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

If you are a major manufacturer in 2020 and you have employed the likes of Deloitte, McKinsey or PWC, it is safe to assume that they have advised you to invest big in artificial intelligence and machine learning.

According to reports by Deloitte and McKinsey, machine learning improves product quality and has the potential to double cash flow. Lets take a look at three global manufacturers who are already on board.

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Siemens is the largest industrial manufacturer in Europe, and whether they are putting together planes, trains or automobiles, their goal is to solve production challenges efficiently and sustainably. One of the ways they are able to do this is by using machine learning (ML) to enhance additive manufacturing, otherwise known as AM.

The process involves putting together parts that make objects from 3D model data. The idea is to streamline the manufacturing process into one printing stage. Machine learning plays a crucial part in achieving this goal.

Lets take a look at the recent creation of the AM Path Optimizer, part of its NX software offering. Its designed to eliminate overheating during production, an issue that stands in the way of the industrialization of AM. According to Siemens, the path optimizer combines simulation technology and ML to analyze a full job file minutes before execution on the machine. With this they hope to achieve reduced scrap and increased production yields. In short, they want to minimize trial and error and get it right the first time around.

Although still in the beta stage, the AM Path Optimizer has had some early adopters. TRUMPF, a German industrial machine manufacturing company based in Stuttgart, has been singing its praises, pointing to improved geometrical accuracy, more homogenous surface quality and a significant reduction in the scrap rate expected.

Machine learning and artificial intelligence do not just influence how companies manufacture but also help them decide what they manufacture. American packaged-food company ConAgra is one such company. They are using AI to identify consumer preferences.

The vegan market, for example, is growing rapidly: by 2026 it is projected to be worth just over $24 billion (the vegan cheese market alone will be worth $4 billion). And ConAgra, despite being over a century old, is aware of consumer preferences moving towards healthier options and away from things like processed meat. This awareness comes in part from their AI platform, which analyses data from social media and consumer food purchasing behavior.

This has led the company to produce alternative meat products like veggie burgers and even cauliflower rice. Its also helped speed up the manufacturing process, so rather than planning for next year, they can design, make, and release a new product in as little as a few weeks.

The major appliance manufacturer Bosch is a great believer in AI and has committed substantial resources to making it a central part of its business. In 2016, it launched a $30,000 competition on Kaggle, an online community of data scientists and machine learning practitioners. Competitors were asked to predict internal failures, with the aim of improving Bosch production line performance.

They described the assembly process as much like a souffle, delicious, delicate and a challenge to prepare; if it comes out of the oven sunken, you are going to retrace your steps to see where things went wrong. In order to identify and predict where its souffles go wrong, Bosch records data at every step of the manufacturing process and assembly line.

This is where the Kagglers come in. With access to advanced data analytics and using thousands of tests and measurements for each component on the assembly line, the winners Ash and Beluga were able to so solve internal failures using their own fault detection method.

In 2017, the Bosch Center for AI was founded with the tagline Solutions created for life. This is part of a broader effort to put AI and machine learning at the heart of the business. What they are working on now is reducing reliance on human expert knowledge base and deploying AI algorithms in safety-critical applications.

More recently, Bosch has been working on preventing increasingly advanced hackers from compromising their cars. According to CTO Michael Bolle: In the area of machine learning and AI, products and machines learn from data, and so the data itself can be part of the attack surface.

SEE ALSO: How machine learning is changing business communications

What Bosch, ConAgra, and Siemens realize is that their business is increasingly reliant on data, and the best way to harness that data is to invest heavily in AI and ML. According to McKinsey, not investing in AI or ML is not really an option, especially if you are a manufacturer with heavy assets: Manufacturers with heavy assets that are unable to read, interpret, and use their own machine-generated data to improve performance by addressing the changing needs of customers and suppliers will quickly lose out to their competitors or be acquired.

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3 global manufacturing brands at the forefront of AI and ML - JAXenter

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

Deep learning chips are customized Silicon chips that integrate AI technology and machine learning. Deep learning and machine learning, which are the sub-sets of Artificial Intelligence (AI) sub-sets, are used in carrying out AI related tasks.

New York, March 20, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Deep Learning Chipset Market By type By Technology By End User By Region, Industry Analysis and Forecast, 2019 - 2025" - https://www.reportlinker.com/p05876895/?utm_source=GNW Deep learning technology has entered many industries around the world and is accomplished through applications like computer vision, speech synthesis, voice recognition, machine translation, drug discovery, game play, and robotics.

