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Matt de Neef
The UniSA-Australia national team has revealed its line-up for both the 2020 men’s and women’s Santos Tour Down Under.
Reigning U23 national road champion Nicholas White (BridgeLane) will headline the men’s team in what will be his second Tour Down Under. He’ll be joined by 19-year-old BridgeLane teammate Tyler Lindorff, former U23 national champion Sam Jenner, Jarrad Drizners (who will race for Hagens Berman Axeon in 2020), and the Pro Racing Sunshine Coast trio of Kelland O’Brien, Cameron Scott and Sam Welsford.
Thirty-seven-year-old Rachel Neylan will spearhead the women’s line-up a year after finishing third in the 2019 edition. The six-rider line-up will also feature Neylan’s soon-to-be Casa Dorada Women Cycling teammate Josie Talbot, Macey Stewart, Georgie Whitehouse, Jessica Pratt, and Ruby Roseman-Gannon.
The Santos Women’s Tour Down Under will be contested over four stages from January 16-19. The men’s race comprises a curtain-raiser criterium on January 19, followed by six stages from January 21-26.
UniSA-Australia men’s team
– Nicholas White
– Tyler Lindorff
– Samuel Jenner
– Jarrad Drizners
– Samuel Welsford
– Kelland O’Brien
– Cameron Scott
UniSA-Australia women’s team
– Rachel Neylan
– Jessica Pratt
– Ruby Roseman-Gannon
– Macey Stewart
– Georgie Whitehouse
– Josie Talbot
The post UniSA-Australia teams announced for men’s and women’s Tour Down Under appeared first on CyclingTips.
The most promising 100 AI startups working across the artificial intelligence value chain, from hardware and data infrastructure to industrial applications.
CB Insights’ third annual cohort of AI 100 startups is a list of 100 of the most promising private companies providing hardware and data infrastructure for AI applications, optimizing machine learning workflows, and applying AI across a variety of major industries.
Our research team selected the 100 startups from a pool of 3K+ companies based on several factors, including patent activity, investor profile, news sentiment analysis, proprietary Mosaic scores, market potential, partnerships, competitive landscape, team strength, and tech novelty.
Startups are categorized by their main focus areas. Categories in the market map below are not mutually exclusive.
Please click to enlarge.
Table of contents
100 startups in different stages of R&D
The AI 100 companies are disrupting 12 core sectors, including healthcare, telecommunications, semiconductors, government, retail, and finance, as well as the broader enterprise tech stack.
From emerging startups to established unicorns, the cohort is a mix of startups in different stages of funding and product commercialization.
For example, Prowler.io is a Series A company in the UK that’s focused on scaling practical applications of reinforcement learning. The team has published around 38 papers accepted for presentations at conferences including NeurIPS and the International Conference on Learning Representations.
Palo Alto-based One Concern raised a $33M equity round last year from New Enterprise Associates. Its AI platform helps governments plan for and predict the impact of natural disasters like earthquakes and floods.
Others, like SenseTime and Graphcore, are valued at over $1B.
A total of 11 companies on the list are unicorns (private companies valued at $1B+).
|Company||Sector||Focus Area||Country||Max Valuation ($M)|
|UiPath||Enterprise Tech||Other: RPA||United States||$3,000|
|Automation Anywhere||Enterprise Tech||Other: RPA||United States||$2,600|
|Graphcore||Semiconductor||Data Centers||United Kingdom||$1,700|
|Butterfly Network||Healthcare||Imaging & Diagnostics||United States||$1,250|
|4Paradigm||Finance & Insurance||Anti-Fraud||China||$1,200|
|Pony.ai||Auto||Autonomous Vehicles||United States||$1,000|
Most well-funded companies
The top 2 most well-funded companies — SenseTime and Face++ — are both from China and focused on facial recognition tech, with government investors and clients.
The third is California-based Zymergen, which uses ML for material discovery. One of its focus areas is finding alternatives to plastics and petroleum-based products.
US patent activity
Patent applications are one measure of a company’s R&D focus. A total of 62 of the 100 startups have applied to patent their tech in the US, accounting for over 600 patent applications. (Note: this excludes patent applications in international markets by US and non-US startups.)
