What Can Machine Learning Tell Us About WR Prospects?

One of my favorite parts of draft season is trying to model the incoming prospects. This year, I wanted to try something new, so I dove into the world of machine learning models. Using machine learning to detail the value of a WR prospect is very useful for dynasty fantasy football.

Machine learning leverages artificial intelligence to identify patterns (learn) from the data, and build an appropriate model. I took over 60 different variables and 366 receiving prospects between the 2004 and 2016 NFL Drafts, and let the machine do its thing. As with any machine, some human intervention is necessary, and I fine-tuned everything down to a 24-model ensemble built upon different logistic regressions.

Just like before, the model presents the likelihood of a WR hitting 200 or more PPR points in at least one of his first three seasons. Here are the nine different components featured, in order of significance:

This obviously represents a massive change from the original model, proving once again that machines are smarter than humans. I decided to move over to ESPN grades and ranks instead of NFL Draft Scout for a few reasons:

Those changes alone made strong improvements to the model, and it should be noted that the ESPN overall ranks have been very closely tied to actual NFL Draft position.

Having an idea of draft position will always help a model since draft position usually begets a bunch of opportunity at the NFL level.

Since the model is built on drafts up until 2016, I figured perhaps youd want to see the results from the last three drafts before seeing the 2020 outputs.

It is encouraging to see some hits towards the top of the model, but there are obviously some misses as well. Your biggest takeaway here should be just how difficult it is to hit that 200 point threshold. Only two prospects the last three years have even a 40% chance of success. The model is telling us not to be over-confident, and that is a good thing.

Now that youve already seen some results, here are the 2020 model outputs.

Tee Higgins as the top WR is likely surprising for a lot of people, but it shouldnt be. Higgins had a fantastic career at Clemson, arguably the best school in the country over the course of his career. He is a proven touchdown scorer, and is just over 21 years old with a prototypical body-type.

Nobody is surprised that the second WR on this list is from Alabama, but they are likely shocked to see that a data-based model has Henry Ruggs over Jerry Jeudy. The pair is honestly a lot closer that many people think in a lot of the peripheral statistics. The major edge for Ruggs comes on the ground. He had a 75 yard rushing touchdown, which really underlines his special athleticism and play-making ability.

The name that likely stands out the most is Geraud Sanders, who comes in ahead of Jerry Jeudy despite being a relative unknown out of Air Force. You can mentally bump him down a good bit. The academy schools are a bit of a glitch in the system, as their offensive approach usually yields some outrageous efficiency. Since 2015, 12 of the top 15 seasons in adjusted receiving yards per pass attempt came from either an academy school or Georgia Techs triple-option attack. Sanders isnt a total zero, his profile looks very impressive, but I would have him closer to a 10% chance of success given his likely Day 3 or undrafted outcome in the NFL Draft.

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The Top Machine Learning WR Prospect Will Surprise You - RotoExperts

Weve finally reached a stage in our technical expertise where we can think about connecting our minds with machines. This is possible through brain-computer interface (BCI) technologies that would soon transcend our human capabilities.

The human race is looking at the past to create future tomorrows would be controlled by your mind, and machines will be your agents. If we look into the recent advancements in Computing, Data Science, Machine Learning and Neural Networking, the future looks very predictable, yet disarmingly tough. Imagine the future like this Were moving into a latent telepathy mode very soon. Its truly going to be a brain-power that will operate machines and get work done, AI or no AI.

In this article, we will quickly summarize the Brain-Computer Interface (BCI) definitions, key technologies, and their applications in the modern Artificial Intelligence age.

A Brain-Computer Interface can be defined as a seamless network mechanism that relays brain activity into a desired mechanical action. A modern BCI action would involve the use of a brain-activity analyzer and neural networking algorithm that acquires complex brain signals, analyzes them, and translates them for a machine. These machines could be a robotic arm, a voice box, or any automated assistive device such as prosthetics, wheelchair, and iris-controlled screen cursors.

This is a simple infographic about BCIs.

Advancements in functional neuroimaging techniques and inter-cranial Spatial imagery have opened up new avenues in the fields of Cognitive Learning and Connected Neural Networking. Today, Brain-Computer Interfaces rely on a mix of signals acquired from the brain and nervous systems. These are classified under

According to the US National Library of Medicine National Institutes of Health, there are three types of BCI technologies. These are

Brain-Computer Interface is used to complete a mental task using neuro-motor output pathways or imagery. For example, lifting your leg to climb steps.

