Category Archives: Machine Learning
There is a need for more comprehensive prediction models for advanced age-related macular degeneration (AMD) that consider a wider range of risk factors. Researchers tested a prediction model and applied a machine learning algorithm that autonomously identified the most significant clinical, genetic, and lifestyle risk factors for AMD.
The training set, obtained from the Rotterdam Study I (RS-I), included 3,838 patients aged 55 years or older. Median follow-up was 10.8 years, and there were 108 incident cases of advanced AMD. The test set, obtained from the ALIENOR study, included 362 participants aged 73 years or older. Median follow-up was 6.5 years, and there were 33 incident cases of advanced AMD.
The following factors were retained by the prediction model:
In the RS-I group, the cross-validated area under the receiver operating characteristic curve (AUC) estimation was: at five years, 0.92; at 10 years, 0.92; and at 15 years, 0.91. In the ALIENOR cohort, at five years, the AUC was 0.92. The researchers noted that when it came to calibration, the prediction model underestimated the cumulative incidence of advanced AMD in high-risk groups; this was particularly evident in the ALIENOR cohort.
They concluded that their prediction model achieved high discrimination abilities and was a step toward precision medicine for patients with AMD.
Further Education News
The FE News Channel gives you the latest education news and updates on emerging education strategies and the#FutureofEducation and the #FutureofWork.
Providing trustworthy and positive Further Education news and views since 2003, we are a digital news channel with a mixture of written word articles, podcasts and videos. Our specialisation is providing you with a mixture of the latest education news, our stance is always positive, sector building and sharing different perspectives and views from thought leaders, to provide you with a think tank of new ideas and solutions to bring the education sector together and come up with new innovative solutions and ideas.
FE News publish exclusive peer to peer thought leadership articles from our feature writers, as well as user generated content across our network of over 3000 Newsrooms, offering multiple sources of the latest education news across the Education and Employability sectors.
FE News also broadcast live events, podcasts with leading experts and thought leaders, webinars, video interviews and Further Education news bulletins so you receive the latest developments inSkills Newsand across the Apprenticeship, Further Education and Employability sectors.
Every week FE News has over 200 articles and new pieces of content per week. We are a news channel providing the latest Further Education News, giving insight from multiple sources on the latest education policy developments, latest strategies, through to our thought leaders who provide blue sky thinking strategy, best practice and innovation to help look into the future developments for education and the future of work.
In May 2020, FE News had over 120,000 unique visitors according to Google Analytics and over 200 new pieces of news content every week, from thought leadership articles, to the latest education news via written word, podcasts, video to press releases from across the sector.
We thought it would be helpful to explain how we tier our latest education news content and how you can get involved and understand how you can read the latest daily Further Education news and how we structure our FE Week of content:
Our main features are exclusive and are thought leadership articles and blue sky thinking with experts writing peer to peer news articles about the future of education and the future of work. The focus is solution led thought leadership, sharing best practice, innovation and emerging strategy. These are often articles about the future of education and the future of work, they often then create future education news articles. We limit our main features to a maximum of 20 per week, as they are often about new concepts and new thought processes. Our main features are also exclusive articles responding to the latest education news, maybe an insight from an expert into a policy announcement or response to an education think tank report or a white paper.
FE Voices was originally set up as a section on FE News to give a voice back to the sector. As we now have over 3,000 newsrooms and contributors, FE Voices are usually thought leadership articles, they dont necessarily have to be exclusive, but usually are, they are slightly shorter than Main Features. FE Voices can include more mixed media with the Further Education News articles, such as embedded podcasts and videos. Our sector response articles asking for different comments and opinions to education policy announcements or responding to a report of white paper are usually held in the FE Voices section. If we have a live podcast in an evening or a radio show such as SkillsWorldLive radio show, the next morning we place the FE podcast recording in the FE Voices section.
In sector news we have a blend of content from Press Releases, education resources, reports, education research, white papers from a range of contributors. We have a lot of positive education news articles from colleges, awarding organisations and Apprenticeship Training Providers, press releases from DfE to Think Tanks giving the overview of a report, through to helpful resources to help you with delivering education strategies to your learners and students.
