Category Archives: Machine Learning
21 PPC lessons learned in the age of machine learning automation – Search Engine Land
What youre about to read is not actually from me. Its a compilation of PPC-specific lessons learned by those who actually do the work every day in this age of machine learning automation.
Before diving in, a few notes:
Its simple, a machine cannot optimize toward a goal if there isnt enough data to find patterns.
For example, Google Ads may recommend Maximize Conversions as a bid strategy, BUT the budget is small (like sub $2,000/mo) and the clicks are expensive.
In a case like this, you have to give it a Smart Bid strategy goal capable of collecting data to optimize towards.
So a better option might be to consider Maximize Clicks or Search Impression Share. In small volume accounts, that can make more sense.
The key part of machine learningis the second word: learning.
For a machine to learn what works, it must also learn what doesnt work.
That part can be agonizing.
When launching an initial Responsive Search Ad (RSA), expect the results to underwhelm you. The system needs data to learn the patterns of what works and doesnt.
Its important for you to set these expectations for yourself and your stakeholders. A real-life client example saw the following results:
As you can see, month two looked far better. Have the proper expectations set!
Many of us whove been in the industry a while werent taught to manage ad campaigns the way they need to be run now. In fact, it was a completely different mindset.
For example, I was taught to:
Any type of automation relies on proper inputs. Sometimes what would seem to be a simple change could do significant damage to a campaign.
Some of those changes include:
Those are just a few examples, but they all happened and they all messed with a live campaign.
Just remember, all bets are off when any site change happens without your knowledge!
The best advice to follow regarding Recommendations are the following:
Officially defined as the impressions youve received on the Search Network divided by the estimated number of impressions you were eligible to receive, Search Impression Share is basically a gauge to inform you what percentage of the demand you are showing to compete for.
This isnt to imply Search Impression Share is the single most important metric. However, you might implement a smart bidding rule with Performance Max or Maximize Conversions and doing so may negatively impact other metrics (like Search Impression Share).
That alone isnt wrong. But make sure youre both aware and OK with that.
Sometimes things change. Its your job to stay on top of it. For smart bidding, Target CPA no longer exists for new campaigns. Its now merged with Maximize Conversions.
Smart Shopping and Local Campaigns are being automatically updated to Performance Max between July and September 2022. If youre running these campaigns, the best thing you can do is to do the update manually yourself (one click implementation via the recommendations tab in your account).
Why should you do this?
This doesnt need to be complicated. Just use your favorite tool like Evernote, OneNote, Google Docs/Sheets, etc. Include the following for each campaign:
There are three critical reasons why this is a good idea:
Imagine youre setting up a campaign and loading snippets of an ad. Youve got:
Given the above conditions, do you think it would be at all useful to know which combinations performed best? Would it help you to know if a consistent trend or theme emerges? Wouldnt having that knowledge help you come up with even more effective snippets of an ad to test going forward?
Well, too bad because thats not what you get at the moment.
If you run a large volume account with a lot of campaigns, then anytime you can provide your inputs in a spreadsheet for a bulk upload you should do it. Just make sure you do a quality check of any bulk actions taken.
Few things can drag morale down like a steady stream of mundane tasks. Automate whatever you can. That can include:
To an outsider, managing an enterprise level PPC campaign would seem like having one big pile of money to work with for some high-volume campaigns. Thats a nice vision, but the reality is often quite different.
For those who manage those campaigns, it can feel more like 30 SMB accounts. You have different regions with several unique business units (each having separate P&Ls).
The budgets are set and you cannot go over it. Period.
You also need to ensure campaigns run the whole month so you cant run out of budget on the 15th.
Below is an example of a custom budget tracking report built within Google Data Studio that shows the PPC manager how the budget is tracking in the current month:
Devote 10% of your management efforts (not necessarily budget) to trying something new.
Try a beta (if you have access to it), a new smart bidding strategy, new creative snippets, new landing page, call to action, etc.
If you are required (for example by legal, compliance, branding, executives) to always display a specific message in the first headline, you can place a pin that will only insert your chosen copy in that spot while the remainder of the ad will function as a typical RSA.
Obviously if you pin everything, then the ad is no longer responsive. However, it has its place so when you gotta pin, you gotta pin!
Its simple: The ad platform will perform the heavy lifting to test for the best possible ad snippet combinations submitted by you to achieve an objective defined by you.
The platform can either perform that heavy lifting to find the best combination of well-crafted ad snippets or garbage ones.
Bottom line, an RSA doesnt negate the need for skilled ad copywriting.
If youve managed campaigns for an organization in a highly regulated industry (healthcare, finance, insurance, education, etc.) you know all about the legal/compliance review and frustrations that can mount.
Remember, you have your objectives (produce campaigns that perform) and they have theirs (to keep the organization out of trouble).
When it comes to RSA campaigns, do yourself a favor and educate the legal, compliance, and branding teams on:
To use an automotive analogy, think of automation capabilities more like park assist than full self driving.
For example, you set up a campaign to Bid to Position 2 and then just let it run without giving it a second thought. In the meantime, a new competitor enters the market and showing up in position 2 starts costing you a lot more. Now youre running into budget limitations.
Use automation to do the heavy lifting and automate the mundane tasks (Lesson #11), but ignore a campaign once its set up.
