The latest report titled, Global Machine Learning in Retail Market 2019 by Company, Regions, Type and Application, Forecast to 2024 unveils the value at which the Machine Learning in Retail industry is anticipated to grow during the forecast period, 2019 to 2024. The report estimates CAGR analysis, competitive strategies, growth factors and regional outlook 2024. The report is a rich source of an exhaustive study of the driving elements, limiting components, and different market changes. It states market structure and then further forecasts several segments and sub-segments of the global market. The market study is provided on the basis of type, application, manufacturer as well as geography. Different elements such as opportunities, drivers, restraints, and challenges, market situation, market share, growth rate, future trends, risks, entry limits, sales channels, distributors are analyzed and examined within this report.

Exploring The Growth Rate Over A Period:

Business owners want to expand their business can refer to this report as it includes data regarding the rise in sales within a given consumer base for the forecast period, 2019 to 2024. The research analysts have mentioned a comparison between the Machine Learning in Retail market growth rate and product sales to allow business owners to discover the success or failure of a specific product or service. They have also added the driving factors such as demographics and revenue generated from other products to offer a better analysis of products and services by owners.

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Top industry players assessment: IBM, Microsoft, Amazon Web Services, Oracle, SAP, Intel, NVIDIA, Google, Sentient Technologies, Salesforce, ViSenze,

Product type assessment based on the following types: Cloud Based, On-Premises

Application assessment based on application mentioned below: Online, Offline

Leading market regions covered in the report are: North America (United States, Canada and Mexico), Europe (Germany, France, UK, Russia and Italy), Asia-Pacific (China, Japan, Korea, India and Southeast Asia), South America (Brazil, Argentina, Colombia), Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Main Features Covered In Global Machine Learning in Retail Market 2019 Report:

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Moreover in the report, supply chain analysis, regional marketing type analysis, international trade type analysis by the market as well as consumer analysis of Machine Learning in Retail market has been covered. Further, it determines the manufacturing plants and technical data analysis, capacity, and commercial production date, R&D Status, manufacturing area distribution, technology source, and raw materials sources analysis. It also depicts to depict sales, merchants, brokers, wholesalers, research findings and conclusion, and information sources.

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Emerging Trend of Machine Learning in Retail Market 2019 by Company, Regions, Type and Application, Forecast to 2024 - Bandera County Courier

Boffins in Germany have devised a technique to subvert neural network frameworks so they misidentify images without any telltale signs of tampering.

Erwin Quiring, David Klein, Daniel Arp, Martin Johns, and Konrad Rieck, computer scientists at TU Braunschweig, describe their attack in a pair of papers, slated for presentation at technical conferences in May and in August this year events that may or may not take place given the COVID-19 global health crisis.

The papers, titled "Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning" [PDF] and "Backdooring and Poisoning Neural Networks with Image-Scaling Attacks [PDF]," explore how the preprocessing phase involved in machine learning presents an opportunity to fiddle with neural network training in a way that isn't easily detected. The idea being: secretly poison the training data so that the software later makes bad decisions and predictions.

This example image, provided by the academics, of a cat has been modified so that when downscaled by an AI framework for training, it turns into a dog, thus muddying the training dataset

There have been numerous research projects that have demonstrated that neural networks can be manipulated to return incorrect results, but the researchers say such interventions can be spotted at training or test time through auditing.

"Our findings show that an adversary can significantly conceal image manipulations of current backdoor attacks and clean-label attacks without an impact on their overall attack success rate," explained Quiring and Rieck in the Backdooring paper. "Moreover, we demonstrate that defenses designed to detect image scaling attacks fail in the poisoning scenario."

Their key insight is that algorithms used by AI frameworks for image scaling a common preprocessing step to resize images in a dataset so they all have the same dimensions do not treat every pixel equally. Instead, these algorithms, in the imaging libraries of Caffe's OpenCV, TensorFlow's tf.image, and PyTorch's Pillow, specifically, consider only a third of the pixels to compute scaling.

