Category Archives: Machine Learning

Misinformation has always been a problem, but the combination of widespread social media as well as a loose definition of what can be seen as factual truth in recent times has lead to a veritable explosion in misinformation over the course of the past few years. The problem is so dire that in a lot of cases websites are made specifically because of the fact that this is the sort of thing that could potentially end up allowing misinformation to spread more easily, and this is a problem that might just have been addressed by a new machine learning tool.

This machine learning tool was developed by researchers at UCL, Berkeley and Cornell will be able to detect domain registration data and use this to ascertain whether the URL is legitimate or if it has been made specifically to legitimize a certain piece of information that people might be trying to spread around. A couple of other factors also come into play here. For example, if the identity of the person that registered the domain is private, this might be a sign that the site is not legitimate. The timing of the domain registration matters to. If it was done around the time a major news event broke out, such as the recent US presidential election, this is also a negative sign.

With all of that having been said and out of the way, it is important to note that this new machine learning tool has a pretty impressive success rate of about 92%, which is the proportion of fake domains it was able to discover. Being able to tell whether or not a news source is legitimate or whether it is direct propaganda is useful because of the fact that it can help reduce the likelihood that people might just end up taking the misinformation seriously.

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This New Machine Learning Tool Might Stop Misinformation - Digital Information World

Learning Curve

Artificial intelligence has been a hot technology area in recent years and machine learning, a subset of AI, is one of the most important segments of the whole AI arena.

Machine learning is the development of intelligent algorithms and statistical models that improve software through experience without the need to explicitly code those improvements. A predictive analysis application, for example, can become more accurate over time through the use of machine learning.

But machine learning has its challenges. Developing machine-learning models and systems requires a confluence of data science, data engineering and development skills. Obtaining and managing the data needed to develop and train machine-learning models is a significant task. And implementing machine-learning technology within real-world production systems can be a major hurdle.

Heres a look at a dozen startup companies, some that have been around for a few years and some just getting off the ground, that are addressing the challenges associated with machine learning.

AI.Reverie

Top Executive: Daeil Kim, Co-Founder, CEO

Headquarters: New York

AI.Reverie develops AI and machine -earning technology for data generation, data labeling and data enhancement tasks for the advancement of computer vision. The companys simulation platform is used to help acquire, curate and annotate the large amounts of data needed to train computer vision algorithms and improve AI applications.

In October AI.Reverie was named a Gartner Cool Vendor in AI core technologies.

Anodot

Top Executive: David Drai, Co-Founder, CEO

Headquarters: Redwood City, Calif.

Anodots Deep 360 autonomous business monitoring platform uses machine learning to continuously monitor business metrics, detect significant anomalies and help forecast business performance.

Anodots algorithms have a contextual understanding of business metrics, providing real-time alerts that help users cut incident costs by as much as 80 percent.

Anodot has been granted patents for technology and algorithms in such areas as anomaly score, seasonality and correlation. Earlier this year the company raised $35 million in Series C funding, bringing its total funding to $62.5 million.

BigML

Top Executive: Francisco Martin, Co-Founder, CEO

Headquarters: Corvallis, Ore.

BigML offers a comprehensive, managed machine-learning platform for easily building and sharing datasets and data models, and making highly automated, data-driven decisions. The companys programmable, scalable machine -earning platform automates classification, regression, time series forecasting, cluster analysis, anomaly detection, association discovery and topic modeling tasks.

The BigML Preferred Partner Program supports referral partners and partners that sell BigML and oversee implementation projects. Partner A1 Digital, for example, has developed a retail application on the BigML platform that helps retailers predict sales cannibalizationwhen promotions or other marketing activity for one product can lead to reduced demand for other products.

Carbon Relay

Top Executive: Matt Provo, Founder, CEO

Headquarters: Cambridge, Mass.

Carbon Relay provides machine learning and data science software that helps organizations optimize application performance in Kubernetes.

The startups Red Sky Ops makes it easy for DevOps teams to manage a large variety of application configurations in Kubernetes, which are automatically tuned for optimized performance no matter what IT environment theyre operating in.