This is related to lesson #5 and cannot be overstated.
For example, you may see a recommendation to reach additional customers at a similar cost per conversion in a remarketing campaign. Take a close look at the audiences being recommended as you can quickly see a lot of inflated metrics especially in remarketing.
You have the knowledge of the business far better than any algorithm possibly could. Use that knowledge to guide the machine and ensure it stays pointed in the right direction.
By some accounts, Im mostly referring to low-budget campaigns.
Machine learning needs data and so many smaller accounts dont have enough activity to generate it.
For those accounts, just keep it as manual as you can.
Speak with one of your industry peers, and youll quickly find someone who understands your daily challenges and may have found ways to mitigate them.
Attend conferences and network with people attending the PPC track. Sign up for PPC webinars where tactical campaign management is discussed.
Participate (or just lurk) in social media discussions and groups specific to PPC management.
Many of the mundane tasks (Lesson #11) can be automated now, thus eliminating the need for a person to spend hours on end performing them. Thats a good thing no one really enjoyed doing most of those things anyway.
As more tasks continue toward the path of automation, marketers only skilled at the mundane work will become less needed.
On the flipside, this presents a prime opportunity for strategic marketers to become more valuable. Think about it the machine doing the heavy lifting needs guidance, direction and course corrective action when necessary.
That requires the marketer to:
Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.
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21 PPC lessons learned in the age of machine learning automation - Search Engine Land
Machine learning hiring levels in the pharmaceutical industry rose in June 2022 – Pharmaceutical Technology
The proportion of pharmaceutical companies hiring for machine learning related positions rose in June 2022 compared with the equivalent month last year, with 26.4% of the companies included in our analysis recruiting for at least one such position.
This latest figure was higher than the 24.1% of companies who were hiring for machine learning related jobs a year ago and an increase compared to the figure of 26.3% in May 2022.
When it came to the rate of all job openings that were linked to machine learning, related job postings dropped in June 2022 from May 2022, with 1.2% of newly posted job advertisements being linked to the topic.
This latest figure was the same as the 1.2% of newly advertised jobs that were linked to machine learning in the equivalent month a year ago.
Machine learning is one of the topics that GlobalData, from whom our data for this article is taken, have identified as being a key disruptive force facing companies in the coming years. Companies that excel and invest in these areas now are thought to be better prepared for the future business landscape and better equipped to survive unforeseen challenges.
Our analysis of the data shows that pharmaceutical companies are currently hiring for machine learning jobs at a rate equal to the average for all companies within GlobalData's job analytics database. The average among all companies stood at 1.2% in June 2022.
GlobalData's job analytics database tracks the daily hiring patterns of thousands of companies across the world, drawing in jobs as they're posted and tagging them with additional layers of data on everything from the seniority of each position to whether a job is linked to wider industry trends.
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Machine learning hiring levels in the pharmaceutical industry rose in June 2022 - Pharmaceutical Technology
Deep Learning Laptops We’ve Reviewed (2022) – Analytics India Magazine
As an amateur professional, there are certain key components to focus on while purchasing a laptop for performing deep learning operations such as RAM, CPU, storage and operating system.
Laptops with higher RAM would ensure faster processing while those with GPU provide an additional advantage to speed up the training process and help reduce time from model training. Another essential component for deep learning laptops is graphics card, used to render higher dimensional images.
Here is a detailed list of top laptops for deep learning
Lambda Labs recognises Tensorbook as the Deep Learning Laptop.
Tensorbook is equipped with GeForce RTX 3080 Max-Q 16GB GPU, VRAM-16 GB GDDR6 and is backed by Intel Core i7-11800H along with RAM of 64 GB 3200 MHz DDR4 and storage of 2 TB NVMe PCIe 4.0.
(Image source: Amazon)
According to Lambda Labs, Tensorbooks GeForce RTX 3080 is capable of delivering model training performance up to 4x faster than Apples M1 Max and 10x faster than Google Colab instances. It is also equipped with pre-installed machine learning tools such as PyTorch, Tensorflow, CUDA, and cuDNN.
(Image source: Lambda Labs)
Razer Blade 15 RTX3080
Razer Blade 15 RTX3080 is an equally good choice in terms of deep learning operations.
The laptop is powered by NVIDIA GeForce RTX 3080 Ti along with Intel Core i7-11800H. The Intel Turbo Boost Technology can boost the i7 processor up to 5.1GHz.Go with ultra-fast 360Hz FHD.
(Image source: Amazon)
Razer Blade 15 RTX3080 has a battery life of upto 5 hours.
The laptop efficiently dissipates heat through the evaporation and condensation of an internal fluid and keeps it running soundlessly and coolly even under intense loads owing to features like vapour chamber cooling for maximised thermal performance.
It is a powerhouse laptop with the combination of both NVIDIA and AMD. It is powered by AMD Ryzen 9 5900HX CPU and GeForce RTX 3080 GPU along with an ultrafast panel up to 300 Hz/3ms. It has a 90 Wh battery with rapid Type-c charging with video playback upto 12 hours.
(Image source: Asus)
Dell Inspiron i5577 is equipped with a 7th Generation Intel Quad-core CPU which makes it suitable for CPU-intensive projects.