The widespread adoption of artificial intelligence (AI) for practical business applications has brought in a range of complexities and risk factors in virtually every industry, but one thing is certain: in todays AI industry, hardware is the key to solving many of the main problems facing the sector, and chipsets are at the heart of that hardware solution. Considering AIs widespread applicability, its almost certain that every chip will have some kind of AI system embedded in future. The engine could make a wide range of forms, from a basic AI library running on a CPU to more complex, custom hardware. The potential for AI is better fulfilled when the chipsets are designed to provide the adequate amount of computing capacity for different AI applications at the right power budget. This is a trend that leads to increased specialization and diversifying of AI-optimized chipsets.

The factors influencing the development of the deep learning chipset market are increased acceptance of cloud-based technology and profound use of learning in big data analytics. A single-chip processor generates lighting effects and transforms objects each time a 3D scene is redrawn, or a graphic processing unit turns out to be very meaningful and efficient when applied to computation styles needed for neural nets. This in turn fuels the growth of the market for deep learning chipsets.

Based on type, the market is segmented into GPU, ASIC, CPU, FPGA and Others. Based on Technology, the market is segmented into System-on-chip (SoC), System-in-package (SIP) and Multi-chip module & Others. Based on End User, the market is segmented into Consumer Electronics, Industrial, Aerospace & Defense, Healthcare, Automotive and Others. Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa.

The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix, Google, Inc., Microsoft Corporation, Samsung Electronics Co., Ltd., Intel Corporation, Amazon.com, Inc., and IBM Corporation are some of the forerunners in the Deep Learning Chipset Market. Companies such as Advanced Micro Devices, Inc., Qualcomm, Inc., Nvidia Corporation, and Xilinx, Inc. are some of the key innovators in Deep Learning Chipset Market. The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Samsung Electronics Co., Ltd. (Samsung Group), Microsoft Corporation, Intel Corporation, Nvidia Corporation, IBM Corporation, Google, Inc., Amazon.com, Inc. (Amazon Web Services), Qualcomm, Inc., Advanced Micro Devices, Inc., and Xilinx, Inc.

Recent strategies deployed in Deep Learning Chipset Market

Partnerships, Collaborations, and Agreements:

Jan-2020: Xilinx collaborated with Telechips, a leading Automotive System on Chip (SoC) supplier. The collaboration would provide a comprehensive solution for addressing the integration of in-cabin monitoring systems (ICMS) and IVI systems.

Dec-2019: Samsung Electronics teamed up with Baidu, a leading Chinese-language Internet search provider. Under the collaboration, the companies announced that the development of Baidu KUNLUN, its first cloud-to-edge AI accelerator has been completed. KUNLUN chip provides 512 gigabytes per second (Gbps) memory bandwidth and offers up to 260 Tera operations per second (TOPS) at 150 watts.

Oct-2019: Microsoft announced technology collaboration with Nvidia, a technology company. The collaboration was focused on intelligent edge computing, which is designed for helping the industries in gaining and managing the insights from the data created by warehouses, retail stores, manufacturing facilities, urban infrastructure, connected buildings, and other environments.

Oct-2019: Microsoft launched Lakefield, a dual-screen device powered by Intels unique processor. This device combines a hybrid CPU with Intels Foveros 3D packaging technology. This provides more flexibility to device makers for innovating designs, experience, and form factor.

Jun-2019: AMD came into partnership with Samsung following which, the former company is licensing its graphics technology to Samsung for use in future mobile chips. Under this partnership, Samsung paid AMD for getting access to its RDNA graphics architecture.

Jun-2019: Nvidia collaborated with Volvo for developing artificial intelligence that is used in self-driving trucks.

May-2019: Samsung Electronics came into partnership with Efinix, an innovator in programmable product platforms and technologies. Under this partnership, the companies were aimed at developing Quantum eFPGAs on Samsungs 10nm silicon process.

Dec-2018: IBM extended its partnership with Samsung for developing 7-nanometer (nm) microprocessors for IBM Power Systems, LinuxONE, and IBM Z. The expansion was aimed at driving the performance of the unmatched system including encryption and compression speed, acceleration, memory, and I/O bandwidth, as well as system scaling.

Jun-2018: AWS announced its collaboration with Cadence Design Systems. The collaboration was aimed at delivering a Cadence Cloud portfolio to electronic systems and semiconductor design.

Mar-2018: Nvidia came into partnership with Arm for bringing deep learning interface to billions of consumer electronics, mobile, and Internet of Things devices.

Acquisition and Mergers:

Aug-2019: Xilinx took over Solarflare, a provider of high-performance, low latency networking solutions. The acquisition helped in generating more revenues and enabled new marketing and R&D funds for the future.