In February the company said that it had raised $63 million in a funding round from Insight Partners that the company will use to expand its Red Sky Ops AIOps offering.

Comet.ML

Top Executive: Gideon Mendels, Co-Founder, CEO

Headquarters: New York

Comet.ML provides a cloud-hosted machine-learning platform for building reliable machine-learning models that help data scientists and AI teams track datasets, code changes, experimentation history and production models.

Launched in 2017, Comet.ML has raised $6.8 million in venture financing, including $4.5 million in April 2020.

Dataiku

Top Executive: Florian Douetteau, Co-Founder, CEO

Headquarters: New York

Dataikus goal with its Dataiku DSS (Data Science Studio) platform is to move AI and machine-learning use beyond lab experiments into widespread use within data-driven businesses. Dataiku DSS is used by data analysts and data scientists for a range of machine-learning, data science and data analysis tasks.

In August Dataiku raised an impressive $100 million in a Series D round of funding, bringing its total financing to $247 million.

Dataikus partner ecosystem includes analytics consultants, service partners, technology partners and VARs.

DotData

Top Executive: Ryohei Fujimaki, Founder, CEO

Headquarters: San Mateo, Calif.

DotData says its DotData Enterprise machine-learning and data science platform is capable of reducing AI and business intelligence development projects from months to days. The companys goal is to make data science processes simple enough that almost anyone, not just data scientists, can benefit from them.

The DotData platform is based on the companys AutoML 2.0 engine that performs full-cycle automation of machine-learning and data science tasks. In July the company debuted DotData Stream, a containerized AI/ML model that enables real-time predictive capabilities.

Eightfold.AI

Top Executive: Ashutosh Garg, Co-Founder, CEO

Headquarters: Mountain View, Calif.

Eightfold.AI develops the Talent Intelligence Platform, a human resource management system that utilizes AI deep learning and machine-learning technology for talent acquisition, management, development, experience and diversity. The Eightfold system, for example, uses AI and ML to better match candidate skills with job requirements and improves employee diversity by reducing unconscious bias.

In late October Eightfold.AI announced a $125 million Series round of financing, putting the startups value at more than $1 billion.

H2O.ai

Top Executive: Sri Ambati, Co-Founder, CEO

Headquarters: Mountain View, Calif.

H2O.ai wants to democratize the use of artificial intelligence for a wide range of users.

The companys H2O open-source AI and machine-learning platform, H2O AI Driverless automatic machine-learning software, H20 MLOps and other tools are used to deploy AI-based applications in financial services, insurance, health care, telecommunications, retail, pharmaceutical and digital marketing.

H2O.ai recently teamed up with data science platform developer KNIME to integrate Driverless AI for AutoMl with KNIME Server for workflow management across the entire data science life cyclefrom data access to optimization and deployment.

Iguazio

Top Executive: Asaf Somekh, Co-Founder, CEO

Headquarters: New York

The Iguazio Data Science Platform for real-time machine learning applications automates and accelerates machine-learning workflow pipelines, helping businesses develop, deploy and manage AI applications at scale that improve business outcomeswhat the company calls MLOps.

In early 2020 Iguazio raised $24 million in new financing, bringing its total funding to $72 million.

OctoML

Top Executive: Luis Ceze, Co-Founder, CEO

Headquarters: Seattle

OctoMLs Software-as-a-Service Octomizer makes it easier for businesses and organizations to put deep learning models into production more quickly on different CPU and GPU hardware, including at the edge and in the cloud.

OctoML was founded by the team that developed the Apache TVM machine-learning compiler stack project at the University of Washingtons Paul G. Allen School of Computer Science & Engineering. OctoMLs Octomizer is based on the TVM stack.

Tecton

Top Executive: Mike Del Balso, Co-Founder, CEO

Headquarters: San Francisco

Tecton just emerged from stealth in April 2020 with its data platform for machine learning that enables data scientists to turn raw data into production-ready machine-learning features. The startups technology is designed to help businesses and organizations harness and refine vast amounts of data into the predictive signals that feed machine-learning models.