(Image source: Amazon)
The laptop has NVIDIA GTX 1050 Graphics with 4GB GDDR5 video memory. The user can choose from hard drive options upto 1TB conventional HDD or PCIe NVMe 512 GB SSD for plenty of storage, stability and responsive performance. It is backed by a 6-cell 74Whr battery.
The ASUS ROG Strix G17 laptop is equipped with RTX3070 GPU along with 8GB VRAM and 8-core Ryzen 9 which makes it one of the most suitable laptops for machine learning. It also has a 165Hz 3ms refresh rate and a 90Wh battery which allows usage upto a solid 10 hours.
(Image source: Asus)
The Eluktronics MAX-17 renders itself the lightest 17.3 gaming laptop in the industry. It is powered by Intel Core i7-10870H Eight Cores-16 Threads (2.2-5.0GHz TurboBoost) along with 8GB GDDR6 VRAM NVIDIAGeForce RTX 2070 Super (Max-PTDP:115 Watts).
(Image source: Eluktronics)
In terms of memory and storage configuration, the laptop is equipped with 1TB Ultra Performance PCIe NVMe SSD + 16GB DDR4 2933MHz RAM.
ASUS TUF Gaming F17 is yet another impressive option for deep learning operations. It is powered by the latest 10th Gen Intel Core i7 CPUwith 8 cores and 16 threads to tear through serious gaming, streaming and heavy duty multitasking. It also has GeForce GTX 1650 Ti GPU with IPS-level displays up to 144Hz.
(Image source: Amazon)
The laptop also features a larger 48Wh battery that allows up to 12.3 hours of video playback and upto 7.3 hours of web browsing. In terms of durability, it claims to be equipped with TUFs signature military-grade durability.
The Razer Blade 15 laptop boasts of 11th Gen Intel Core i7-11800H 8 Core (2.3GHz/4.6GHz) and NVIDIA GeForce RTX 3060 along with 6GB DDR6 VRAM.
(Image source: Amazon)
This laptop comes with a built-in 65WHr rechargeable lithium-ion polymer battery that lasts upto 6 hours.
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Deep Learning Laptops We've Reviewed (2022) - Analytics India Magazine
Machine Learning is the Wrong Way to Extract Data From Most Documents – hackernoon.com
Documents have spent decades stubbornly guarding their contents against software. In the late 1960s, the first OCR (optical character recognition) techniques turned scanned documents into raw text. By indexing and searching the text from these digitized documents, software sped up formerly laborious legal discovery and research projects.
Today, Google, Microsoft, and Amazon provide high-quality OCR as part of their cloud services offerings. But documents remain underused in software toolchains, and valuable data languish in trillions of PDFs. The challenge has shifted from identifying text in documents to turning them into structured data suitable for direct consumption by software-based workflows or direct storage into a system of record.
The prevailing assumption is that machine learning, often embellished as AI, is the best way to achieve this, superseding outdated and brittle template-based techniques. This assumption is misguided. The best way to turn the vast majority of documents into structured data is to use the next generation of powerful, flexible templates that find data in a document much as a person would.
The promise of machine learning is that you can train a model once on a large corpus of representative documents and then smoothly generalize to out-of-sample document layouts without retraining. For example, you want to train an ML model on company A, B, and Cs home insurance policies, and then extract the same data from similar documents issued by company Z. This is very difficult to achieve in practice for three reasons:
Your goal is often to extract dozens or hundreds of individual data elements from each document. A model at the document level of granularity will frequently miss some of these values, and those errors are quite difficult to detect. Once your model attempts to extract those dozens or hundreds of data elements from out-of-sample document types, you get an explosion of opportunities for generalization failure.
While some simple documents might have a flat key/value ontology, most will have a substructure:think of a list of deficiencies in a home inspection report or the set of transactions in a bank statement. In some cases youll even encounter complex nested substructures:think of a list of insurance policies, each with a claims history. You either need your machine learning model to infer these hierarchies, or you need to manually parameterize the model with these hierarchies and the overall desired ontology before training.
A document is anything that fits on one or more sheets of paper and contains data! Documents are really just bags of diverse and arbitrary data representations. Tables, labels, free text, sections, images, headers and footers: you name it and a document can use it to encode data. There's no guarantee that two documents, even with the same semantics, will use the same representational tools.
It's no surprise that ML-based document parsing projects can take months, require tons of data up front, lead to unimpressive results, and in general be "grueling" (to directly quote a participant in one such project with a leading vendor in the space).
These issues strongly suggest that the appropriate angle of attack for structuring documents is at the data element level rather than the whole-document level. In other words, we need to extract data from tables, labels, and free text; not from a holistic document. And at the data element level, we need powerful tools to express the relationship between the universe of representational modes found in documents and the data structures useful to software.
So let's get back to templates.
Historically, templates have had an impoverished means of expressing that mapping between representational mode and data structure. For example, they might instruct: go to page 3 and return any text within these box coordinates. This breaks down immediately for any number of reasons, including if:
None of these minor changes to the document layout would faze a human reader.
For software to successfully structure complex documents, you want a solution that sidesteps the battle of months-long ML projects versus brittle templates. Instead, lets build a document-specific query language that (when appropriate) embeds ML at the data element, rather than document, level.