This is a stimulus-based conditional Brain-Computer Interface that acts on selective attention. For example, crouching on your feet to cross a barbed fence. The principle behind Reactive BCIs can be better understood from the P300 settings. The P300 setting involves a mix of neuroscience-based decision making and cognitive learning based on visual stimulus.

It involves no visual stimulus. The BCI mechanism merely acts like a switch (On/Off) based on the cognitive state of the brain and body at work. It is the least researched category in BCI development.

Unlike general Cloud Computing and Machine Learning DevOps, the BCI developers come with a specialized background.

Hot Start-Ups:TIBCO Recognized as a Leader in 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms

Brain-Computer Interface DevOps engineers have to constantly work with a team of Neuroscientists, Computer Programmers, Neurologists, Psychologists, Rehabilitation Specialists, and sometimes, Camera OEMs.

According to a paper on Brain-computer interfaces for communication and control, BCIs in 2002 could deliver maximum information transfer rates up to 10-25bits/min.

Since then, BCI development has gained major traction from large-scale innovation companies and futurist technocrats such as Teslas Elon Musk. We are already seeing logic-defying amalgamation of AI research and interdisciplinary collaboration between Neurobiology, Psychology, Engineering, Mathematics, and Computer Science.

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Brain Computer Interface: Definitions, Tools and Applications - AiThority

A day after Facebook announced it would rely more heavily on artificial-intelligence-powered content moderation, some users are complaining that the platform is making mistakes and blocking a slew of legitimate posts and links, including posts with news articles related to the coronavirus pandemic, and flagging them as spam.

While trying to post, users appear to be getting a message that their content sometimes just a link to an article violates Facebooks community standards. We work hard to limit the spread of spam because we do not want to allow content that is designed to deceive, or that attempts to mislead users to increase viewership, read the platforms rules.

The problem also comes as social media platforms continue to combat Covid-19-related misinformation. On social media, some now are floating the idea that Facebooks decision to send its contracted content moderators home might be the cause of the problem.

Facebook is pushing back against that notion, and the companys vice president for integrity, Guy Rosen, tweeted that this is a bug in an anti-spam system, unrelated to any changes in our content moderator workforce. Rosen said the platform is working on restoring the posts.

Recode contacted Facebook for comment, and well update this post if we hear back.

The issue at Facebook serves as a reminder that any type of automated system can still screw up, and that fact might become more apparent as more companies, including Twitter and YouTube, depend on automated content moderation during the coronavirus pandemic. The companies say theyre doing so to comply with social distancing, as many of their employees are forced to work from home. This week, they also warned users that, because of the increase in automated moderation, more posts could get taken down in error.

In a blog post on Monday, YouTube told its creators that the platform will turn to machine learning to help with some of the work normally done by reviewers. The company warned that the transition will mean some content will be taken down without human review, and that both users and contributors to the platform might see videos removed from the site that dont actually violate any of YouTubes policies.

The company also warned that unreviewed content may not be available via search, on the homepage, or in recommendations.

Similarly, Twitter has told users that the platform will increasingly rely on automation and machine learning to remove abusive and manipulated content. Still, the company acknowledged that artificial intelligence would be no replacement for human moderators.

We want to be clear: while we work to ensure our systems are consistent, they can sometimes lack the context that our teams bring, and this may result in us making mistakes, said the company in a blog post.

To compensate for potential errors, Twitter said it wont permanently suspend any accounts based solely on our automated enforcement systems. YouTube, too, is making adjustments. We wont issue strikes on this content except in cases where we have high confidence that its violative, the company said, adding that creators would have the chance to appeal these decisions.

Facebook, meanwhile, says its working with its partners to send its content moderators home and to ensure that theyre paid. The company is also exploring remote content review for some of its moderators on a temporary basis.

We dont expect this to impact people using our platform in any noticeable way, said the company in a statement on Monday. That said, there may be some limitations to this approach and we may see some longer response times and make more mistakes as a result.

The move toward AI moderators isnt a surprise. For years, tech companies have pushed automated tools as a way to supplement their efforts to fight the offensive and dangerous content that can fester on their platforms. Although AI can help content moderation move faster, the technology can also struggle to understand the social context for posts or videos and, as a result make inaccurate judgments about their meaning. In fact, research has shown that algorithms that detect racism can be biased against black people, and the technology has been widely criticized for being vulnerable to discriminatory decision-making.