The companys three founders: CEO Mike Del Balso, CTO Kevin Stumpf and Engineering Vice President Jeremy Hermann previously worked together at Uber where they developed the companys Michaelangelo machine-learning platform the ride-sharing company used to scale its operations to thousands of production models serving millions of transactions per second, according to Tecton.

The company started with $25 million in seed and Series A funding co-led by Andreessen Horowitz and Sequoia.

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The 12 Coolest Machine-Learning Startups Of 2020 - CRN

At KMWorld Connect 2020 David Seuss, CEO, Northern Light, Sid Probstein, CTO, Keeeb, and Tom Barfield, chief solution architect, Keeb discussed Machine Learning & KM.

KMWorld Connect, November 16-19, and its co-located events, covers future-focused strategies, technologies, and tools to help organizations transform for positive outcomes.

Machine learning can assist KM activities in many ways. Seuss discussed using a semantic analysis of keywords in social posts about a topic of interest to yield clear guidance as to which terms have actual business relevance and are therefore worth investing in.

What are we hearing from our users? Seuss asked. The users hate the business research process.

By using AstraZeneca as an example, Seuss started the analysis of the companys conference presentations. By looking at the topics, Diabetes sank lower as a focus of AstraZenicas focus.

When looking at their twitter account, themes included oncology, COVID-19, and environmental issues. Not one reference was made to diabetes, according to Seuss.

Social media is where the energy of the company is first expressed, Seuss said.

An instant news analysis using text analytics tells us the same story: no mention of diabetes products, clinical trials, marketing, etc.

AI-based automated insight extraction from 250 AstraZeneca oncolcogy conference presentations gives insight into R&D focus.

Let the machine read the content and tell you what it thinks is important, Seuss said.

You can do that with a semantic graph of all the ideas in the conference presentations. Semantic graphs look for relationships between ideas and measure the number and strength of the relationships. Google search results are a real-world example of this in action.

We are approaching the era when users will no longer search for information, they will expect the machine to analyze and then summarize for them what they need to know, Seuss said. Machine-based techniques will change everything.

Probstein and Barfield addressed new approaches to integrate knowledge sharing into work. They looked at collaborative information curation so end users help identify the best content, allowing KM teams to focus on the most strategic knowledge challenges as well as the pragmatic application of AI through text analytics to improve both curation and findability and improve performance.

The super silo is on the rise, Probstein said. It stores files, logs, customer/sales and can be highly variable. He looked at search results for how COVID-19 is having an impact on businesses.

Not only are there many search engines, each one is different, Probstein said.

Probstein said Keeeb can help with this problem. The solution can search through a variety of data sources to find the right information.

One search, a few seconds, one pane of glass, Probstein said. Once you solve the search problem, now you can look through the documents.

Knowledge isnt always a whole document, it can be a few paragraphs or an image, which can then be captured and shared through Keeeb.

AI and machine learning can enable search to be integrated with existing tools or any system. Companies should give end-users simple approaches to organize with content-augmented with AI-benefitting themselves and others, Barfield said.

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Utilizing machine learning to uncover the right content at KMWorld Connect 2020 - KMWorld Magazine

November 18, 2020 -A machine learning algorithm accurately determined how well skin cancer patients would respond to tumor-suppressing drugs in four out of five cases, according to research conducted by a team from NYU Grossman School of Medicine and Perlmutter Cancer Center.

The study focused on metastatic melanoma, a disease that kills nearly 6,800 Americans each year. Immune checkpoint inhibitors, which keep tumors from shutting down the immune systems attack on them, have been shown to be more effective than traditional chemotherapies for many patients with melanoma.

However, half of patients dont respond to these immunotherapies, and these drugs are expensive and often cause side effects in patients.

While immune checkpoint inhibitors have profoundly changed the treatment landscape in melanoma, many tumors do not respond to treatment, and many patients experience treatment-related toxicity, said corresponding study authorIman Osman, medical oncologist in the Departments of Dermatology and Medicine (Oncology) at New York University (NYU) Grossman School of Medicine and director of the Interdisciplinary Melanoma Program at NYU Langones Perlmutter Cancer Center.