First, you want primitives (i.e., instructions) in the language that describe representational modes (like a label/value pair or repeating subsections) and stay resilient to typical layout variations. For example, if you say:
Find a row starting with this word and grab the lowest dollar amount from it
You want row recognition thats resilient to whitespace variation, vertical jitter, cover pages, and document skew, and you want powerful type detection and filtering.
Second, for data representations with a visual or natural language component, such as tables, checkboxes, and paragraphs of free text, the primitives should embed ML. At this level of analysis, Google, Amazon, Microsoft, and OpenAI all have tools that work quite well off the shelf.
Sensible takes just that approach: blending powerful and flexible templates with machine learning. With SenseML, our JSON-based query language for documents, you can extract structured data from most document layouts in minutes with just a single reference sample. No need for thousands of training documents and months spent tweaking algorithms, and no need to write hundreds of rules to account for tiny layout differences.
SenseMLs wide range of primitives allows you to quickly map representational modes to useful data structures, including complex nested substructures. In cases where the primitives do not use ML, they behave deterministically to provide strong behavior and accuracy guarantees. And even for the non-deterministic output of our ML-powered primitives, such as tables, validation rules can identify errors in the ML output.
What this means is that document parsing with Sensible is incredibly fast, transparent, and flexible. If you want to add a field to a template or fix an error, it's straightforward to do so.
The tradeoff for Sensibles rapid time to value is that each meaningfully distinct document layout requires a separate template. But this tradeoff turns out to be not so bad in the real world. In most business use cases, there are a countable number of layouts (e.g., dozens of trucking carriers generating rate confirmations in the USA; a handful of software systems generating home inspection reports). Our customers dont create thousands of document templates most generate tremendous value with just a few.
Of course, for every widely used tax form, insurance policy, and verification of employment, collectively we only need to create a template once. Thats why weve introduced
Our open-source Sensible Configuration Library is a collection of over 100 of the most frequently parsed document layouts, from auto insurance policies to ACORD forms, loss runs, tax forms, and more. If you have a document that's of broad interest, we'll do the onboarding for you and then make it freely available to the public. It will also be free for you to use for up to 150 extractions per month on our free account tier.
We believe that this hybrid approach is the path to transparently and efficiently solving the problem of turning documents into structured data for a wide range of industries, including logistics, financial services, insurance, and healthcare. If you'd like to join us on this journey and connect your documents to software,schedule a demo or sign up for a free account!
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Machine Learning is the Wrong Way to Extract Data From Most Documents - hackernoon.com
Enko Raises $70M Series C to Commercialize Safe Crop Protection through Machine Learning-based Discovery Technology – PR Newswire
Round led by Nufarm will advance company's digital discovery platform and pipeline of leading crop health molecules
MYSTIC, Conn., July 27, 2022 /PRNewswire/ --Enko, the crop health company, today announced $70 million in Series C funding, bringing the company's overall capital raised to date to $140 million. Global agrochemical company Nufarm led the round as part of an expanded partnership to bring innovative products to their core markets.
Enko will use the new funds to advance its product pipeline of crop protection chemistries that target critical pests and weeds through novel pathways. The funds will also expand Enko's ENKOMPASSTM technology platform, which combines DNA-encoded library screening with machine learning and structure-based design to quickly find new, better performing and more targeted chemistries. Since its start in 2017, Enko has generated hundreds of leading molecules across all categories of crop protection. Enko's product pipeline is currently led by a range of herbicides that are demonstrating breakthrough performance compared to industry standards like glyphosate.
"Reliance on outdated chemistries has led to rampant resistance that is threatening farmer livelihoods and our food supply," said Enko CEO and founder Jacqueline Heard. "Enko's digital platform massively increases the scale and discovery rate for new solutions, screening out off-target organisms from the get-go. The result is bringing safe and effective products to growers better, faster and cheaper. The need for this innovation has never been more urgent."
To move the industry forward amidst stalled R&D, Enko is collaborating withSyngenta andBayer on promising new chemistries. Enko's target-based approach has generated its industry-leading discoveries in roughly half the time and with fewer resources than conventional R&D methods.
On expanding their partnership, Nufarm Managing Director and CEO Greg Hunt said, "We were early investors in Enko and have followed the performance of their pipeline in the lab and field over the last two years with increased interest. As an agricultural innovator, Nufarm's strategy is to partner with like-minded companies who recognize that innovation and technology are the future for sustainable agriculture practices. We were delighted to invest in this Series C financing round."
In addition to Nufarm, its investors include Anterra Capital, Taher Gozal, the Bill & Melinda Gates Foundation, Eight Roads Ventures, Finistere Ventures, Novalis LifeSciences, Germin8 Ventures, TO Ventures Food, Endeavor8, Alumni Ventures Group and Rabo Food & Agri Innovation Fund.
About EnkoEnko designs safe and sustainable solutions to farmers' biggest crop threats today, from pest resistance to new diseases by applying the latest drug discovery and development approaches from pharma to agriculture. Enko is headquartered in Mystic, Connecticut. For more information, visit enkochem.com.
About NufarmNufarm is a global crop protection and seed technology company established over 100 years ago. It is listed on the Australian Securities Exchange (ASX:NUF) with its head office in Melbourne, Australia. As an agricultural innovator, Nufarm is focused on innovative crop protection and seed technology solutions. It has introduced to the market Omega-3 canola and has an expanding portfolio of unique GHG biofuel solutions. Nufarm has manufacturing and marketing operations in Australia, New Zealand, Asia, Europe and North America.