We have a range of education podcasts on FE News, from hour long full production FE podcasts such as SkillsWorldLive in conjunction with the Federation of Awarding Bodies, to weekly podcasts from experts and thought leaders, providing advice and guidance to leaders. FE News also record podcasts at conferences and events, giving you one on one podcasts with education and skills experts on the latest strategies and developments.
We have over 150 education podcasts on FE News, ranging from EdTech podcasts with experts discussing Education 4.0 and how technology is complimenting and transforming education, to podcasts with experts discussing education research, the future of work, how to develop skills systems for jobs of the future to interviews with the Apprenticeship and Skills Minister.
We record our own exclusive FE News podcasts, work in conjunction with sector partners such as FAB to create weekly podcasts and daily education podcasts, through to working with sector leaders creating exclusive education news podcasts.
FE News have over 700 FE Video interviews and have been recording education video interviews with experts for over 12 years. These are usually vox pop video interviews with experts across education and work, discussing blue sky thinking ideas and views about the future of education and work.
FE News has a free events calendar to check out the latest conferences, webinars and events to keep up to date with the latest education news and strategies.
The FE Newsroom is home to your content if you are a FE News contributor. It also help the audience develop relationship with either you as an individual or your organisation as they can click through and box set consume all of your previous thought leadership articles, latest education news press releases, videos and education podcasts.
Do you want to contribute, share your ideas or vision or share a press release?
If you want to write a thought leadership article, share your ideas and vision for the future of education or the future of work, write a press release sharing the latest education news or contribute to a podcast, first of all you need to set up a FE Newsroom login (which is free): once the team have approved your newsroom (all content, newsrooms are all approved by a member of the FE News team- no robots are used in this process!), you can then start adding content (again all articles, videos and podcasts are all approved by the FE News editorial team before they go live on FE News). As all newsrooms and content are approved by the FE News team, there will be a slight delay on the team being able to review and approve content.
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Global Machine Learning in Automobile Market: Development History, Current Analysis and Estimated Forecast to 2024 – The Market Correspondent
A new report added by Big Market Research claims that the globalMachine Learning in Automobile marketgrowth is set to reach newer heights during the forecast period,20202025.
The report is an exhaustive analysis of this market across the world. It offers an overview of the market including its definition, applications, key drivers, key market players, key segments, and manufacturing technology. In addition, the study presents statistical data on the status of the market and hence is a valuable source of guidance for companies and individuals interested in the industry. Additionally, detailed insights on the company profile, product specifications, capacity, production value, and market shares for key vendors are presented in the report.
Request a sample of this premium research @:https://www.bigmarketresearch.com/request-sample/3773575?utm_source=SHASHI&utm_medium=TMC
By Key Players:Allerin, Intellias Ltd, NVIDIA Corporation, Xevo, Kopernikus Automotive, Blippar, Alphabet Inc, Intel, IBM, Microsoft
A proper understanding of the Machine Learning in Automobile Market dynamics and their inter-relations helps in gauging the performance of the industry. The growth and revenue patterns can be revised and new strategic decisions taken by companies to avoid obstacles and roadblocks. It could also help in changing the patterns using which the market will generate revenues. The analysis includes an assessment of the production chain, supply chain, end user preferences, associated industries, proper availability of resources, and other indexes to help boost revenues.
Regions & Top Countries Data Covered in this Report are:Asia-Pacific (China, Southeast Asia, India, Japan, Korea, Western Asia), Europe (Germany, UK, France, Italy, Russia, Spain, Netherlands, Turkey, Switzerland), North America (United States, Canada, Mexico), Middle East & Africa (GCC, North Africa, South Africa) , South America (Brazil, Argentina, Columbia, Chile, Peru).
The Machine Learning in Automobile Market is gaining pace and businesses have started understanding the benefits of analytics in the present day highly dynamic business environment. The market has witnessed several important developments over the past few years, with mounting volumes of business data and the shift from traditional data analysis platforms to self-service business analytics being some of the most prominent ones.
With the help of in-depth research offered in the report, readers can effortlessly get detailed analysis of the key dynamics of the Machine Learning in Automobile market. The report also offers competitive landscape by providing detailed information on trends in competition, prominent players, and nature of competition. Additionally, it offers detailed analysis of the key segments of the market that helps in understanding the global trends in the Machine Learning in Automobile Market. An overview of each market segment such as type, application, and region are presented in the report. Additionally, the report presents drivers, limitations, and opportunities for the Machine Learning in Automobile industry, followed by industry news and policies.