"This imbalanced influence of the source pixels provides a perfect ground for image-scaling attacks," the academics explained. "The adversary only needs to modify those pixels with high weights to control the scaling and can leave the rest of the image untouched."

On their explanatory website, the eggheads show how they were able to modify a source image of a cat, without any visible sign of alteration, to make TensorFlow's nearest scaling algorithm output a dog.

This sort of poisoning attack during the training of machine learning systems can result in unexpected output and incorrect classifier labels. Adversarial examples can have a similar effect, the researchers say, but these work against one machine learning model.

Image scaling attacks "are model-independent and do not depend on knowledge of the learning model, features or training data," the researchers explained. "The attacks are effective even if neural networks were robust against adversarial examples, as the downscaling can create a perfect image of the target class."

The attack has implications for facial recognition systems in that it could allow a person to be identified as someone else. It could also be used to meddle with machine learning classifiers such that a neural network in a self-driving car could be made to see an arbitrary object as something else, like a stop sign.

To mitigate the risk of such attacks, the boffins say the area scaling capability implemented in many scaling libraries can help, as can Pillow's scaling algorithms (so long as it's not Pillow's nearest scaling scheme). They also discuss a defense technique that involves image reconstruction.

The researchers plan to publish their code and data set on May 1, 2020. They say their work shows the need for more robust defenses against image-scaling attacks and they observe that other types of data that get scaled like audio and video may be vulnerable to similar manipulation in the context of machine learning.

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FYI: You can trick image-recog AI into, say, mixing up cats and dogs by abusing scaling code to poison training data - The Register

With businesses everywhere being disrupted by the coronavirus outbreak, it seems like a tough time to be an entrepreneur starting a new venture.

Yet the co-founders of the Richmond-based startup company Forestry Machine Learning say they are keeping a positive long-term outlook.

The startup specializes in helping clients implement a cutting-edge type of artificial intelligence called machine learning to improve their business strategies and operations, and the co-founders say they foresee demand only increasing for that service.

It is an interesting time to be launching a company, said David Der, the startups CEO. Co-founder Brian Forrester is chief revenue officer.

Overall, I am optimistic, Der said. Sure, there might be some setbacks nobody is really taking in-person meetings right now but a lot of the value we can deliver can be done virtually anyway.

Our sales strategy remains the same, he said. We are still prospecting and in business development stages, full speed ahead.

Machine learning is a subset of artificial intelligence that involves using computer algorithms to quickly analyze large amounts of data and learn from it. The tools can be used to make better predictions about how people and systems behave.

The Forestry part of the companys name is a nod to lingo within the artificial intelligence industry.

Machine learning, artificial intelligence, and the larger ecosystem around that, is really just coming of age, said Forrester, who is also co-founder of Workshop Digital, a Richmond-based digital marketing firm where he continues to work.

For the last three or four years, we have had access to more data than we have ever had before, Forrester said. Computing power has caught up to be able to process that. A lot of the companies I work with over 100 companies across the U.S. and Canada are still trying to figure out how to leverage that data to inform business strategy, reduce risk and increase profitability.

Machine learning can be used to improve financial forecasting, cybersecurity and fraud prevention, among other things, said Der, who brings to the startup a background in computer science.

Der was among a group of co-founders of Notch, a technology consulting company founded in Richmond in 2014 that specialized in data engineering and machine learning. In late 2017, Notch was acquired by financial services giant Capital One Financial Corp.

Der said he left Capital One in December after a two-year commitment and started working on creating the new business.

Entrepreneurship is really a passion of mine, Der said. In a way, we are picking up the torch where Notch left off two years ago. I also want to bring to the table my experience now from the financial services industry.

While machine learning can be utilized by many organizations, Der said the startup is targeting three primary industries: financial services, health care and digital marketing.

The goal of machine learning in digital marketing is to deliver the right message to the right person through the right medium at the right time, Der said.

Forrester brings deep experience in digital marketing through his company, Digital Workshop.

I have spent 11 years building a company, and we have been fairly successful, Forrester said. My role in this company [Forestry] is to build our sales and marketing strategy as we grow and follow Davids lead.