Normally, the shortcomings of AI have led us to rely on human moderators who can better understand nuance. Human content reviewers, however, are by no means a perfect solution either, especially since they can be required to work long hours analyzing traumatic, violent, and offensive words and imagery. Their working conditions have recently come under scrutiny.

But in the age of the coronavirus pandemic, having reviewers working side by side in an office could not only be dangerous for them, it could also risk further spreading the virus to the general public. Keep in mind that these companies might be hesitant to allow content reviewers to work from home as they have access to lots of private user information, not to mention highly sensitive content.

Amid the novel coronavirus pandemic, content review is just another way were turning to AI for help. As people stay indoors and look to move their in-person interactions online, were bound to get a rare look at how well this technology fares when its given more control over what we see on the worlds most popular social platforms. Without the influence of human reviewers that weve come to expect, this could be a heyday for the robots.

Update, March 17, 2020, 9:45 pm ET: This post has been updated to include new information about Facebook posts being flagged as spam and removed.

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Facebook, YouTube, and Twitter warn that AI systems could make mistakes - Vox.com

Machine Learning in Finance Market report profile provides top-line qualitative and quantitative summary information including: Market Size (Production, Consumption, Value and Volume 2014-2019, and Forecast from 2020 to 2026). The Machine Learning in Finance Market profile also contains descriptions of the leading topmost manufactures/players like (Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance) which including Capacity, Production, Price, Revenue, Cost, Gross, Gross Margin, Growth Rate, Import, Export, Market Share and Technological Developments. Besides, this Machine Learning in Finance market covers Type, Application, Major Key Players, Regional Segment Analysis Machine Learning in Finance, Industry Chain Analysis, Competitive Insights and Macroeconomic Analysis.

Some of The Major Highlights Of TOC Covers: Development Trend of Analysis of Machine Learning in Finance Market; Marketing Channel; Direct Marketing; Indirect Marketing; Machine Learning in Finance Customers; Machine Learning in Finance Market Dynamics; Opportunities; Market Drivers; Challenges; Influence Factors; Research Programs/Design; Machine Learning in Finance Market Breakdown; Data Triangulation and Source.

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Scope of Machine Learning in Finance Market:The value of machine learning in finance is becoming more apparent by the day. As banks and other financial institutions strive to beef up security, streamline processes, and improve financial analysis, ML is becoming the technology of choice.

Split by Product Types, this report focuses on consumption, production, market size, share and growth rate of Machine Learning in Finance in each type, can be classified into:

Supervised Learning Unsupervised Learning Semi Supervised Learning Reinforced Leaning

Split by End User/Applications, this report focuses on consumption, production, market size, share and growth rate of Machine Learning in Finance in each application, can be classified into:

Banks Securities Company Others

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Machine Learning in Finance Market Regional Analysis Covers:

The Study Objectives Of This Machine Learning in Finance Market Report Are:

To analyze the key Machine Learning in Finance manufacturers, to study theProduction, Capacity, Volume, Value, Market Size, Shareand development plans in future.

To analyze the key regions Machine Learning in Finance market potential andAdvantage, Opportunity and Challenge, Restraints and Risks.

Focuses on the key manufacturers, to define, describe and analyze the marketCompetition Landscape, SWOT Analysis.

To define, describe and forecast the Machine Learning in Finance market by type, application and region.

To analyze the opportunities in the Machine Learning in Finance market forStakeholders by Identifying the High Growth Segments.

To analyze competitive developments such as Expansions, Agreements, New Product Launches, And Acquisitions in the Machine Learning in Finance Market.

To strategically analyze each submarket with respect to individualGrowth Trend and Their Contribution to the Machine Learning in Finance Market.

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Machine Learning in Finance Market Size 2020 Global Industry Share, Top Players, Opportunities And Forecast To 2026 - 3rd Watch News

Editor's note:This is the second part of aseries on the evolution of war and warfareacross decades. Over the course of these articles, the relationship between technology, politics and war will be put under the magnifying glass.

Effective war-fighting demands that militaries should be able to peek into the future. As such, victory in battle is often linked with clairvoyance.

Let me explain. Suppose you are leading a convoy in battle and expect to meet resistance at some point soon. If you could see precisely where and when this is going to happen, you can call in an airstrike to vastly diminish the enemy's forces, thereby increasing your chances of victory when you finally meet it.

While modern satellites and sensors linked with battle units provide such a capability first demonstrated by the US military with striking effect in the 1991 Gulf War the quest for such a capability has been around as long as wars which is to say, forever. Watch towers on castles with sentries, for example, are also sensors albeit highly imperfect ones. They sought to render the battlefield "transparent", to use modern terminology, in the face of cavalry charges by the enemy.