An unmet need is the ability to accurately predict which tumors will respond to which therapy. This would enable personalized treatment strategies that maximize the potential for clinical benefit and minimize exposure to unnecessary toxicity.

READ MORE: How Social Determinants Data Can Enhance Machine Learning Tools

Researchers set out to develop a machine learning model that could help predict a melanoma patients response to immune checkpoint inhibitors. The team collected 302 images of tumor tissue samples from 121 men and women treated for metastatic melanoma with immune checkpoint inhibitors at NYU Langone hospitals.

They then divided these slides into 1.2 million portions of pixels, the small bits of data that make up images. These were fed into the machine learning algorithm along with other factors, such as the severity of the disease, which kind of immunotherapy regimen was used, and whether a patient responded to the treatment.

The results showed that the machine learning model achieved an AUC of 0.8 in both the training and validation cohorts, and was able to predict which patients with a specific type of skin cancer would respond well to immunotherapies in four out of five cases.

Our findings reveal that artificial intelligence is a quick and easy method of predicting how well a melanoma patient will respond to immunotherapy, said study first author Paul Johannet, MD, a postdoctoral fellow at NYU Langone Health and its Perlmutter Cancer Center.

Researchers repeated this process with 40 slides from 30 similar patients at Vanderbilt University to determine whether the results would be similar at a different hospital system that used different equipment and sampling techniques.

READ MORE: Simple Machine Learning Method Predicts Cirrhosis Mortality Risk

A key advantage of our artificial intelligence program over other approaches such as genetic or blood analysis is that it does not require any special equipment, said study co-author Aristotelis Tsirigos, PhD, director of applied bioinformatics laboratories and clinical informatics at the Molecular Pathology Lab at NYU Langone.

The team noted that aside from the computer needed to run the program, all materials and information used in the Perlmutter technique are a standard part of cancer management that most, if not all, clinics use.

Even the smallest cancer center could potentially send the data off to a lab with this program for swift analysis, said Osman.

The machine learning method used in the study is also more streamlined than current predictive tools, such as analyzing stool samples or genetic information, which promises to reduce treatment costs and speed up patient wait times.

Several recent attempts to predict immunotherapy responses do so with robust accuracy but use technologies, such as RNA sequencing, that are not readily generalizable to the clinical setting, said corresponding study authorAristotelis Tsirigos, PhD, professor in the Institute for Computational Medicine at NYU Grossman School of Medicine and member of NYU Langones Perlmutter Cancer Center.

READ MORE: Machine Learning Forecasts Prognosis of COVID-19 Patients

Our approach shows that responses can be predicted using standard-of-care clinical information such as pre-treatment histology images and other clinical variables.

However, researchers also noted that the algorithm is not yet ready for clinical use until they can boost the accuracy from 80 percent to 90 percent and test the algorithm at more institutions. The research team plans to collect more data to improve the performance of the model.

Even at its current level of accuracy, the model could be used as a screening method to determine which patients across populations would benefit from more in-depth tests before treatment.

There is potential for using computer algorithms to analyze histology images and predict treatment response, but more work needs to be done using larger training and testing datasets, along with additional validation parameters, in order to determine whether an algorithm can be developed that achieves clinical-grade performance and is broadly generalizable, said Tsirigos.

There is data to suggest that thousands of images might be needed to train models that achieve clinical-grade performance.

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Machine Learning Predicts How Cancer Patients Will Respond to Therapy - HealthITAnalytics.com

For example, they trained 50 versions of an image recognition model on ImageNet, a dataset of images of everyday objects. The only difference between training runs were the random values assigned to the neural network at the start. Yet despite all 50 models scoring more or less the same in the training testsuggesting that they were equally accuratetheir performance varied wildly in the stress test.

The stress test used ImageNet-C, a dataset of images from ImageNet that have been pixelated or had their brightness and contrast altered, and ObjectNet, a dataset of images of everyday objects in unusual poses, such as chairs on their backs, upside-down teapots, and T-shirts hanging from hooks. Some of the 50 models did well with pixelated images, some did well with the unusual poses; some did much better overall than others. But as far as the standard training process was concerned, they were all the same.