Media ContactsMission North for Enko[emailprotected]
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Enko Raises $70M Series C to Commercialize Safe Crop Protection through Machine Learning-based Discovery Technology - PR Newswire
Global Machine Learning Market is Expected to Grow at a CAGR of 39.2 % by 2028 – Digital Journal
According to the latest research by SkyQuest Technology, the Global Machine Learning Market was valued at US$ 16.2 billion in 2021, and it is expected to reach a market size of US$ 164.05 billion by 2028, at a CAGR of 39.2 % over the forecast period 20222028. The research provides up-to-date Machine Learning Market analysis of the current market landscape, latest trends, drivers, and overall market environment.
Software systems may forecast events more correctly with the use of machine learning (ML), a type of artificial intelligence (AI), without needing to be explicitly told to do so. Machine learning algorithms use historical data as input to anticipate new output values. As organizations adopt more advanced security frameworks, the global machine learning market is anticipated to grow as machine learning becomes a prominent trend in security analytics. Due to the massive amount of data being generated and communicated over several networks, cyber professionals struggle considerably to identify and assess potential cyber threats and assaults.
Machine-learning algorithms can assist businesses and security teams in anticipating, detecting, and recognising cyber-attacks more quickly as these risks become more widespread and sophisticated. For example, supply chain attacks increased by 42% in the first quarter of 2021 in the US, affecting up to 7,000,000 people. For instance, AT&T and IBM claim that the promise of edge computing and 5G wireless networking for the digital revolution will be proven. They have created virtual worlds that, when paired with IBM hybrid cloud and AI technologies, allow business clients to truly experience the possibilities of an AT&T connection.
Computer vision is a cutting-edge technique that combines machine learning and deep learning for medical imaging diagnosis. This has been accepted by the Microsoft InnerEye programme, which focuses on image diagnostic tools for image analysis. For instance, using minute samples of linguistic data, an AI model created by a team of researchers from IBM and Pfizer can accurately forecast the eventual onset of Alzheimers disease in healthy persons by 71 percent (obtained via clinical verbal cognition tests).
Read Market Research Report, Global Machine Learning Market by Component, (Solutions, and Services), Enterprise Size (SMEs And Large Enterprises), Deployment (Cloud, On-Premise), End-User [Healthcare, Retail, IT and Telecommunications, Banking, Financial Services and Insurance (BFSI), Automotive & Transportation, Advertising & Media, Manufacturing, Others (Energy & Utilities, Etc.)], and Region Forecast and Analysis 20222028 By Skyquest
Get Sample PDF : https://skyquestt.com/sample-request/machine-learning-market
Large enterprises segment dominated the machine learning market in 2021. This is because data science and artificial intelligence technologies are being used more often to incorporate quantitative insights into business operations. For instance, under a contract between Pitney Bowes and IBM, IBM will offer managed infrastructure, IT automation, and machine learning services to help Pitney Bowes convert and adopt hybrid cloud computing to support its global business strategy and goals.
Small and midsized firms are expected to grow considerably throughout the anticipated timeframe. It is projected that AI and ML would be the main technologies allowing SMEs to reduce ICT investments and access digital resources. For instance, the IPwe Platform, IPwe Registry, and Global Patent Marketplace are just a few of the small- and medium-sized firms (SMEs) and other organizations that are reportedly already using IPwes technology.
The healthcare sector had the biggest share the global machine learning market in 2021 owing to the industrys leading market players doing rapid research and development, as well as the partnerships formed in an effort to increase their market share. For instance, per the terms of the two businesses signed definitive agreement, Francisco Partners would buy IBMs healthcare data and analytics assets that are presently a part of the Watson Health company. An established worldwide investment company with a focus on working with IT startups is called Francisco Partners. Francisco Partners acquired a wide range of assets, including Health Insights, MarketScan, Clinical Development, Social Program Management, Micromedex, and imaging software services.
The prominent market players are constantly adopting various innovation and growth strategies to capture more market share. The key market players are IBM Corporation, SAP SE, Oracle Corporation, Hewlett Packard Enterprise Company, Microsoft Corporation, Amazon Inc., Intel Corporation, Fair Isaac Corporation, SAS Institute Inc., BigML, Inc., among others.
The report published by SkyQuest Technology Consulting provides in-depth qualitative insights, historical data, and verifiable projections about Machine Learning Market Revenue. The projections featured in the report have been derived using proven research methodologies and assumptions.
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What does this Report Deliver?
SkyQuest has Segmented the Global Machine Learning Market based on Component, Enterprise Size, Deployment, End-User, and Region:
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Key Players in the Global Machine Learning Market
About Us-SkyQuest Technology Group is a Global Market Intelligence, Innovation Management & Commercialization organization that connects innovation to new markets, networks & collaborators for achieving Sustainable Development Goals.
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Global Machine Learning Market is Expected to Grow at a CAGR of 39.2 % by 2028 - Digital Journal
New $10M NSF-funded institute will get to the CORE of data science – EurekAlert
image:The core of EnCORE: co-principal investigators include (from l to r) Yusu Wang, Barna Saha (the principal investigator), Kamalika Chaudhuri, (top row) Arya Mazumdar and Sanjoy Dasgupta. view more
Credit: University of California San Diego
A new National Science Foundation initiative has created a $10 million dollar institute led by computer and data scientists at University of California San DIego that aims to transform the core fundamentals of the rapidly emerging field of Data Science.