At the heart of this quest for battlefield transparency lies intelligence, the first key attribute of warfare. Our colloquial understanding of the word and its use in the context of war can appear to be disconnected, but the two are not. If "intelligence refers to an individual's or entity's ability to make sense of the environment",as security-studies scholar Robert Jervis defined it, intelligent behaviour in war and everyday life are identical. It is, to continue the Jervis quote, the consequent ability "to understand the capabilities and intentions of others and the vulnerabilities and opportunities that result". The demands of modern warfare require that militaries augment this ability using a wide array of technologies.

The goal of intelligent warfare is very simple: See the enemy and prepare (that is to observe and orient) and feed this information to the war fighters (who then decide on what to do, and finally act through deployment of firepower). This cycle, endlessly repeated across many weapon-systems, is the famous OODA loop pioneered by maverick American fighter pilot, progenitor of the F-16 jet, and military theorist John Boyd beginning in 1987. It is an elegant reimagination of war. As one scholar of Boyd's theory put it, "war can be construed of as a collision of organisations going through their respective OODA loops".

To wit, the faster you can complete these loops perfectly (and your enemy's job is to not let you do so while it goes about with its own OODA loops), the better off you are in battle. Modern militaries seek this advantage by gathering as much information as it is possible about the enemy's forces and disposition, through space-based satellites, electronic and acoustic sensors and, increasingly, unmanned drones. Put simply, the basic idea is to have a rich 'information web' in the form of battlefield networks which links war fighters with machines that help identify their targets ahead. A good network mediated by fast communication channels shrinks time as it were, by bringing the future enemy action closer.

Representational image. AFP

The modern search for the decisive advantage that secret information about enemy forces often brings came to the fore with the Cold War, driven by the fear of nuclear annihilation in the hands of the enemy. In the mid-1950s, the United States Central Intelligence Agencys U-2 spy planes flew over large swathes of Soviet territory in order to assess enemy capabilities; its Corona satellite programme, also launched roughly around the same time, marked the beginning of space-based reconnaissance. Both were among the most closely guarded secrets of the early Cold War.

But the United States also used other more exotic methods to keep an eye on Soviet facilities to have an upper hand, should war break out. For example, it sought to detect enemy radar facilities by looking for faint radio waves they bounced off the moon.

The problem with having (sophisticated) cameras alone as sensors as was the case with the U-2 planes as well as the Corona satellite is that one is at the mercy of weather conditions such as cloud cover over the area of interest. Contemporary airborne- or space-based radars, which build composite images of the ground using pulses of radio waves, overcome this problem. In general, radar performance does not depend on the weather despite a famous claim to the contrary. That said, these 'synthetic aperture radars' (SAR) are often unable to pick up very fine-resolution details unlike optical cameras.

The use of sensors is hardly limited to land warfare. Increasingly, underwater 'nets' of sensors are being conceived to detect enemy ships. It is speculated that China has already made considerable progress in this direction, by deploying underwater gliders that can transmit its detections to other military units in real time. The People's Liberation Army has also sought to use space-based LIDARs (radar-like instruments which use pulsed lasers instead of radio-waves) to detect submarines 1,600 feet below the water surface.

Means of detection of course are a small (but significant) part of the solution in battlefield transparency. A large part of one's ability to wage intelligent wars depend on the ability to integrate the acquired information with battle units and weapon-systems for final decision and action. But remember, the first thing your enemy is likely to do is to prevent you from doing so, by jamming electronic communications or even targeting your communications satellite using a missile of the kind India tested last March. In a future war, major militaries will operate in such contested environments where a major goal of the adversary will be to disrupt the flow of information.

Artificial intelligence (AI) may eventually come to the rescue to OODA loops, but in a manner whose political and ethical costs are still unknown. Note that AI too obeys the definition Jervis set for intelligence, the holy grail being the design of all-purpose computers that can learn about the environment on their own and make decisions autonomously based on circumstances.

Such computers are still some way in the future. What we do have is a narrower form of AI where algorithms deployed on large computers manage to learn certain tasks by teaching themselves from human-supplied data. These machine-learning algorithms have made stupendous progress in the recent years. In 2016, Google AlphaGo a machine-learning algorithm defeated the reigning world champion in a notoriously difficult East Asian board game setting a new benchmark for AI.