Machine Learning in Automobile Market By Type:
Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced Leaning
Machine Learning in Automobile Market By Application:
AI Cloud ServicesAutomotive InsuranceCar ManufacturingDriver MonitoringOthers
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The report provides insights on the following pointers:
North America, Europe, Asia Pacific, Middle East & Africa, Latin America market size (sales, revenue and growth rate) of Machine Learning in Automobile industry.
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Global major countries (United States, Canada, Germany, France, UK, Italy, Russia, Spain, Netherlands, Switzerland, Belgium, China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Vietnam, Turkey, Saudi Arabia, United Arab Emirates, South Africa, Israel, Egypt, Nigeria, Brazil, Mexico, Argentina, Colombia, Chile, Peru) market size (sales, revenue and growth rate) of Machine Learning in Automobile industry.
Different types and applications of Machine Learning in Automobile industry, market share of each type and application by revenue.
Global market size (sales, revenue) forecast by regions and countries from 2020 to 2026 of Machine Learning in Automobile industry.
Upstream raw materials and manufacturing equipment, downstream major consumers, industry chain analysis of Machine Learning in Automobile industry.
Key drivers influencing market growth, opportunities, the challenges and the risks analysis of Machine Learning in Automobile industry.
New Project Investment Feasibility Analysis of Machine Learning in Automobile industry.
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Our analysis involves the study of the market taking into consideration the impact of the COVID-19 pandemic. Please get in touch with us to get your hands on an exhaustive coverage of the impact of the current situation on the market. Our expert team of analysts will provide as per report customized to your requirement.
Table of Content:
Industry Overview of Machine Learning in Automobile
Major Manufacturers Analysis of Machine Learning in Automobile
Global Price, Sales and Revenue Analysis of Machine Learning in Automobile by Regions, Manufacturers, Types and Applications
North America Sales and Revenue Analysis of Machine Learning in Automobile by Countries
Europe Sales and Revenue Analysis of Machine Learning in Automobile by Countries
Asia Pacific Sales and Revenue Analysis of Machine Learning in Automobile by Countries
Latin America Sales and Revenue Analysis of Machine Learning in Automobile by Countries
Middle East & Africa Sales and Revenue Analysis of Machine Learning in Automobile by Countries
Global Market Forecast of Machine Learning in Automobile by Regions, Countries, Manufacturers, Types and Applications
Industry Chain Analysis of Machine Learning in Automobile
New Project Investment Feasibility Analysis of Machine Learning in Automobile
Conclusion of the Global Machine Learning in Automobile Industry Market Professional Survey 2020
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Because of the popularity of MOFs, scientists are developing, synthesizing, studying, and cataloging MOFs. However, the sheer number of MOFs is creating a problem.
Even if synthesizing new MOF, it is quite challenging to know whether it is new and not some minor variation of a structure that has already been synthesized.
To address this problem, EPFL scientists, in collaboration with MIT, have used machine-learning to organize the chemical diversity found in the ever-growing databases for the popular metal-organic framework materials. Using machine learning, scientists developed a language to compare two materials and quantify their differences.
Through this new language, scientists set off to determine the chemical diversity in MOF databases.
Professor Berend Smit at EPFL said,Before, the focus was on the number of structures. But now, we discovered that the major databases have all kinds of bias towards particular structures. There is no point in carrying out expensive screening studies on similar structures. One is better off in carefully selecting a set of very diverse structures, which will give much better results with far fewer structures.
Another exciting application is scientific archeology: The researchers used their machine-learning system to identify the MOF structures that, at the time of the study, were published as very different from the ones that are already known.
Smit said,So we now have a straightforward tool that can tell an experimental group how different their novel MOF is compared to the 90,000 other structures already reported.
Dashboard AI Announces Its Technology Vision for the Foodservice and Hospitality Industry – PRNewswire
TEMPE, Ariz., Sept. 15, 2020 /PRNewswire/ --Dashboard AI,a technology innovator in the foodservice and hospitality industry, today announces its vision for a single system of record for the industry.The company leverages advances in machine learning, computer vision, and IoT toreveal insights that improve safety, efficiency, compliance, and training thatcreate a single system of record.