The researchers carried out similar experiments with two different NLP systems, and three medical AIs for predicting eye disease from retinal scans, cancer from skin lesions, and kidney failure from patient records. Every system had the same problem: models that should have been equally accurate performed differently when tested with real-world data, such as different retinal scans or skin types.

We might need to rethink how we evaluate neural networks, says Rohrer. It pokes some significant holes in the fundamental assumptions we've been making.

DAmour agrees. The biggest, immediate takeaway is that we need to be doing a lot more testing, he says. That wont be easy, however. The stress tests were tailored specifically to each task, using data taken from the real world or data that mimicked the real world. This is not always available.

Some stress tests are also at odds with each other: models that were good at recognizing pixelated images were often bad at recognizing images with high contrast, for example. It might not always be possible to train a single model that passes all stress tests.

One option is to design an additional stage to the training and testing process, in which many models are produced at once instead of just one. These competing models can then be tested again on specific real-world tasks to select the best one for the job.

Thats a lot of work. But for a company like Google, which builds and deploys big models, it could be worth it, says Yannic Kilcher, a machine-learning researcher at ETH Zurich. Google could offer 50 different versions of an NLP model and application developers could pick the one that worked best for them, he says.

DAmour and his colleagues dont yet have a fix but are exploring ways to improve the training process. We need to get better at specifying exactly what our requirements are for our models, he says. Because often what ends up happening is that we discover these requirements only after the model has failed out in the world.

Getting a fix is vital if AI is to have as much impact outside the lab as it is having inside. When AI underperforms in the real-world it makes people less willing to want to use it, says co-author Katherine Heller, who works at Google on AI for healthcare: We've lost a lot of trust when it comes to the killer applications, thats important trust that we want to regain.

Read more from the original source:
The way we train AI is fundamentally flawed - MIT Technology Review

Licensing agreement enables machine learning intelligence with best-in-class performance and power for robotics, surveillance, autonomous, and automotive applications

SAN JOSE, Calif.-- November 18, 2020 -- SiMa.ai, the machine learning company enabling high performance compute at the lowest power, today announced the adoption of low-power Arm compute technology to build its purpose-built Machine Learning SoC (MLSoC) platform. The licensing of this technology brings machine learning intelligence with best-in-class performance and power to a broad set of embedded edge applications including robotics, surveillance, autonomous, and automotive.

SiMa.ai is adopting Arm Cortex-A and Cortex-M processors optimized for power, throughput efficiency, and safety-critical tasks. In addition, SiMa.ai is leveraging a combination of widely used open-source machine learning frameworks from Arms vast ecosystem, to allow software to seamlessly enable machine learning for legacy applications at the embedded edge.

Arm is the industry leader in energy-efficient processor design and advanced computing, said Krishna Rangasayee, founder and CEO of SiMa.ai. The integration of SiMa.ai's high performance and low power machine learning accelerator with Arm technology accelerates our progress in bringing our MLSoC to the market, creating new solutions underpinned by industry-leading IP, the broad Arm ecosystem, and world-class support from its field and development teams."

From autonomous systems to smart cities, the applications enabled by ML at the edge are delivering increased functionality, leading to more complex device requirements, said Dipti Vachani, senior vice president and general manager, Automotive and IoT Line of Business at Arm. SiMa.ai is innovating on top of Arms foundational IP to create a unique low power ML SoC that will provide intelligence to the next generation of embedded edge use cases.

SiMa.ai is strategically leveraging Arm technology to deliver its unique Machine Learning SoC. This includes:

About SiMa.ai

SiMa.ai is a machine learning company enabling high performance compute at the lowest power. Initially focused on solutions for computer vision applications at the embedded edge, the company is led by a team of technology experts committed to delivering the industrys highest frames per second per watt solution to its customers. To learn more, visit http://www.sima.ai.

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SiMa.ai Adopts Arm Technology to Deliver a Purpose-built Heterogeneous Machine Learning Compute Platform for the Embedded Edge - Design and Reuse