Programmes like AlphaGo are designed after how networks of neurons in the human brain and in the part of the brain responsible for processing visual images, in particular are arranged and known to function biologically. Therefore, it is not a surprise that the problem of image recognition has served as a benchmark of sorts for such programmes.

Recall that militaries are naturally interested in not only gathering images of adversary forces but also recognising what they see in them, a challenge with often-grainy SAR images, for example. (In fact, the simplest of machine-learning algorithms modelled on neurons the Perceptron was invented by Frank Rosenblatt in 1958 using US Navy's funds.) While machine-learning programmes until now have only made breakthroughs with optical images last year, in a demonstration by private defence giant Lockheed Martin, one such algorithm scanned the entire American state of Pennsylvania and correctly identified all petroleum fracking sites radar images are not out of sight.

Should AI programmes be able to process images from all wavelengths, one way to bypass the 'contested environment problem' is to let weapons armed with them observe, orient, decide, and act all without the need for humans. In a seminal book on lethal autonomous weapons, American defence strategist Paul Scharre describes this as taking people off the OODA loop. As he notes, while the United States officially does not subscribe to the idea of weapons deciding on what to hit, the research agencies in that country have continued to make significant progress on the issue of automated target recognition.

Other forces have not been as circumspect about deploying weapon-systems without humans playing a significant role in OODA loops. The Russian military has repeatedly claimed that it has the ability to deploy AI-based nuclear weapons. This have been interpreted to include cruise missiles with nuclear warheads moving more than five times the speed of sound.

How can India potentially leverage such intelligent weapons? Consider the issue of a nuclear counterforce strike against Pakistan where New Delhi destroys Rawalpindi's nukes before they can be used against Indian targets. While India's plans to do so are a subject of considerable analytical debate, one can perhaps wildly speculate about the following scenario.

Based on Pakistan's mountainous topography including the Northern Highlands and the Balochistan plateau, it is quite likely that it will seek to conceal them there, inside cave-like structures or in hardened silos, in sites that are otherwise very hard to recognise. Machine-learning programmes dedicated to the task of image recognition from satellite surveillance data can improve India's ability to identify many more such sites than what is currently possible. This ability, coupled with precision-strike missiles, will vastly improve India's counterforce posture should it officially adopt one.

All this is not to say that the era of omniscient intelligent weapons is firmly upon us. Machine-learning algorithms for pattern recognition are still work-in-progress in many cases and far from being fool-proof. (For example, one such programme had considerable difficulty telling the difference between a turtle and a rifle.) But if current trends in the evolution of machine-learning continue, a whole new era of intelligent warfare may not be far.

Read the first part of the series here:Risk of 19th Century international politics being pursued using 21st Century military means looms large

Find latest and upcoming tech gadgets online on Tech2 Gadgets. Get technology news, gadgets reviews & ratings. Popular gadgets including laptop, tablet and mobile specifications, features, prices, comparison.

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The remaking of war; Part 2: Machine-learning set to usher in a whole new era of intelligent warfare - Firstpost

The global Machine Learning Courses market reached ~US$ xx Mn in 2018 and is anticipated grow at a CAGR of xx% over the forecast period 2019-2029. In this Machine Learning Courses market study, the following years are considered to predict the market footprint:

The business intelligence study of the Machine Learning Courses market covers the estimation size of the market both in terms of value (Mn/Bn USD) and volume (x units). In a bid to recognize the growth prospects in the Machine Learning Courses market, the market study has been geographically fragmented into important regions that are progressing faster than the overall market. Each segment of the Machine Learning Courses market has been individually analyzed on the basis of pricing, distribution, and demand prospect for the following regions:

Each market player encompassed in the Machine Learning Courses market study is assessed according to its market share, production footprint, current launches, agreements, ongoing R&D projects, and business tactics. In addition, the Machine Learning Courses market study scrutinizes the strengths, weaknesses, opportunities and threats (SWOT) analysis.

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On the basis of age group, the global Machine Learning Courses market report covers the footprint, and adoption pattern of the segments including

The key players covered in this study

EdX

Ivy Professional School

NobleProg

Udacity

Edvancer

Udemy

Simplilearn

Jigsaw Academy

BitBootCamp

Metis

DataCamp

Market segment by Type, the product can be split into

Rote Learning

Learning From Instruction

Learning By Deduction

Learning By Analogy

Explanation-Based Learning

Learning From Induction

Market segment by Application, split into

Data Mining

Computer Vision

Natural Language Processing

Biometrics Recognition

Search Engines

Medical Diagnostics

Detection Of Credit Card Fraud

Securities Market Analysis

DNA Sequencing

Market segment by Regions/Countries, this report covers

United States

Europe

China

Japan

Southeast Asia

India

Central & South America

The study objectives of this report are:

To analyze global Machine Learning Courses status, future forecast, growth opportunity, key market and key players.