"The current fragmented software landscape creates an opportune time to implement a unified platform that is the system of record for critical data around business operations, safety, inventory, staffing, and productivity." said Brian Pierce, Dashboard AI cofounder and chairman."Our Dashboard AI's stewardship of this data creates aplatform of powerthat can be used in other business software and services categories that need access to this underlying data."
Harnessing the recent advancements in facial recognition, computer vision, sensors, and IoT, the company is building a suite of products that automate existing operational processes that are typically performed manually.Dashboard AI's machine learning algorithms are built using the company's proprietary data and training libraries designed specifically for foodservice operations.Its machine learning capabilities include first-of-its-kind inventory and usage monitoring algorithms to naturally identify food and beverage brands within the foodservice and hospitality environment, to learn, monitor, track, and measure brand usage. These applications use the company's technology to improve accuracy, efficacy, and efficiency.
"The foodservice sector has been riveted by the global pandemic and we need to leverage technology to rethink how we can optimize our operational processes and build a foundation for the future" said Brian Pierce."At Dashboard AI,we've been working on this opportunity for over a year before the pandemic hit and as a result of its impact on the industry, we've accelerated our efforts to develop and refine our solutions."
AboutDashboard AIDashboard AI is an "All-in-One" platform for foodservice.The company's mission is toincrease efficiency, safety, compliance, and training for the foodservice industry.Leveraging advances in machine learning, computer vision, and IoT, Dashboard AI creates a single system of record for the industry.The company's suite of solutions includes machine learning-powered inventory monitoring and ordering, safety and security, training and education, and labor productivity measurement all in an integrated, open, real-time platform.
Founded in 2019 by serial entrepreneurs in the foodservice and technology industry, Brian Pierce and Kelly Egan, Dashboard AI is backed by Resiliency Ventures as well as notable angel investors from the foodservice and technology industry.The company is expanding its current round of financing as a result of increased interest in its technology and solutions.
Dashboard AI60 Rio Salado ParkwayTempe, AZ 85281
SOURCE Dashboard AI, Inc.
Alfa, provider of Alfa Systems, released its second paper on artificial intelligence in the industry. Part 2: Using Machine Learning in the Wild is a more technical follow-up to 2019s Part 1: Balancing Risk and Reward and explores in two specific use cases which take very different approaches to machine learning implementation. It features a foreword from Blaise Thomson, whose speech technology startup VocalIQ was acquired by Apple and formed a part of the Siri development team.
AI and machine learning are front and center in the asset finance conversation at the moment but many dont know where to start how much expertise they need, what they can outsource, and where they should concentrate their efforts and costs, Martyn Tamerlane, a solution architect at Alfa and co-author of the paper, said. Our worked-through examples convey genuinely useful and practically applicable advice for people wanting to kick off their own machine learning projects. By comparing the approaches used, we offer advice on whats right for others.
The first example, which addresses automated license plate recognition and its ongoing embedding in business processes, takes an off-the-shelf approach to training machine learning models, drawing heavily on tools provided by AWS. Meanwhile, the second, which analyses Alfas internal code tests, is carried out wholly in-house with existing resources and knowledge. The paper also features a decision aid to help readers clarify how their projects might compare.
2019s Balancing Risk and Reward outlined the high-risk, high-reward nature of using AI in the asset finance industry, and machine learning in particular. Alfa will continue its commentary on AI in asset finance with further upcoming publications.
See the article here:
Alfa Releases Second Paper on AI, Using Machine Learning in the Wild - Monitor Daily
Presented by AWS Machine Learning
As machine learning has evolved, so have best practices, especially in the wake of COVID-19. Join this VB Live event to learn from experts about how machine learning solutions are helping companies respond in these uncertain times and the lessons learned along the way.
Register here for free.
Misinformation around COVID-19 is driving human behavior across the world. Here in the information age, sensationalized clickbait headlines are crowding out actual fact-based content, and, as a result misinformation spreads virally. Conversations within small communities become the epicenter of false information, and that misinformation spreads as people talk, both online and off. As the number of misinformed people grow, this infodemic grows.
The spread of misinformation around COVID-19 is especially problematic, because it could overshadow the key messaging around safety measures from public health and government officials.
In an effort to counter misinformed narratives in central and west Africa, Novetta Mission Analytics (NMA) is working with Africa CDC (Center for Disease Control) to discover and identify narratives and behavior patterns around the disease, says David Cyprian, product owner at Novetta. And machine learning is key.