Will Loving and Scott Walker, both with Richmond-based Consult360, also are investing partners in the startup.

Forrester said he has experience navigating a startup during a time of economic disruption.

I dont think the problems that machine learning is trying to solve are going to go away just because of this, he said, referring to the coronavirus disruptions. In fact, they are more pervasive now than ever. Leveraging more computing power to tackle bigger problems is not going to go away.

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Startup Spotlight: Forestry Machine Learning wants to help clients use artificial intelligence to improve business - Richmond.com

Engineers at the AMRC have researched the use of the cloud to capture data from machine tools with Tier 2 member Amido

Cloud data solutions being trialled at the University of Sheffield Advanced Manufacturing Research Centre (AMRC) could provide a secure and cost-effective way for SME manufacturers to explore how machine learning and Industry 4.0 technologies can boost their productivity.

Jon Stammers, AMRC technical fellow in the process monitoring and control team, says: Data is available on every shopfloor but a lot of time it isnt being captured due to lack of connectivity, and therefore cannot be analysed. If the cloud can capture and analyse that data then the possibilities are massive.

Engineers in the AMRCs Machining Group have researched the use of the cloud to capture data from machine tools with new Tier Two member Amido, an independent technical consultancy specialising in assembling, integrating and building cloud-native solutions.

Mr Stammers adds: Typically we would have a laptop sat next to a machine tool capturing its data; a researcher might do some analysis on that laptop and share the data on our internal file system or on a USB stick. There is a lot of data generated on the shopfloor and it is our job to capture it, but there are plenty of unanswered questions about the analysis process and the cloud has a lot to bring to that.

In the trial, data from two CNC machines in the AMRCs Factory of the Future: a Starrag STC 1250 and a DMG Mori DMU 40 eVo, was transferred to the Microsoft Azure Data Lake cloud service and converted into a parquet format, which allowed Amido to run a series of complex queries over a long period of time.

Steve Jones, engagement director at Amido, explains handling those high volumes of data is exactly what the cloud was designed for: Moving the data from the manufacturing process into the cloud means it can be stored securely and then structured for analysis. The data cant be intercepted in transit and it is immediately encrypted by Microsoft Azure.

Security is one of the huge benefits of cloud technology, Mr Stammers comments. When we ask companies to share their data for a project, it is usually rejected because they dont want their data going offsite. Part of the work were doing with Amido is to demonstrate that we can anonymise data and move it off site securely.

In addition to the security of the cloud, Mr Jones says transferring data into a data lake means large amounts can be stored for faster querying and machine learning.

One of the problems of a traditional database is when you add more data, you impact the ability for the query to return the answers to the questions you put in; by restructuring into a parquet format you limit that reduction in performance. Some of the queries that were taking one of the engineers up to 12 minutes to run on the local database, took us just 12 seconds using Microsoft Azure.

It was always our intention to run machine learning against this data to detect anomalies. A reading in the event data that stands out may help predict maintenance of a machine tool or prevent the failure of a part.

Storing data in the cloud is extremely inexpensive and that is why, according to software engineer in the process monitoring and control team Seun Ojo, cloud technology is a viable option for SMEs working with the AMRC, part of the High Value Manufacturing (HVM) Catapult.

He says: SMEs are typically aware of Industry 4.0 but concerned about the return on investment. Fortunately, cloud infrastructure is hosted externally and provided on a pay-per-use basis. Therefore, businesses may now access data capture, storage and analytics tools at a reduced cost.

Mr Jones adds: Businesses can easily hire a graphics processing unit (GPU) for an hour or a quantum computer for a day to do some really complicated processing and you can do all this on a pay-as-you-go basis.

The bar to entry to doing machine learning has never been lower. Ten years ago, only data scientists had the skills to do this kind of analysis but the tools available from cloud platforms like Microsoft Azure and Google Cloud now put a lot of power into the hands of inexpert users.

Mr Jones says the trials being done with Amido could feed into research being done by the AMRC into non-geometric validation.