Called The Institute for Emerging CORE Methods in Data Science (EnCORE), the institute will be housed in the Department of Computer Science and Engineering (CSE), in collaboration with The Halcolu Data Science Institute (HDSI), and will tackle a set of important problems in theoretical foundations of Data Science.
UC San Diego team members will work with researchers from three partnering institutions University of Pennsylvania, University of Texas at Austin and University of California, Los Angeles to transform four core aspects of data science: complexity of data, optimization, responsible computing, and education and engagement.
EnCORE will join three other NSF-funded institutes in the country dedicated to the exploration of data science through the NSFs Transdisciplinary Research in Principles of Data Science Phase II (TRIPODS) program.
The NSF TRIPODS Institutes will bring advances in data science theory that improve health care, manufacturing, and many other applications and industries that use data for decision-making, said NSF Division Director for Electrical, Communications and Cyber Systems Shekhar Bhansali.
UC San Diego Chancellor Pradeep K. Khosla said UC San Diegos highly collaborative, multidisciplinary community is the perfect environment to launch and develop EnCORE. We have a long history of successful cross-disciplinary collaboration on and off campus, with renowned research institutions across the nation. UC San Diego is also home to the San Diego Supercomputer Center, the HDSI, and leading researchers in artificial intelligence and machine learning, Khosla said. We have the capacity to house and analyze a wide variety of massive and complex data sets by some of the most brilliant minds of our time, and then share that knowledge with the world.
Barna Saha, the EnCORE project lead and an associate professor in UC San Diegos Department of Computer Science and Engineering and HDSI, said: We envision EnCORE will become a hub of theoretical research in computing and Data Science in Southern California. This kind of national institute was lacking in this region, which has a lot of talent. This will fill a much-needed gap.
The other UC San Diego faculty members in the institute include professors Kamalika Chaudhury, and Sanjoy Dasgupta from CSE; Arya Mazumdar (EnCORE co-principal investigator), Gal Mishne, and Yusu Wang from HDSI; and Fan Chung Graham from Mathematics. Saura Naderi of HDSI will spearhead the outreach activities of the institute.
Professor Barna Saha has assembled a team of exceptional scholars across UC San Diego and across the nation to explore the underpinnings of data science. This kind of institute, focused on groundbreaking research, innovative education and effective outreach, will be a model of interdisciplinary initiatives for years to come, said Department of Computer Science and Engineering Chair Sorin Lerner.
CORE Pillars of Data Science
The EnCORE Institute seeks to investigate and transform three research aspects of Data Science:
EnCORE represents exactly the kind of talent convergence that is necessary to address the emerging societal need for responsible use of data. As a campus hub for data science, HDSI is proud of a compelling talent pool to work together in advancing the field, said HDSI founding director Rajesh K. Gupta.
Team members expressed excitement about the opportunity of interdisciplinary research that the institute will provide. They will work together to improve privacy-preserving machine learning and robust learning, and to integrate geometric and topological ideas with algorithms and machine learning methodologies to tame the complexity in modern data. They envision a new era in optimization with the presence of strong statistical and computational components adding new challenges.
One of the exciting research thrusts at EnCORE is data science for accelerating scientific discoveries in domain sciences, said Gal Mishne, a professor at HDSI. As part of EnCORE, the team will be developing fast, robust low-distortion visualization tools for real-world data in collaboration with domain experts. In addition, the team will be developing geometric data analysis tools for neuroscience, a field which is undergoing an explosion of data at multiple scales.
From K-12 and Beyond
A distinctive aspect to EnCORE will be the E, education and engagement, component.
The institute will engage students at all levels, from K-12 to postdoctoral students, and junior faculty and conduct extensive outreach activities at all of its four sites.
The geographic span of the institute in three regions of the United States will be a benefit as the institute executes its outreach plan, which includes regular workshops, events, hiring of students and postdoctoral students. Online and joint courses between the partner institutions will also be offered.
Activities to reach out to high school, middle school and elementary students in Southern California are also part of the institutes plan, with the first engagement planned for this summer with the Sweetwater Union High School District to teach students about the foundations of data science.
There will also be mentorship and training opportunities with researchers affiliated with EnCORE, helping to create a pipeline of data scientists and broadening the reach and impact of the field. Additionally, collaboration with industry is being planned.
Mazumdar, an associate professor in the HDSI and an affiliated faculty member in CSE, said the team has already put much thought and effort into developing data science curricula across all levels. We aim to create a generation of experts while being mindful of the needs of society and recognizing the demands of industry, he said.
We have made connections with numerous industry partners, including prominent data science techs and also with local Southern California industries including start-ups, who will be actively engaged with the institute and keep us informed about their needs, Mazumdar added.
An interdisciplinary, diverse field- and team
Data science has footprints in computer science, mathematics, statistics and engineering. In that spirit, the researchers from the four participating institutions who comprise the core team have diverse and varied backgrounds from four disciplines.