They supply data that measures the acceptability, impact, and effectiveness of public health and social measures. In turn, the Africa CDC analysis of the data enables them to generate tailored guidelines for each country.
With all these different narratives out there, we can use machine learning to quantify which ones are really affecting the largest population, Cyprian explains. We uncover how quickly these things are spreading, how many people are talking about the issues, and whether anyone is actually criticizing the misinformation itself.
NMA uncovered trending phrases that indicate worry around the disease, mistrust about official messaging, and criticisms of local measures to combat the disease. They found that herbal remedies are becoming popular, as is the idea of herd immunity.
We know all of these different narratives are changing behavior, Cyprian says. Theyre causing people to make decisions that make it more difficult for the COVID-19 response community to be effective and implement countermeasures that are going to mitigate the effects of the virus.
To identify these narrative threads, Novetta ingests publicly-available social media at scale and pairs it with a collection of domestic and international news media. They process and analyze that raw social and traditional media content in their ML platform built on AWS to identify where people are talking about these things, and where events are happening that drive the conversations. They also use natural language processing for directed sentiment analysis to discover whether narratives are being driven by mistrust of a local government entity, the west, or international organizations, as well as identifying influencers that are engendering a lot of positive sentiment among users and building trust.
Pieces of content are tagged as positive or negative to local and global pandemic measures and public entities, creating small human-labeled data sets about specific micronarratives for specific populations that might be trading in misinformation.
By fusing rapid ingestion with a human labeling process of just a few hundred artifacts, theyre able to kick off machine learning and apply it to the scale of social media. This allows them to have more than one learning model that is used for all the problem sets.
We dont have a one-size-fits-all approach, says Cyprian. Were always tuning and researching accuracy for specific narratives, and then were able to provide large, near-real-time insights into how these narratives are propagating or spreading in the field.
Built on AWS, their machine learning architecture allows their development team to focus on what they do well, which is develop new applications and new widgets to be able to analyze this data.
They dont need to worry about any server management, or scaling, since thats taken care of for them with Amazon EC2 and S3. Their microservices architecture uses some additional features that Amazon offers, particularly Elastic Kubernetes Service (EKS), to orchestrate their services, and Amazon Elastic Container Registry (ECR), to store images and run vulnerability testing before they deploy.
Novettas approach is cross-disciplinary, bringing in domain experts from the health field, media analysts, machine learning research engineers, and software developers. They work in small teams to solve problems together.
In my experience, thats been the best way for machine learning to make a practical difference, he says. I would just urge folks who are facing these similar difficult problems to enable their people to do what people do well, and then have the machine learning engineers help to harden, verify, and scale those efforts so you can bring countermeasures to bear quickly.
To learn more about the impact machine learning solutions can deliver and lessons learned along the way, dont miss this round table with leaders from Kabbage and Novetta, as well as Michelle K. Lee, VP of the Amazon Machine Learning Solutions Lab.
Dont miss out!
Register here for free.
Domino Data Lab Named a Leader in Notebook-Based Predictive Analytics and Machine Learning Evaluation by Global Research Firm – Business Wire
SAN FRANCISCO--(BUSINESS WIRE)--Domino Data Lab, provider of the leading open enterprise data science management platform trusted by over 20% of the Fortune 100, has been named a Leader by Forrester Research in its The Forrester Wave: Notebook-based Predictive Analytics and Machine Learning (PAML), Q3 2020 report. As previously announced, Domino was also a Leader in the Q3 2018: Notebook-based Predictive Analytics and Machine Learning (PAML) Forrester Wave.
According to the Forrester report, the Domino Data Science Platform ...supports the diversity of [machine learning] ML options that users need in todays rapidly expanding PAML ecosystem, with repeatability, discipline and governance.
The report also notes that ...Domino tames the chaos, bringing all your different PAML tools together and binding them in a common, governed platform. Adding that Domino drives productivity by abstracting away infrastructure provisioning, managing clusters, tracking experiments, maintaining version control, and deploying and monitoring models. And it drives collaboration with built-in knowledge management tools and shared repositories for data, code, model artifacts, and apps irrespective of where they were developed.