Hover, for example, allows users to take pictures of their home using a smartphone camera, and uses image processing techniques to stitch together a 3D model of the home. In one recent patent filing on “directed image capture,” machine learning algorithms are used to assess the quality of captured images based on various features.
Butterfly Network has built a portable, hand-held ultrasound for less than $2K. Computer vision will be integrated within the hardware to help to interpret the images. The image below is from a patent filing for “portable electronic devices with integrated image processing capabilities.”
Cerebras, which has revealed few details about its AI processor, filed 3 patents in 2018 that highlight its R&D on semiconductor fabrication and accelerating deep learning. The image below highlights “neural network training and inference using a deep learning accelerator.”
Startups outside the United States
23 startups on the list are headquartered outside the US, including 6 each from China, Israel, and the United Kingdom.
Most active investors
Over 680 unique investors have funded this year’s cohort, including corporations, CVCs, VC firms, and angel investors.
|Investor||Number of deals||Most recent portfolio companies backed|
|Google Ventures||27||Machinify, mabl, Benson Hill Biosystems, Viz.ai, Tamr|
|Kleiner Perkins Caufield & Byers||22||AEye, Shape Security, Area 1 Security, Viz.ai, Jask Labs|
|Data Collective||21||Zymergen, Area 1 Security, Mythic, Fortem Technologies, Atomwise|
|New Enterprise Associates||19||Data Robot, One Concern, Tamr, Automation Anywhere, DataVisor|
|Accel||16||Demisto, UiPath, DeepMap, Vectra Networks, Trifacta|
|Norwest Venture Partners||15||Shape Security, Agari Data, Qventus, Mist Systems, Dremio|
|Battery Ventures||15||HyperScience, Dataiku, Habana Labs, Machinify, Jask Labs|
|Intel Capital||14||Habana Labs, AEye, DataRobot, Syntiant, Gamalon|
|IA Ventures||13||DataRobot, Signifyd, Vectra Networks|
|AME Cloud Ventures||13||Mythic, Vectra Networks, Zymergen, Arterys, Atomwise|
Track all the 2019 AI 100 startups in this brief on our platform
The most promising 100 AI startups working across the artificial intelligence value chain, from hardware and data infrastructure to industrial applications.
Table of 100 startups by industry and focus area
|Benson Hill Biosystems||Agriculture||Agricultural Biotech||United States|
|Iris Automation||Auto||Perception||United States|
|Perceptive Automata||Auto||Perception||United States|
|AI.Reverie||Enterprise Tech||Training Data||United States|
|DefinedCrowd||Enterprise Tech||Training Data||United States|
|Mighty AI||Enterprise Tech||Training Data||United States|
|Dataiku||Enterprise Tech||Data Management||United States|
|Machinify||Enterprise Tech||Data Management||United States|
|DataRobot||Enterprise Tech||Data Management||United States|
|Tamr||Enterprise Tech||Data Management||United States|
|H2O.ai||Enterprise Tech||Data Management||United States|
|Trifacta||Enterprise Tech||Data Management||United States|
|Dremio||Enterprise Tech||Data Management||United States|
|SigOpt||Enterprise Tech||Data Management||United States|
|mabl||Enterprise Tech||Software Development||United States|
|Applitools||Enterprise Tech||Software Development||United States|
|Demisto||Enterprise Tech||Cybersecurity||United States|
|Shape Security||Enterprise Tech||Cybersecurity||United States|
|Vectra Networks||Enterprise Tech||Cybersecurity||United States|
|Area 1 Security||Enterprise Tech||Cybersecurity||United States|
|Agari Data||Enterprise Tech||Cybersecurity||United States|
|Jask Labs||Enterprise Tech||Cybersecurity||United States|
|PerimeterX||Enterprise Tech||Cybersecurity||United States|
|BounceX||Enterprise Tech||Ads, Sales, & Marketing||United States|
|Unbabel||Enterprise Tech||Ads, Sales, & Marketing||United States|
|Gong||Enterprise Tech||Ads, Sales, & Marketing||United States|
|Gamalon||Enterprise Tech||Ads, Sales, & Marketing||United States|