Data science is a new, and a very interdisciplinary area. To make significant progress in Data Science you need expertise from these diverse disciplines. And its very hard to find experts in all these areas under one department, said Saha. To make progress in Data Science, you need collaborations from across the disciplines and a range of expertise. I think this institute will provide this opportunity.
And the institute will further diversity in science, as EnCORE is being spearheaded by women who are leaders in their fields.
Continued here:
New $10M NSF-funded institute will get to the CORE of data science - EurekAlert
The imperative need for machine learning in the public sector – VentureBeat
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The sheer number of backlogs and delays across the public sector are unsettling for an industry designed to serve constituents. Making the news last summer was the four-month wait period to receive passports, up substantially from the pre-pandemic norm of 6-8 weeks turnaround time. Most recently, the Internal Revenue Service (IRS) announced it entered the 2022 tax season with 15 times the usual amount of filing backlogs, alongside its plan for moving forward.
These frequently publicized backlogs dont exist due to a lack of effort. The sector has made strides with technological advancements over the last decade. Yet, legacy technology and outdated processes still plague some of our nations most prominent departments. Todays agencies must adopt digital transformation efforts designed to reduce data backlogs, improve citizen response times and drive better agency outcomes.
By embracing machine learning (ML) solutions and incorporating advancements in natural language processing (NLP), backlogs can be a thing of the past.
Whether tax documents or passport applications, processing items manually takes time and is prone to errors on the sending and receiving sides. For example, a sender may mistakenly check an incorrect box or the receiver may interpret the number 5 as the letter S. This creates unforeseen processing delays or, worse, inaccurate outcomes.
But managing the growing government document and data backlog problem is not as simple and clean-cut as uploading information to processing systems. The sheer number of documents and citizens information entering agencies in varied unstructured data formats and states, often with poor readability, make it nearly impossible to reliably and efficiently extract data for downstream decision-making.
Embracing artificial intelligence (AI) and machine learning in daily government operations, just as other industries have done in recent years, can provide the intelligence, agility and edge needed to streamline processes and enable end-to-end automation of document-centric processes.
Government agencies must understand that real change and lasting success will not come with quick patchworks built upon legacy optical character recognition (OCR) or alternative automation solutions, given the vast amount of inbound data.
Bridging the physical and digital worlds can be attained with intelligent document processing (IDP), which leverages proprietary ML models and human intelligence to classify and convert complex, human-readable document formats. PDFs, images, emails and scanned forms can all be converted into structured, machine-readable information using IDP. It does so with greater accuracy and efficiency than legacy alternatives or manual approaches.
In the case of the IRS, inundated with millions of documents such as 1099 forms and individuals W-2s, sophisticated ML models and IDP can automatically identify the digitized document, extract printed and handwritten text, and structure it into a machine-readable format. This automated approach speeds up processing times, incorporates human support where needed and is highly effective and accurate.
Alongside automation and IDP, introducing ML and NLP technologies can significantly support the sectors quest to improve processes and reduce backlogs. NLP isan area of computer science that processes and understands text and spoken words like humans do, traditionally grounded in computational linguistics, statistics and data science.
The field has experienced significant advancements, like the introduction of complex language models that contain more than 100 billion parameters. These models could power many complex text processing tasks, such as classification, speech recognition and machine translation. These advancements could support even greater data extraction in a world overrun by documents.
Looking ahead, NLP is on course to reach the level of text understanding capability similar to that of a human knowledge worker, thanks to technological advancements driven by deep learning.Similar advancements in deep learning also enable the computer to understand and process other human-readable content such as images.
For the public sector specifically, this could be images included in disability claims or other forms or applications consisting of more than just text. These advancements could also improve downstream stages of public sector processes, such as ML-powered decision-making for agencies determining unemployment assistance, Medicaid insurance and other invaluable government services.
Though weve seen a handful of promising digital transformation improvements, the call for systemic change has yet to be fully answered.
Ensuring agencies go beyond patching and investing in various legacy systems is needed to move forward today. Patchwork and investments in outdated processes fail to support new use cases, are fragile to change and cannot handle unexpected surges in volume. Instead, introducing a flexible solution that can take the most complex, difficult-to-read documents from input to outcome should be a no-brainer.
Why? Citizens deserve more out of the agencies who serve them.
CF Su is VP of machine learning at Hyperscience.
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The imperative need for machine learning in the public sector - VentureBeat
This robot dog just taught itself to walk – MIT Technology Review
The teams algorithm, called Dreamer, uses past experiences to build up a model of the surrounding world. Dreamer also allows the robot to conduct trial-and-error calculations in a computer program as opposed to the real world, by predicting potential future outcomes of its potential actions. This allows it to learn faster than it could purely by doing. Once the robot had learned to walk, it kept learning to adapt to unexpected situations, such as resisting being toppled by a stick.
Teaching robots through trial and error is a difficult problem, made even harder by the long training times such teaching requires, says Lerrel Pinto, an assistant professor of computer science at New York University, who specializes in robotics and machine learning. Dreamer shows that deep reinforcement learning and world models are able to teach robots new skills in a really short amount of time, he says.
Jonathan Hurst, a professor of robotics at Oregon State University, says the findings, which have not yet been peer-reviewed, make it clear that reinforcement learning will be a cornerstone tool in the future of robot control.
