfolio, share class and lookthrough hedging solutions alongside transparency and analytics tools.

Northern Trusts deployment of ROSY amplifies the scalability of its already highly automated currency hedging operation; especially for the more sophisticated products such as look-through hedging offered to its global clients, added Alex Dunegan, CEO, Lumint.

The solution is the latest rollout of machine learning technology by Northern Trust, as the bank continues to leverage new technologies across its businesses. In August last year, Northern Trust developed a new pricing engine within its securities lending business by utilising machine learning and advanced statistical technology.

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Northern Trust rolls out machine learning tech for FX management solutions - The TRADE News

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Machine Learning Market 2020 Business Overview by Manufacturers, Regions, Investment Analysis, Growth Prospects, and Forecast to 2025 - News Distinct

Global markets continue to sink as the corona virus spreads, reaching over 200 countries in total by the end of March. Now the outbreak continued to grow, as the number of cases in USA, Italy, Spain, Germany, France all spiked, Europe and USA have now become the epicenter of the outbreak, Cases in China appear have steadied in April, but theres growing concern about the overall impact to the global Machine Learning in Communication market. This study analysis was given on a worldwide scale, for instance, present and traditional Machine Learning in Communication growth analysis, competitive analysis, and also the growth prospects of the central regions. The report gives an exhaustive investigation of Machine Learning in Communication industry at country & regional levels, and provides an analysis of the industry trends in each of the sub-segments, from sales, revenue and consumption. A Machine Learning in Communication quantitative and qualitative analysis of the main players in related regions is introduced, from the perspective of sales, revenue and price.

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According to Orbis Reports, the global Machine Learning in Communication market was valued at USD xxx million in 2019, and it is expected to reach a value of USD xxx million by 2025, at a CAGR of xx% over the forecast period 2021-2025. Correspondingly, the forecast analysis of Machine Learning in Communication industry comprises of Asia, North America, South America, Middle East and Africa, Europe, with the sales and revenue data in each of the sub-segments.

At the upcoming section, Machine Learning in Communication report discusses industrial policy, economic environment, in addition to the fabrication processes and cost structures of the industry. And this report encompasses the fundamental dynamics of the Machine Learning in Communication market which include drivers, opportunities, and challenges faced by the industry. Additionally, this report showed a keen market study of the main consumers, raw material manufacturers and distributors, etc.

In order to stop the spread of the COVID-19 outbreak, countries and world capital have been put under strict lockdown, bringing a total halt to major industrial production chains. It has caused supply chain disruptions for nearly three-quarters of U.S. companies, and in the second quarter, domestically consumption is likely to be hit even harder. The same situation also appeared in Europe, as the epidemic has required large-scale restrictions on the movement of people, investment, consumption and exports will all be strongly impacted by the epidemic, domestic production and consumption will plummet in the first half of 2020. We expected a U-shaped recovery in the second half of the year in USA and Europe market.

China, Japan, South Korea, India, and other Asia countries took the lead in introducing unprecedented measures to contain the virus, the market confidence in Asia-Pacific region is returning, EU and USA have relaxed its fiscal rules with maximum flexibility, this will stimulate the market demand in the second half of 2020.

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Geographically, this report is segmented into several key Regions, with production, consumption, revenue (M USD), market share and growth rate of Machine Learning in Communication in these regions, from 2020 to 2025 (forecast), covering

Asia-Pacific (China, Japan, Korea, India and Southeast Asia)North America (United States, Canada and Mexico)Europe (Germany, France, UK, Russia and Italy)South America (Brazil, Argentina, Columbia)Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Global Machine Learning in Communication market competition by top manufacturers, with production, price, revenue (value) and market share for each manufacturer; the top players including

AmazonIBMMicrosoftGoogleNextivaNexmoTwilioDialpadCiscoRingCentral

On the basis of product, this report displays the production, revenue, price, Machine Learning in Communication market share and growth rate of each type, primarily split into

Cloud-BasedOn-Premise

On the basis on the end users/applications, this report focuses on the status and outlook for major applications/end users, consumption (sales), market share and growth rate of Machine Learning in Communication for each application, including

Network OptimizationPredictive MaintenanceVirtual AssistantsRobotic Process Automation (RPA)

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Machine Learning in Communication Market Growth Analysis, Share, Demand By Regions, Types And Analysis Of Key Players Research Forecasts To 2025 -...

Valve is introducing an exciting new Steam Labs experiment to Steam. Experiment 008, officially titled Play Next, is a simple feature that uses machine learning to tell you what game you should play next.

Using machine learning to make informed suggestions, the feature is designed to help users with extensive libraries decide which of their games to play next, stated Valve in its official blog. Users who have unplayed (or very low playtime) games in their library, will now have a Play Next shelf available in the library view.

Though Valve has not told us anything in-depth about the feature, it seems to take information from your recently played games and gives you options based on the similarities in genre and modes. If you usually play FPS games, Steam might recommend different kinds of shooters like battle royales or survival games that are found in your library.

To check out the Play Next feature, make sure you have the latest Steam update installed and the new feature should appear right under your recent games in the Library tab.

READ MORE: This is what Tokido thinks about when he plays Street Fighter

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Steam now uses machine-learning to tell you what game you should play next - Yahoo News

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Moreover, the AI/Machine Learning report offers a detailed analysis of the competitive landscape in terms of regions and the major service providers are also highlighted along with attributes of the market overview, business strategies, financials, developments pertaining as well as the product portfolio of the AI/Machine Learning market. Likewise, this report comprises significant data about market segmentation on the basis of type, application, and regional landscape. The AI/Machine Learning market report also provides a brief analysis of the market opportunities and challenges faced by the leading service provides. This report is specially designed to know accurate market insights and market status.