|FullStory||Enterprise Tech||Ads, Sales, & Marketing||United States|
|UiPath||Enterprise Tech||Other: RPA||United States|
|Automation Anywhere||Enterprise Tech||Other: RPA||United States|
|Orbital Insight||Enterprise Tech||Other: Alternative Data||United States|
|Descartes Labs||Enterprise Tech||Other: Alternative Data||United States|
|Element AI||Enterprise Tech||Other||Canada|
|SparkCognition||Enterprise Tech||Other||United States|
|Prowler.io||Enterprise Tech||Other: RL Platform||United Kingdom|
|4Paradigm||Finance & Insurance||Anti-Fraud||China|
|BioCatch||Finance & Insurance||Anti-Fraud||Israel|
|DataVisor||Finance & Insurance||Anti-Fraud||United States|
|HyperScience||Finance & Insurance||Back Office Automation||United States|
|Behavox||Finance & Insurance||Behavioral Analytics||United Kingdom|
|AppZen||Finance & Insurance||Auditing||United States|
|One Concern||Government||Disaster Management||United States|
|Fortem Technologies||Government||Security||United States|
|Shield AI||Government||Security||United States|
|Insitro||Healthcare||Drug R&D||United States|
|OWKIN||Healthcare||Drug R&D||United States|
|Atomwise||Healthcare||Drug R&D||United States|
|PAIGE.AI||Healthcare||Imaging & Diagnostics||United States|
|Niramai||Healthcare||Imaging & Diagnostics||India|
|Butterfly Network||Healthcare||Imaging & Diagnostics||United States|
|IDx Technologies||Healthcare||Imaging & Diagnostics||United States|
|Arterys||Healthcare||Imaging & Diagnostics||United States|
|Viz.ai||Healthcare||Imaging & Diagnostics||United States|
|Mindstrong Health||Healthcare||Mental Health||United States|
|Gauss Surgical||Healthcare||Operating Room||United States|
|Medopad||Healthcare||Remote Monitoring||United Kingdom|
|Sense Labs||Industrials||Energy Disaggregation||United States|
|Kebotix||Industrials||Material Discovery||United States|
|Zymergen||Industrials||Material Discovery||United States|
|Landing AI||Industrials||Quality Inspection||United States|
|LawGeex||Legal, Compliance, & HR||Contract Review||Israel|
|Eigen Technologies||Legal, Compliance, & HR||Contract Review||United Kingdom|
|Onfido||Legal, Compliance, & HR||Onboarding & Compliance||United Kingdom|
|Textio||Legal, Compliance, & HR||Augmented Writing||United States|
|AI Foundation||Media||Fake News Detection||United States|
|New Knowledge||Media||Fake News Detection||United States|
|Arraiy||Media||Motion Pictures||United States|
|Hover||Real estate||3D Modeling||United States|
|Skyline AI||Real estate||Asset Management||United States|
|AiFi||Retail||Checkout-Free Store Tech||United States|
|Habana Labs||Semiconductor||Data Centers||Israel|
|Graphcore||Semiconductor||Data Centers||United Kingdom|
|Cerebras Systems||Semiconductor||Data Centers||United States|
|Horizon Robotics||Semiconductor||Edge Devices||China|
|Thinci||Semiconductor||Edge Devices||United States|
|Syntiant||Semiconductor||Edge Devices||United States|
|Mythic||Semiconductor||Edge Devices||United States|
|Mist Systems||Telecom||WLAN||United States|
2018 AI 100
2017 AI 100
AI Magazine | Artificial Intelligence News and Discussions
One of the things that really annoys AI researchers is how supposedly “intelligent” machines are judged by much higher standards than are humans. Take self-driving cars, they say. So far they’ve driven millions of miles with very few accidents, a tiny number of them fatal. Yet whenever an autonomous vehicle kills someone there’s a huge hoo-ha, while every year in the US nearly 40,000 people die in crashes involving conventional vehicles.
Likewise, the AI evangelists complain, everybody and his dog (this columnist included) is up in arms about algorithmic bias: the way in which automated decision-making systems embody the racial, gender and other prejudices implicit in the data sets on which they were trained. And yet society is apparently content to endure the astonishing irrationality and capriciousness of much human decision-making.