To present the Machine Learning Courses development in United States, Europe and China.

To strategically profile the key players and comprehensively analyze their development plan and strategies.

To define, describe and forecast the market by product type, market and key regions.

In this study, the years considered to estimate the market size of Machine Learning Courses are as follows:

History Year: 2014-2018

Base Year: 2018

Estimated Year: 2019

Forecast Year 2019 to 2025

For the data information by region, company, type and application, 2018 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

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The Machine Learning Courses market report answers the following queries:

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Rapid Industrialization to Boost Machine Learning Courses Growth by 2019-2025 - Keep Reading

Y Combinators Demo Day was a bit different this time around.

As concerns grew over the spread of COVID-19, Y Combinator shifted the event format away from the two-day gathering in San Francisco weve gotten used to, instead opting to have its entire class debut to invited investors and media via YCs Demo Day website.

In a bit of a surprise twist, YC also moved Demo Day forward one week, citing accelerated pacing from investors. Alas, this meant switching up its plan for each company to have a recorded pitch on the Demo Day website; instead, each company pitched via slides, a few paragraphs outlining what theyre doing and the traction theyre seeing, and team bios. Its unclear so far how this new format in combination with the rapidly evolving investment climate will impact this class.

As we do with each class, weve collected our notes on each company based on information gathered from their pitches, websites and, in some cases, our earlier coverage of them.

To make things a bit easier to read, weve split things up by category rather than have it be one huge wall of text. These are the companies that are working on hardware, robotics, AI, machine learning or tools for developers. You can find the other categories (such as biotech, consumer, and fintech) here.

Datasaur: A tool meant to help humans label machine data data sets more accurately and efficiently through things like auto-correct, auto-suggest and keyboard hotkeys. Its free for individual labelers, $100 per month for teams of up to 20 labelers, with custom pricing for larger teams.

1build: Automatic, data-driven job cost estimates for construction companies. You upload your plans, and 1build says it can prepare accurate bids in minutes. The company projects a revenue run rate of over $600,000, and says it has completed estimates for mega companies like Amazon, Starbucks and 7-Eleven.

Handl: An API for turning paper documents including handwritten ones into structured data ready to be plunked into a database or CRM. While the company says that around 85% of its processing is handled by their AI, its backed by humans to validate data when the AIs confidence is low. Nine months after launch, the company is seeing an ARR of $0.9 million.

Zumo Labs: Uses game engines to generate pre-labeled training data for computer vision systems. By synthesizing the data rather than collecting it from photos/videos of the real world, the company says it can create massive data sets faster, cheaper and without privacy issues.

Teleo: Retrofits existing construction equipment to allow operators to control them remotely. The company says it has built a fully functional teleoperated loader since being founded three months ago, and plans to charge construction companies a flat monthly fee per vehicle. The companys co-founders were previously head of Hardware Engineering and director of Product Manager at Lyft, with both having worked on Googles Street View team.

Menten AI: Menten AI says its using quantum computing and machine learning combined with synthetic biology to design new protein-based drugs.

Turing Labs Inc.: Automated, simulated testing of different formulas for consumer goods like soaps and deodorant. Home products and cosmetics can be months of work for R&D labs. Turing has built an AI engine that helps with this process much like the AI engines used in drug discovery cutting down the time to days. Its already working with some of the biggest CPG companies in the world. You can find our previous coverage on Turing here.

Segmed: Segmed is building data sets for AI-driven medical research. Rather than requiring each and every researcher to individually partner with hospitals and imaging facilities, Segmed partners with these organizations (currently over 50) and standardizes, labels and anonymizes the data.

Ardis AI: Ardis AI wants to build the foundation of artificial general intelligence technology that read and comprehend text like a human. By combining neural networks, symbolic reasoning and new natural language processing techniques, Ardis AI can serve companies that dont want to hire teams to do data extraction and labeling.

Agnoris: Agnoris analyzes a restaurants point-of-sale data to recommend changes to pricing, delivery menus and staffing. For $3,600 per year per restaurant location, Agnoris claims to be able to raise profits by 20%. The company started after the founder opened a restaurant that was packed yet losing money, so it built machine learning tools to improve margins and now its selling that software to all eateries.