He concludes: Rather than measuring the length and breadth of a finished part to validate that it has been machined correctly; I want to see engineers use data to determine the quality of a job.

That could be really powerful and if successful would make the process of manufacturing much quicker. That shows the value of data in manufacturing today.

AMRCwww.amrc.co.uk

Amidowww.amido.com

Michael Tyrrell

Digital Coordinator

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Proof in the power of data - PES Media

The statistical branch of Artificial Intelligence has enamored organizations across industries, spurred an immense amount of capital dedicated to its technologies, and entranced numerous media outlets for the past couple of years. All of this attention, however, will ultimately prove unwarranted unless organizations, data scientists, and various vendors can answer one simple question: can they provide Explainable AI?

Although the ability to explain the results of Machine Learning modelsand produce consistent results from themhas never been easy, a number of emergent techniques have recently appeared to open the proverbial black box rendering these models so difficult to explain.

One of the most useful involves modeling real-world events with the adaptive schema of knowledge graphs and, via Machine Learning, gleaning whether theyre related and how frequently they take place together.

When the knowledge graph environment becomes endowed with an additional temporal dimension that organizations can traverse forwards and backwards with dynamic visualizations, they can understand what actually triggered these events, how one affected others, and the critical aspect of causation necessary for Explainable AI.

Investments in AI may well hinge upon such visual methods for demonstrating causation between events analyzed by Machine Learning.

Read more: How to Make AI Work in Extreme Conditions?

As Judea Pearls renowned The Book of Why affirms, one of the cardinal statistical concepts upon which Machine Learning is based is that correlation isnt tantamount to causation. Part of the pressing need for Explainable AI today is that in the zeal to operationalize these technologies, many users are mistaking correlation for causationwhich is perhaps understandable because aspects of correlation can prove useful for determining causation. In ascending order of importance, an abridged hierarchy of statistical concepts contributing to Explainable AI involves:

Causation is the foundation of Explainable AI. It enables organizations to understand that when given X, they can predict the likelihood of Y. In aircraft repairs, for example, causation between events might empower organizations to know that when a specific part in an engine fails, theres a greater probability for having to replace cooling system infrastructure.

Theres an undeniable temporal element of causation readily illustrated in knowledge graphs so when depicting real-world events, organizations can ascertain which took place first and how it might have affected others. This added temporal dimension is critical in establishing causation between events, such as patients having both HIV and bipolar disorder. In this domain, deep neural networks and other black-box Machine Learning approaches can pinpoint any number of interesting patterns, such as the fact that theres a high co-occurrence of these conditions in patients.

When modeling these events in graph settings alongside other relevant eventslike what erratic decisions individual bi-polar patients made relating to their sexual or substance abuse activitiesthey might differentiate various aspects of correlation. However, the ability to dynamically visualize the sequence of those events to see which took place before what and how that contributed to other events is indispensable to finding causation.

The flexibility of the knowledge graph schema enables organizations to specify the start and end time of events. When leveraging speech recognition technologies in contact centers for Sales opportunities, organizations can model when agents mentioned certain Sales products, how long they talked about them, and the same information for customers. Visual graph mechanisms can depict these events sequentially, so organizations can see which led to what. Without this temporal method, organizations can leverage Machine Learning to specify co-occurrence and correlation between products.

Nevertheless, the ability to traverse these events at various points in time allows them to see which products, services, or customer prototypes generate interest in other offerings. This causation is determinate for increasing the accuracy of machine learning predictions about how to boost sales with this information. As valuable as this capacity is, the more meritorious quality of such causation is that the explanation for these predictions is not only perfectly clear but also able to be visualized.

Causation is the basis for understanding the predictions of Machine Learning models. Knowledge graphs have visualizations enabling organizations to go back and forth in time to see which events are causative to others. This capability is vital to solving the issue of Explainable AI.

Read more: Is Artificial Intelligence the Next Stepping Stone for Web Designers?

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Answering the Question Why: Explainable AI - AiThority