If you are a prisoner applying for parole in some jurisdictions, for example, you had better hope that the (human) judge has just eaten when your case comes up. A fascinating empirical study, conducted in 2011 and peer-reviewed by the Nobel laureate Daniel Kahneman, found that “the percentage of favourable rulings drops gradually from about 65% to nearly zero within each decision session and returns abruptly to about 65% after a break. Our findings suggest that judicial rulings can be swayed by extraneous variables that should have no bearing on legal decisions.” Since an AI doesn’t need lunch, might it be more consistent in making decisions about granting parole?
In judging the debate about whether human intelligence (HI) is always superior to the artificial variety (AI), are we humans just demonstrating how capricious and irrational we can be? Er, yes, says Jason Collins, a behavioural and data scientist who now works for PwC Australia. In a wickedly satirical article in the online journal Behavioral Scientist, he turns the question we routinely ask about AI on its head: “Before humans become the standard way in which we make decisions,” he writes, “we need to consider the risks and ensure implementation of human decision-making systems does not cause widespread harm.”
Collins outlines four basic principles that we should apply before allowing humans to make critical decisions. The first is to avoid bias. This is difficult for humans because we are subject to a wide range of cognitive biases. Second, human-made decisions should be transparent, explicable and accountable. Indeed, but guess what? Humans are often inscrutable and while they can “create the impression of transparency through the verbal and written explanations that they offer, there is strong evidence that these explanations cannot be trusted to provide the true basis for the decision”. We might accurately think of people as black boxes, but with a better bedside manner than their purely algorithmic counterparts. Who can explain, for example, what goes on in what might loosely be called Boris Johnson’s mind? Third, human decision-making should be at least as good as AI or machine-learning alternatives. Sometimes, it turns out it’s not.
And, finally, human decisions should be consistent. This, too, we struggle with, although human judges make the best available stab at it. Two different humans confronted with the same decision will often come to a different conclusion, says Collins. The same human confronted with a decision on different occasions will also often decide inconsistently. In comparison, machines would be relentlessly consistent, at least in principle.
And the implications of all this? “Before humans become the standard way in which we make decisions,” says Collins, “we need to consider the risks and ensure implementation of human decision-making systems does not cause widespread harm.”
This is all good knockabout stuff and a good source of belly laughs for AI enthusiasts, but actually there’s a serious edge to it. For example, although bias is intrinsic in all machine-learning systems – and is just as common as it is in human decision-making systems – nevertheless, biased algorithms may be easier to fix than biased people.
That, at any rate, is the conclusion of a couple of empirical studies of racial bias in recruitment and healthcare published in the American Economic Review and Science. It turned out that uncovering algorithmic bias was relatively easy – it’s basically a statistical exercise. “The work was technical and rote, requiring neither stealth nor resourcefulness,” a researcher wrote. The humans in the system, on the other hand, were a different story. The researchers found them “inscrutable”, and discovered that “changing people’s hearts and minds is no simple matter”. Changing biased algorithms was “easier than changing people: software on computers can be updated; the ‘wetware’ in our brains has so far proven much less pliable”.
None of this should come as a surprise to anyone who knows anything about human nature. Our politics tell us that some people would rather die than change their minds. There’s something distinctively human about inconsistency, cognitive dissonance and sheer cussedness. And maybe that’s really why we fear AI: because it would be all the things that we are not.
What I’m reading
Down with democracy
The Punishment of Democracy by Will Davies is a remarkable, insightful reflection on the election campaign we have just lived through. Find it on Goldsmith’s Political Economy Research Centre’s site.
When Larry met Sergey
Nick Carr’s blog Larry and Sergey: A Valediction is a living obituary of Google’s co-founders, Larry Page and Sergey Brin, who have stepped down from managing the monster they created.
Go slow, wunderkinds
Guess what? Young people don’t make the best entrepreneurs. A heartening article, for oldies anyway, by Jeffrey Tucker on the website of the American Institute for Economic Research.
The Guardian – Artificial intelligence
euronews (in English) Among the flagship measures she will outline during a special plenary of the EU parliament in Brussels is a €100bn transition fund to boost green investment.…
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(MONROE, La.) — The luck has ran out for a Louisiana man allegedly caught rigging bingo games to win more than $10,000.