The Domino data science platform was built to satisfy the needs of large enterprises with teams of code-first data scientists who demand collaboration, openness and reproducibility, backed by IT governance, security and compliancefor centralized data science at scale.
Were proud to be the platform that powers data science for leading enterprises. This gives us a front-row seat to see how companies like Bayer, Bristol-Myers Squibb, and Dell are using data science to solve the worlds most complex problems like fighting cancer or creating a vaccine for COVID, said Nick Elprin, CEO and co-founder at Domino Data Lab. Domino was built to drive the critical capabilities the worlds most advanced organizations need, and were delighted that Forrester has recognized us a Leader.
Domino Data Lab was evaluated in The Forrester Wave on 26 criteria across three categories: current offering, strategy and market presence, among 11 other vendors. In the evaluation, Domino received among the top scores in the ModelOps criterion, and received the highest scores possible in the criteria of collaboration, platform infrastructure, ability to execute, solution roadmap, and enablement.
A complimentary copy of this research report is available at dominodatalab.com.
About Domino Data Lab
Domino Data Lab empowers data science teams with the leading, open data science platform that enables enterprises to manage and scale data science with discipline and maturity. Model-driven companies including Allstate, Dell Technologies, and Bayer use Domino as a data science system of record to accelerate breakthrough research, increase collaboration, and rapidly deliver high-impact models. Founded in 2013 and based in San Francisco, Domino is backed by Sequoia Capital, Coatue, Bloomberg Beta, Dell Technologies Capital, Highland Capital Partners, and Zetta Venture Partners. For more information, visit dominodatalab.com.
Some people have spent their quarantine downtime bakingsourdough bread. Others experiment with tie-dye. But others namely Toronto-based artist Daniel Voshart have createdpainstaking portraits of all 54 Roman emperors of the Principate period, which spanned from 27 BC to 285 AD.
The portraits help people visualize what the Roman emperors would have looked like when they were alive.
Included are Vosharts best artistic guesses of the faces of emperors Augustus, Nero, Caligula, Marcus Aurelius and Claudius, among others. They dont look particularly heroic or epic rather, they look like regular people, with craggy foreheads, receding hairlines and bags under their eyes.
To make the portraits, Voshart used a design software called Artbreeder, which relies on a kind of artificial intelligence called generative adversarial networks (GANs).
Voshart starts by feeding the GANs hundreds of images of the emperors collected from ancient sculpted busts, coins and statues. Then he gets a composite image, which he tweaks in Photoshop. To choose characteristics such as hair color and eye color, Voshart researches the emperors backgrounds and lineages.
It was a bit of a challenge, he says. About a quarter of the project was doing research, trying to figure out if theres something written about their appearance.
He also needed to find good images to feed the GANs.
Another quarter of the research was finding the bust, finding when it was carved because a lot of these busts are recarvings or carved hundreds of years later, he says.
In a statement posted on Medium, Voshartwrites: My goal was not to romanticize emperors or make them seem heroic. In choosing bust/sculptures, my approach was to favor the bust that was made when the emperor was alive. Otherwise, I favored the bust made with the greatest craftsmanship and where the emperor was stereotypically uglier my pet theory being that artists were likely trying to flatter their subjects.
Related:Battle of the bums: Museums complete over best artistic behinds
Voshart is not a Rome expert. His background is in architecture and design, and by day he works in the art department of the TV show "Star Trek: Discovery," where he designs virtual reality walkthroughs of the sets before they'rebuilt.
But when the coronavirus pandemic hit, Voshart was furloughed. He used the extra time on his hands to learn how to use the Artbreeder software.The idea for the Roman emperor project came from a Reddit threadwhere people were posting realistic-looking images theyd created on Artbreeder using photos of Roman busts. Voshart gave it a try and went into exacting detail with his research and design process, doing multiple iterations of the images.
Voshart says he made some mistakes along the way. For example, Voshart initially based his portrait of Caligula, a notoriously sadistic emperor, on a beautifully preserved bust in the Metropolitan Museum of Art. But the bust was too perfect-looking, Voshart says.
Multiple people told me he was disfigured, and another bust was more accurate, he says.
So, for the second iteration of the portrait, Voshart favored a different bust where one eye was lower than the other.
People have been telling me my first depiction of Caligula was hot, he says. Now, no ones telling me that.