Froglabs: Froglabs provides weather forecasting AI to businesses for predicting solar and wind energy production, delivery delays, staffing shortages, sales demand and food availability. By ingesting petabytes of weather data, it can save companies money by ensuring their logistics arent disrupted. Founded by a long-time Googler who started its Project Loon internet-beaming weather balloons, its now signing up e-commerce, retail, rideshare, restaurant and event businesses.

PillarPlus: PillarPlus is a platform that automates the blueprint-designing phase of a building project. It takes a design from an architect or contractor and maps out mechanical, fire, electrical and plumbing details, and estimates the bill of materials and project cost, steps that otherwise take months of work.

Glisten: Glisten uses computer vision and machine learning technologies to develop better, more consistent data sets for e-commerce companies. Its first product is an AI-based tool to populate and enrich sparse product data. Find our previous coverage of Glisten here.

nextmv: Nextmv gives its customers the ability to create their own logistics algorithms automatically allowing businesses to optimize fleets and manage routes internally.

Visual One: Movement-detecting security cameras can bring up a lot of false positives: theres motion, yes, but not necessarily anything harmful. Visual One has built an AI platform that integrates with home security cameras to read the specific movements that they detect. Owners can create customised alerts so they get notifications only for what they care about. The companys software can check for furniture-destroying pets, package-lifting thieves, the death-defying antics of toddlers and more. Find our previous coverage of Visual One here.

PostEra: Medicinal chemistry-as-a-service is the idea here: PostEras platform can design and synthesize molecules faster and at a lower cost than the typical R&D lab, speeding up the research time it takes to test new combinations in the drug discovery process.

Cyberdontics: Robotics have already revolutionized surgery, courtesy of companies like da Vinci-maker, Intuitive. Cyberdontics is aimed at doing the same for oral surgery, beginning with crowns one of the more expensive and time-intensive procedures. The company says its robot is capable of performing the generally two-hour procedure in 15 minutes, charging a mere $140 for the job.

Avion: Focused on inhabitants of difficult to reach areas in Africa, Avion is building a drone-based delivery system. The plans consist of medium and long-range medical drones tied to a centralized hub. The drones are hybrid and autonomous with vertical take-off capabilities, able to take 5-kg payloads as far as 150 kms.

SOMATIC: Industrial bathroom cleaning is a prime dull/dirty candidate to be replaced by automation. Somatic builds large robots that are trained to clean restrooms via VR. The system sprays and wipes down surfaces and is capable of opening doors and riding up and down in the elevator. Find our previous coverage of SOMATIC here.

RoboTire: Anyone whos ever sat in a service shop waiting room knows how time-intensive the process can be. RoboTire promises to cut the wait time from 60 minutes down to 10 for a set of four tires. The company has begun piloting the technology in locations around the U.S. Find our previous coverage of RoboTire here.

Morphle: Designed to replace outdated analog microscopes, Morphles system uses robotic automation to improve imaging. The startup processes higher-resolution images than far pricier systems and with a much smaller failure rate. Morphle has begun selling its system to labs in India.

Daedalus: Founded by an early engineer at OpenAI, Daedalus is building autonomous software to allow industrial robots to operate without human programming, beginning with CNC machines. The company projects that it can improve productivity in the metal machining market by 5x.

Exosonic, Inc.: Exosonic makes supersonic commercial aircraft that dont have to produce a loud sonic boom, so they can be flown over land. Its goal is a plane that can fly from SF to NYC in three hours. The CEO worked on NASAs low-boom X-59 aircraft while at Lockheed Martin. Exosonic now has letters of intent from a major airline and two Department of Defense groups, plus a $300,000 U.S. Air Force contract.

Nimbus: Founded by a serial entrepreneur and based in Ann Arbor, Mich., Nimbus is developing the next-generation vehicle platform for urban transportation. Founder Lihang Nong previously launched the fuel-injection systems developer PicoSpray and is now looking to answer the question, Can a vehicle be several times more space and energy efficient than todays cars while actually being more comfortable to ride in?

UrbanKisaan: UrbanKisaan is a vertical farming operation based in India that delivers fresh produce subscriptions to households. Its farms of stacked-up hydroponic tables can be located near cities with just 1% of the land usage of traditional agriculture, and there are no pesticides necessary. In a market with a growing middle class seeking healthy foods, delivering from farm-to-door could let UrbanKisaan control quality and its margins.