John Cook, 43, was booked into the Ouachita Correctional Facility on Friday on a felony theft charge and two counts for failing to appear multiple times following his June 29 arrest, according to a Monroe Police warrant obtained by news outlets.
Police say Cook was recorded on video at a bingo parlor manipulating a “Bonanza Bingo” game by handpicking the balls he wanted to play and then hiding the winner until he was ready to end the game. The warrant says Cook did this four times and won thousands for three people, including his sister.
One of the winners was captured speaking with Cook before the drawing, and two winners were seen giving Cook money after the game, the warrant states.
He’s in jail on an $11,000 bond.
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- Apple CEO Tim Cook doesn’t think the smartphone industry has peaked, he said when recently speaking with Nikkei Asian Review.
- His comments come as iPhone sales have slumped in recent quarters and shipments across the industry have largely remained stagnant.
- iPhone revenue is expected to return to growth again next year with the debut of Apple’s first 5G iPhone.
- Visit Business Insider’s homepage for more stories.
Apple CEO Tim Cook doesn’t think the smartphone industry is anywhere near reaching its peak.
"I know of no one who would call a 12-year-old mature," Cook said when speaking with Nikkei Asian Review, apparently referencing the 12 years since the first iPhone debuted — a milestone launch that’s largely perceived to have kicked off the smartphone era. "Sometimes these steps are humongous, sometimes these steps are smaller. But the key is to always make things better, not just change for change’s sake."
His comments come as iPhone sales have declined in recent quarters and smartphone shipments across the industry have slowed over the past several quarters. In its fiscal fourth quarter earnings results, Apple revealed that iPhone revenue had fallen by 9% year-over-year during that period, continuing a streak that’s persisted for several consecutive quarters.
And more broadly, the smartphone industry is only just starting to recover after facing a wave of declines. Global smartphone shipments grew by 0.8% year-over-year in the third quarter of 2019, according to the International Data Corporation, signalling the first sign of growth after seven quarters of declines.
The industry-wide slump has in some cases been attributed to the notion that many new flagship smartphones only offer minor upgrades over their predecessors, making it more difficult for smartphone makers like Apple to convince consumers to upgrade every year or two. Taken together, these trends have contributed to the notion that the modern smartphone has peaked.
Even before the company unveiled its iPhone 11 and 11 Pro in September, some Wall Street analysts had said this year’s new Apple smartphones would likely offer incremental updates over 2018’s iPhone XS and XS Max.
But those analysts also think next year’s iPhone, which is expected to support 5G connectivity and may feature a new design and 3-D cameras, could bring iPhone revenue back to growth.
Apple too believes iPhone sales will begin to trend upward again starting in 2020, as Bloomberg reported in October. Estimates from Strategy Analytics suggest that Apple could lead the 5G smartphone market in 2020, even despite the fact that it’s late to the market compared to rivals like Huawei and Samsung.
"The ethos and DNA of the company have never been stronger on the innovation front," Cook also said to Nikkei Asian Review. "The product line has never been stronger."
- Facebook and Google are no longer among the 10 best places to work in the US, according to employees
- The best tech of the decade
- How to see all the apps you’ve ever downloaded on your iPhone in 6 simple steps
Now, the Louisiana chain can add “ugly Christmas sweater” to the list.
That’s right. A Popeyes holiday sweater exists now, and it’s bestrewn with stitched sandwiches.
It’s available on UglyChristmasSweater.com, though
, this one is fairly tame.
The knit runs $44.95, roughly what it would cost you to buy 14 of the sandwiches with some change for fries.
So the choice is yours — would you rather devour the golden, buttermilk goodness or rep it?
In case you haven’t been following the
, it was released to great fanfare in August. It was so addictive, that crispy patty nestled between two brioche buns, that customers lined up around the store and out the door for hours just to score the sandwich.
, but the droughts have only fueled demand — and hunger.
May 2019 henceforth be known as the year the Chicken Sandwich Wars were won, not with violence but with
and food-inspired holiday garb.
CNN.com – RSS Channel – HP Hero