By Regions:

* North America (The US, Canada, and Mexico)

* Europe (Germany, France, the UK, and Rest of the World)

* Asia Pacific (China, Japan, India, and Rest of Asia Pacific)

* Latin America (Brazil and Rest of Latin America.)

* Middle East & Africa (Saudi Arabia, the UAE, , South Africa, and Rest of Middle East & Africa)

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Table of Content

1 Introduction of AI/Machine Learning Market

1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology

3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 AI/Machine Learning Market Outlook

4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 AI/Machine Learning Market, By Deployment Model

5.1 Overview

6 AI/Machine Learning Market, By Solution

6.1 Overview

7 AI/Machine Learning Market, By Vertical

7.1 Overview

8 AI/Machine Learning Market, By Geography

8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 AI/Machine Learning Market Competitive Landscape

9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix

11.1 Related Research

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AI/Machine Learning Market Growth by Top Companies, Trends by Types and Application, Forecast to 2026 - Cole of Duty

AUSTIN, Texas, May 14, 2020 /PRNewswire/ --SparkCognition, a leading industrial artificial intelligence (AI) company, is pleased to announce that Japanese AI and Fintech company, MILIZE Co., Ltd. will offer Japanese financial institutions fraud detection and anti-money laundering solutions. These solutions will be built using the automated machine learning software of SparkCognition.

With the enormous increase of online payment, internet banking, and QR code payments, illegal use of credit cards is on the rise. However, there are not many Japanese companies that have introduced advanced solutions for fraud detection that currently exist internationally. In addition, financial authorities and institutions around the world are expected to report strengthened measures against money laundering in August 2020. As a result, taking these steps against money laundering has become an urgent management issue in Japanese financial institutions.

At one credit card company in South America, the ratio of fraudulent use to the total transactions reached about 20%, which reduced the profitability of the business. Therefore the company introduced a fraudulent transaction detection system that utilizes the AI technology of SparkCognition, which has extensive experience working with financial service clients. Though the credit card company did not have a team of data scientists, due to the ease with which analysts on staff were able to apply SparkCognition technology, accurate machine learning models were developed, tested, and operationalized within a few short weeks. As a result, it is now possible to detect fraudulent transactions with about 90% accuracy, which has led to a significant improvement in the credit card company's profitability.

Based on SparkCognition's international success in fielding machine learning systems in financial services, MILIZE will offer a fraud detection and anti-money laundering solution, built with SparkCognition AI technology, along with consulting services, development and operational assistance to local credit card companies, banks and other financial institutions. By submitting transaction data to a MILIZE-operated cloud service, financial institutions will be able to detect suspicious transactions without making large-scale investments in self-hosted infrastructure.

MILIZE makes full use of quantitative techniques, fintech, AI, and big data, and provides a large number of operational support solutions such as risk management, performance forecast, stock price forecast, and more, to a wide range of financial institutions. SparkCognition is a leading company in the field of artificial intelligence and provides AI solutions to companies and government agencies around the world.

To learn more about SparkCognition, visit http://www.sparkcognition.com.

About SparkCognition:

With award-winning machine learning technology, a multinational footprint, and expert teams focused on defense, IIoT, and finance, SparkCognition builds artificial intelligence systems to advance the most important interests of society. Our customers are trusted with protecting and advancing lives, infrastructure, and financial systems across the globe. They turn to SparkCognition to help them analyze complex data, empower decision-making, and transform human and industrial productivity. SparkCognition offers four main products:DarwinTM, DeepArmor, SparkPredict, and DeepNLPTM. With our leading-edge artificial intelligence platforms, our clients can adapt to a rapidly changing digital landscape and accelerate their business strategies. Learn more about SparkCognition's AI applications and why we've been featured in CNBC's 2017 Disruptor 50, and recognized three years in a row on CB Insights AI 100, by visiting http://www.sparkcognition.com.

For Media Inquiries:

Michelle SaabSparkCognitionVP, Marketing Communications [emailprotected] 512-956-5491

SOURCE SparkCognition

http://sparkcognition.com

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SparkCognition and Milize to Offer Automated Machine Learning Solutions for Financial Institutions to the APAC Region - PRNewswire

The chaos and uncertainty surrounding the coronavirus pandemic have claimed an unlikely victim: the machine learning systems that are programmed to make sense of our online behavior.

The algorithms that recommend products on Amazon, for instance, are struggling to interpret our new lifestyles, MIT Technology Review reports.

And while machine learning tools are built to take in new data, they're typically not so robust that they can adapt as dramatically as needed.

For instance, MIT Tech reports that a company that detects credit card fraud needed to step in and tweak its algorithm to account for a surge of interest in gardening equipment and power tools.

An online retailer found that its AI was ordering stock that no longer matched with what was selling. And a firm that uses AI to recommend investments based on sentiment analysis of news stories was confused by the generally negative tone throughout the media.

"The situation is so volatile," Rael Cline, CEO of the algorithmic marketing consulting firm Nozzle, told MIT Tech.

"You're trying to optimize for toilet paper last week, and this week everyone wants to buy puzzles or gym equipment."

While some companies are dedicating more time and resources to manually steering their algorithms, others see this as an opportunity to improve.

"A pandemic like this is a perfect trigger to build better machine-learning models," Sharma said.

READ MORE: Our weird behavior during the pandemic is messing with AI models

This article was originally published by Futurism. Read the original article.

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Our Behaviour in This Pandemic Has Seriously Confused AI Machine Learning Systems - ScienceAlert