Voshart says people who see his portraits on Twitter and Reddit often approach them like theyd approachTinder profiles.
I get maybe a few too many comments, like such-and-such is hot. But a lot of these emperors are such awful people!
I get maybe a few too many comments, like such-and-such is hot. But a lot of these emperors are such awful people! Voshart says.
Voshart keeps a list on his computer of all the funny comparisons people have made to present-day celebrities and public figures.
Ive heard Nero looks like a football player. Augustus looks like Daniel Craigmy early depiction of Marcus Aurelius looks like the Dude from 'The Big Lebowski.'
But the No. 1 comment? Augustus looks like Putin.
Related:UNESCO says scammers are using its logo to defraudartcollectors
No one knows for sure whether Augustus actually looked like Vladimir Putin in real life.Voshart says his portraits are speculative.
Its definitely an artistic interpretation, he says. Im sure if you time-traveled, youd be very angry at me."
Machine learning era has reached the stage of interpretability where developing models and making predictions is simply not enough any more. To make a powerful impact and get good results on the data it is important to investigate and probe the dataset and the models. A good model investigation involves digging deep into the understanding of the model to find insights and inconsistencies in the developed model. This task usually involves writing a lot of custom functions. But, with tools like What-If, it makes the probing task very easy and saves time and efforts for programmers.
In this article we will learn about:
What-If tool is a visualization tool that is designed to interactively probe the machine learning models. WIT allows users to understand machine learning models like classification, regression and deep neural networks by providing methods to evaluate, analyse and compare the model. It is user friendly and can be used not only by developers but also by researchers and non-programmers very easily.
WIT was developed by Google under the People+AI research (PAIR) program. It is open-source and brings together researchers across Google to study and redesign the ways people interact with AI systems.
This tool provides multiple features and advantages for users to investigate the model.
Some of the features of using this are:
WIT can be used with a Google Colab notebook or Jupyter notebook. It can also be used with Tensorflow Board.
Let us take a sample dataset to understand the different features of WIT. I will choose the forest fire dataset available for download on Kaggle. You can click here for downloading the dataset. The goal here is to predict the areas affected by forest fires given the temperature, month, amount of rain etc.
I will implement this tool on google collaboratory. Before we load the dataset and perform the processing, we will first install the WIT. To install this tool use,
!pip install witwidget
Once we have split the data, we can convert the columns month and day to categorical values using label encoder.
Now we can build our model. I will use sklearn ensemble model and implement the gradient boosting regression model.
Now that we have the model trained, we will write a function to predict the data since we need to use this for the widget.
Next, we will write the code to call the widget.
This opens an interactive widget with two panels.
To the left, there is a panel for selecting multiple techniques to perform on the data and to the right is the data points.
As you can see on the right panel we have options to select features in the dataset along X-axis and Y-axis. I will set these values and check the graphs.
Here I have set FFMC along the X-axis and area as the target. Keep in mind that these points are displayed after the regression is performed.
Let us now explore each of the options provided to us.
You can select a random data point and highlight the point selected. You can also change the value of the datapoint and observe how the predictions change dynamically and immediately.
As you can see, changing the values changes the predicted outcomes. You can change multiple values and experiment with the model behaviour.
Another way to understand the behaviour of a model is to use counterfactuals. Counterfactuals are slight changes made that can cause a model to flip its decision.
By clicking on the slide button shown below we can identify the counterfactual which gets highlighted in green.
This plot shows the effects that the features have on the trained machine learning model.
As shown below, we can see the inference of all the features with the target value.
This tab allows us to look at the overall model performance. You can evaluate the model performance with respect to one feature or more than the one feature. There are multiple options available for analysis of the performance.
I have selected two features FFMC and temp against the area to understand performance using mean error.
If multiple training models are used their performance can be evaluated here.
The features tab is used to get the statistics of each feature in the dataset. It displays the data in the form of histograms or quantile charts.
The tab also enables us to look into the distribution of values for each feature in the dataset.
It also highlights the features that are most non-uniform in comparison to the other features in the dataset.
Identifying non-uniformity is a good way to reduce bias in the model.
WIT is a very useful tool for analysis of model performance. Ability to inspect models in a simple no-code environment will be of great help especially in the business perspective.
It also gives insights to factors beyond training the model like understanding why and how that model was created and how the dataset is fitting in the model.