Talyn Air: Two former SpaceX engineers have developed a long-range electric vertical take-off and landing (eVTOL) aircraft for passengers and cargo. The startup has created an electric fixed-wing aircraft that is caught mid-air with a custom winged drone during take offs and landings, an approach that its founders say give this aircraft three times the range of its competitors, at 350 miles.

BuildBuddy: Two ex-Googlers want to provide a Google-style development environment to all by building an open-source UI/feature set on top of Googles Bazel software. The company says that their solution speeds up build times by up to 10x. Its free for independent developers, with the price scaling from $4 per user to $49 per user depending on the size of the team and the features required.

Dataline: Meant to let websites gather analytics data from users who are using ad-blocking tools. Claiming that most ad-blocker users care mostly about display ads or cross-site tracking, the company says that first-party analytics gets hit as collateral damage. By acting as a smart proxy that runs on a sub-domain, Dataline avoids most ad-blocking systems (for now, presumably.)

Cortex: Many modern online software applications are powered by countless independent, purpose-focused tools or microservices. Cortex monitors your apps microservices to automatically flag the right person (hooking into Datadog/Slack/PagerDuty/etc.) when one breaks.

apitracker: Even if your website seems to be loading fine, the APIs you use to make it work might be having trouble, breaking things in not so obvious ways. Apitracker tracks your APIs. It monitors the APIs you use, alerting you when one of them starts to fail and providing insights into their overall performance.

Freshpaint: Freshpaints autotrack system collects all pageviews/clicks/etc. across your site, allowing you to push it into tools like Google Analytics/Facebook Pixel etc. retroactively without requiring your dev team to make manual trackers for each event. The base plan is free for sites with fewer than 3,000 users and $300 for sites with up to 50,000 monthly users, after which point the pricing shifts to custom packaging.

Datree: Datree allows companies to set up rules and security policies for their codebase, and ensures those rules are followed before any code is merged. Charging $28 per developer (noting that its free for independent/open source projects), theyve pulled in ~$230K in revenue to date. Find our previous coverage of Datree here.

fly.io: Deploys your app on servers that are physically closer to your users, decreasing latency and improving the user experience. If your app grows more popular in a certain city, Fly detects that and scales resources accordingly.

Sweeps: Sweeps claims that they can make your website 40% faster with one line of code, by more intelligently loading all of the third-party tools that a website is using. The team says that their tech not only improves speed but does so while improving SEO.

Orbiter: Orbiter is an automatic real-time monitoring and alert system integrated with Slack to ensure better customer service and revenue management.

Release: Product releases can be tricky. Release provides a staging management toolkit it builds a staging environment each time theres a pull request, allowing for faster/more collaborative development cycles.

Signadot: Signadot is monitoring and management software for the microservices that modern startups rely on to power their own applications and services, hopefully flagging issues before they become apparent to the end user.

Raycast: Raycast is a universal command bar for developers and many of the tools they use. Users can integrate apps including Jira, GitHub or Slack and take a Superhuman-like approach to completing forms and tasks. The team is pitching the tool as a way to help engineers get their non-engineering work done quickly.

Cotter: Cotter is building a phone number-based login platform that authenticates a users device in a workflow that the companys founders say has the convenience of SMS-based OTP without the security issues. The startup is aiming to target customers in developing countries where email is less utilized and less convenient as a login.

ditto: Dittos founders are hoping to create the Figma for words, helping teams plan out more thoughtfully the copy they use to describe their products and workflows. The collaboration tool created by Stanford roommates Jolena Ma and Jessica Ouyang currently has 80+ different companies represented among their users.

Scout: A continuous integration and deployment toolkit for machine learning experiments inside a GitHub workflow.

ToDesktop: ToDesktop has designed a service to automate all of your desktop application publishing needs. It works with Windows, Mac and Linux and provides native installers, auto-updates, code signing and crash reports without the need for any infrastructure or configurations for developers.

DeepSource: DeepSource is a code review tool that allows developers to check for bug risks, anti-patterns, performance issues and security flaws in Python and Go.

Flowbot: Flowbot is a natural language, autocomplete search tool for coding in Python. It lets Python developers type in plain English when they cant remember the exact function theyre thinking of, with Flowbot digging through documentation and considering the context to find the code it thinks youre looking for.

PostHog: PostHog is a software service that lets developers understand how their users are actually working with their products. Its a product analytics toolkit for open-source programmers.

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All the companies from Y Combinators W20 Demo Day, Part III: Hardware, Robots, AI and Developer Tools - TechCrunch