Removing the simulator from robot training has many perks. The algorithm could be useful for teaching robots how to learn skills in the real world and adapt to situations like hardware failures, Hafner saysfor example, a robot could learn to walk with a malfunctioning motor in one leg.
The approach could also have huge potential for more complicated things like autonomous driving, which require complex and expensive simulators, says Stefano Albrecht, an assistant professor of artificial intelligence at the University of Edinburgh. A new generation of reinforcement-learning algorithms could super quickly pick up in the real world how the environment works, Albrecht says.
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This robot dog just taught itself to walk - MIT Technology Review
How Data Has Changed the World of HR – ADP
In this "On the Job" segment from Cheddar News, Amin Venjara, General Manager of Data Solutions at ADP, describes the importance of data and how human resources leaders are relying on real-time access to data now more than ever. Venjara offers real-world examples of data's impact on the top challenges faced by organizations today.
Businesses big and small have been utilizing the latest tech and innovation to make the new remote and hybrid working environments possible.
Speaking with Cheddar News, above, Amin Venjara (AV), says relying on quality and accessible data to take action is how today's HR teams are impacting the modern workforce.
Q: How does data influence the role of human resources (HR)?
AV: The last few years have thrust HR teams into the spotlight. Think about all the changes we've seen managing the onset of the pandemic, the return to the workplace, the great resignation and all the challenges that's brought and even the increased focus on diversity, equity and inclusion. HR has been at the focal point of responding to these challenges. And in response, we've seen an uptick in the use of workforce analytics and benchmarking. HR teams need the data to be able to help make decisions in real time as things are changing. And they're using it with the executives and managers they support to make data-driven decisions.
Q: Clearly data-driven solutions are critical in today's workforce as you've been discussing, where has data made the most significant impact?
AV: When we talk to employers, we continuously hear about four key areas related to their workforce: attracting top talent, retaining and engaging talent, optimizing labor costs, and fostering a diverse, equitable and inclusive workforce.
To give an example of the kind of impact that data can have. We have a product that helps organizations calculate and take action on pay equity. They can see gaps by gender and race ethnicity and based on internal and market data. Over 70% of active clients using this tool are seeing a decrease in pay equity gaps. If you look at the size of this - they're spending over a billion dollars to close those gaps. That's not just analytics and data - that's taking action. So, think about the impact that has on the message about equal pay for equal work. And also, the impact it has on productivity, and the lives of those individual workers and their families.
Q: In today's tight talent market, employers increasingly need help recruiting and even retaining workers. How can data and machine learning alleviate some of those very pressing challenges?
AV: Here's an interesting thing about what's happening in the current labor market. U.S. employment numbers are back to pre-pandemic levels with 150 million workers on the payroll. However, we're at the lowest unemployed workers to jobs openings rate we've seen in over 15 years. To put it simply, it's a candidate's market out there; and jobs are chasing workers.
Two things to keep in mind: employers have to employ data-driven strategies to be competitive. So we're seeing with labor markets changing, remote work, hybrid work, expectations on pay and even the physical locations of workers people have moved a lot. Employers need access to real-time data, accurate data on supply and demand of labor and on compensation to hire the right workers and keep the ones they have.
The second thing is really about the adoption of machine learning in recruiting workflows. We're seeing machine learning being adopted in chatbots for personalizing the experience and even helping with scheduling, but also AI-based algorithms to help score candidate profiles against jobs. Overall, the best organizations are combing technology and data with their recruiting and hiring managers to decrease the overall time to fill open jobs.
Q: Becoming data confident might be a concern or even perhaps intimidating for some, but what's an example of how an organization can use data well?
AV: A lot of organizations are trying to make this happen. We recently worked with a quick service restaurant with about 2,000 locations across the U.S. In light of the supply chain challenges and demographic shifts of the last couple of years, they wanted to know how to combine and optimize the supply at each location based on expected demand.
Their research enabled them to correlate demographics, things like age, income and even family status to items on the menu like salads, sandwiches and kids' meals. But what they needed was a stronger signal on what's happening in the local context of each location. They had used internal data for so long, but things had shifted. By using our monthly anonymized and aggregated data from nearly 20% of the workforce, they were able to optimize their demand forecasting models and increase their supply chain efficiency. There are two lessons to think about. They had a key strategic problem, and they worked backwards from that. That's a key piece of becoming data confident - focusing on something that matters and making a data-driven decisions about it. The second is about going beyond the four walls of your organization. There are so many different and new sources of data available due to the digitization of our economy. In order to lock the insight and the strength of signal you need you really need to look for the best sources to get there.
Q: How do you see the role of data evolving as we look toward the future of work?
AV: Data has really come the language of business right now. I see a couple of trends as we look out. The first is the acceleration of data in the flow of work. When you look at a lot of organizations today, when people need data, they have to go to a reporting group or a business intelligence group to request the data. Then it takes a couple cycles to get it right and then make a decision. The cycle time can be high.
What I expect to see now is data more and more in the flow of work where business decision makers are working immediately; they have the right data at their fingertips. You see that across domains. Second is just the separation between haves and have nots. With the increasing speed of change, data haves are going to be able to outstrip data have nots. Those who have invested in building the right organizational, technical, and cultural muscle will see the spoils of this in the years to come.
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How Data Has Changed the World of HR - ADP