Apr-2019: Intel completed the acquisition of Omnitek, a provider of video and vision field-programmable gate array (FPGA). Through the acquisition, the FPGA processor business of the company has been doubled.

Jul-2018: Intel took over eASIC, a fabless semiconductor company. The acquisition bolstered the companys business in providing chips.

Apr-2017: AMD acquired Nitero, a company engaged in providing technology to connect VR headsets wirelessly to PCs. The acquisition helped the company in getting control over VR experiences.

Product Launches and Product Expansions:

Dec-2019: Nvidia launched Drive AGX Orin, a new Orin AI processor or system-on-chip (SoC). This processor improves power efficiency and performance. This processor is used in evolving the automotive business.

Dec-2019: AWS unveiled Graviton2, the next-generation of its ARM processors. It is a custom chip that is designed with 7nm architecture and based on 64-bit ARM Neoverse cores.

Nov-2019: AMD launched two new Threadripper 3 CPUs with 24 and 32 cores. Both these CPUs will be integrated into AMDs new TRX40 platform using the new sTRX4 socket.

Nov-2019: Intel unveiled Ponte Vecchio GPUs, a graphics processing unit (GPU) architecture. This chip was designed for handling the artificial intelligence loads and heavy data in the data center.

Nov-2019: Intel launched Stratix 10 GX 10M, a new FPGA. This consists of two large FPGA dies and four transceiver tiles and has a total of 10.2 million logic elements and 2304 user I/O pins.

Oct-2018: Google launched TensorFlow, the popular open-source artificial intelligence framework. This framework runs deep learning, machine learning, and other predictive and statistical analytics workloads. This simplifies training models, the process of acquiring data, refining future results, and serving predictions.

Sep-2019: AWS released Amazon EC2 G4 GPU-powered Amazon Elastic Compute Cloud (Amazon EC2) instances. It delivers up to 1.8 TB of local NVMe storage and up to 100 Gbps of networking throughput to AWS custom Intel Cascade Lake CPUs and NVIDIA T4 GPUs.

Aug-2019: Xilinx released Virtex UltraScale+ VU19P, a 16nm device with 35 billion transistors. It has four chips on an interposer. It is the worlds largest field-programmable gate array (FPGA) and has 9 million logic cells.

May-2019: Nvidia introduced NVIDIA EGX, an accelerated computing platform. This platform was aimed at allowing the companies in performing low-latency AI at the edge for perceiving, understanding, and acting in real-time on continuous streaming data between warehouses, factories, 5G base stations, and retail stores.

Nov-2018: AWS introduced Inferentia and Elastic Inference, two chips and 13 machine learning capabilities and services. Through these launches, the company aimed towards attracting more developers.

Sep-2018: Qualcomm unveiled Snapdragon Wear 3100 chipset. This chipset is used in smartwatches and has extended battery life.

Aug-2018: AMD introduced B450 chipset for Ryzen processors. The chip runs about 2 watts lower in power than B350 chipset.

Jul-2018: Google introduced Tensor Processing Units or TPUs, the specialized chips. This chip lives in data centers of the company and simplifies the AI tasks. These chips are used in enterprise jobs.

Apr-2018: Qualcomm launched QCS605 and QCS603 SoCs, two new system-on-chips. These chips combine image signal processor, CPU, AI, GPU technology for accommodating several camera applications, smart displays, and robotics.

Scope of the Study

Market Segmentation:

By Compute Capacity

High

Low

By Type

GPU

ASIC

CPU

FPGA

Others

By Technology

System-on-chip (SoC)

System-in-package (SIP)

Multi-chip module & Others

By End User

Consumer Electronics

Industrial

Aerospace & Defense

Healthcare

Automotive

Others

By Geography

North America

o US

o Canada

o Mexico

o Rest of North America

Europe

o Germany

o UK

o France

o Russia

o Spain

o Italy

o Rest of Europe

Asia Pacific

o China

o Japan

o India

o South Korea

o Singapore

o Malaysia

o Rest of Asia Pacific

LAMEA

o Brazil

o Argentina

o UAE

o Saudi Arabia

o South Africa

o Nigeria

o Rest of LAMEA

Companies Profiled

Samsung Electronics Co., Ltd. (Samsung Group)

Microsoft Corporation

Intel Corporation

Nvidia Corporation

IBM Corporation

Google, Inc.

Amazon.com, Inc. (Amazon Web Services)

Qualcomm, Inc.

Advanced Micro Devices, Inc.

More:
The Global Deep Learning Chipset Market size is expected to reach $24.5 billion by 2025, rising at a market growth of 37% CAGR during the forecast...