A new report shows that only 7 percent of new-car buyers in the US completed their purchase online, despite a major push by automakers, Amazon, and others to move past the dealership. In the US, cars follow only housing as the most expensive purchase consumers make. So it makes a lot of sense that, according to recent buyer surveys, very few of them want an Amazon-style, one-click approach to getting a new set of wheels. "People want to see, feel, and touch the car," says Erin Lomax, the vice president of consumer marketing at Cox Automotive, a research firm that also makes digital auto sales products that allow dealers to initiate transactions online. Not to mention test-driving the expensive thing they'll probably use every day.

As the free online encyclopedia turns 25, it's facing political opposition, AI scraping, dwindling volunteers, and a public that may no longer believe in its ideals. In 2010, the FBI sent Wikipedia a letter that would be intimidating for any organization to receive. The missive demanded that the free online encyclopedia remove the FBI's logo from an entry about the agency, claiming that reproducing the emblem was illegal and punishable with fines, imprisonment, "or both." Rather than back down, a lawyer for the Wikimedia Foundation, which hosts Wikipedia, shot back a sharp refusal outlining how the FBI's interpretation of the relevant statute was incorrect and saying that Wikipedia was "prepared to argue our view in court." It worked--the FBI dropped the matter.

Is Craigslist the Last Real Place on the Internet? Millennials are still using Craigslist to find jobs, find love, and even to cast creative projects--eschewing other AIand algorithm-dominated online spaces. The writer and comedian Megan Koester got her first writing job, reviewing internet pornography, from a Craigslist ad she responded to more than 15 years ago. Several years after that, she used the listings website to find the rent-controlled apartment where she still lives today. When she wanted to buy property, she scrolled through Craigslist and found a parcel of land in the Mojave Desert.

Craigslist, is an American Classified Advertisements website having various sections about housing, jobs, services (beauty, legal, health, etc.), and products for sale. Anyone can list a product or service on Craigslist for free and those interested can contact the poster. However, there are many listings on Craigslist that are not properly classified and are posted in incorrect sections. A particular category on the website that is of interest for us is the'Bikes' Section. Just like all other sections on the website, the'Bikes' Section of Craigslist, has many listings that do not belong there.

In this paper, we focus on the problem of extracting structured labeled data from short unstructured ad-postings from online sources like Craigslist, where ads are posted on various topics, such as job postings, rentals, car sales etc. A fundamental challenge in addressing this problem is that most ad-postings are highly unstructured, short-text postings written in an informal manner with no inherent grammar or well-defined dictionary. In this paper, we propose unsupervised and supervised algorithms for extracting structured data from unstructured ads in the form of (key, value) pairs where the keys naturally represent topic-specific features in the ads. The unsupervised algorithm is centered around building an affinity graph, using the words from a topic-specific corpus of such ads where the edge weights represent affinities between words; the (key, value) extraction algorithm identifies specific groups of words in the affinity graph corresponding to different classes of key attributes. The supervised algorithm uses a Conditional Random Field based training algorithm to identify specific structured (key, value) pairs based on pre-defined topic-specific structural data representations of ads. Based on a corpus of car and apartment ad-postings from Craigslist, the unsupervised algorithm reported an accuracy of 67.74% and 68.74% for car and apartment ads respectively. The supervised algorithm demonstrated an improved performance with accuracies of 74.07%

True collectors are fascinating people; they're smart and persistent. As time goes on, everyday objects fall out of fashion and then, years later, clever collectors swoop in. Scouring the auction sites is a good way to find valuables and evaluate treasures. Tap or click here for 5 sneaky eBay scams to watch out for. When you're ready to look beyond eBay, I have you covered with links to government, law enforcement and Department of Treasury auctions.

It was originally designed to rival Craigslist. And, now its totally eclipsed the defunct site, Facebook have enhanced their eBay-style Marketplace with artificial intelligence. The new feature, which launched on Tuesday to mark the site's second anniversary, will streamline the service by automatically adding price suggestions to listings. It will also include an auto-categorisation feature to make selling easier. Currently, Facebook utilises AI to automatically enhance the images uploaded by sellers - improving their brightness or saturation, for example. Similarly, it is used to seek-out and flag inappropriate content.

In the market for a used orbital launch vehicle? Do I have the deal for you! (Photo: Screenshot, Craigslist) CAPE CANAVERAL -- In the market for a "gently used" SpaceX orbital rocket? Oh boy, do I have the deal for you ... Someone has listed a gently used orbital launch vehicle for sale on Craigslist for $9.9 million, "or best offer." Good news, the "seller" accepts cryptocurrency, so you finally have an excuse to use those Bitcoin profits. And this sucker is a beaut.

Robots that slice, grill, assemble and bag 400 burgers in one hour could soon take over restaurants everywhere. Momentum Machines first unveiled its autonomous grill master, which can do the work of three human burger flippers, in 2012. Now the robotics company has secured $18 million (£14 million) in venture funding, reigniting old fears that the machines could soon replace human workers in fast-food restaurants. In 2012, Momentum Machines debuted its fully autonomous burger-making robot. Customers will be able to customize their meat with their own specifications, such as asking for their preferred ratio of pork to bison.

Targeted socio-economic policies require an accurate understanding of a country’s demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning-driven approaches are cheaper and faster—with the potential ability to detect trends in close to real time. In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to predict demographic attributes using the detected cars. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classified by car experts to account for even the most subtle of visual differences. We use this data to construct the largest scale fine-grained detection system reported to date. Our prediction results correlate well with ground truth income data (r=0.82), Massachusetts department of vehicle registration, and sources investigating crime rates, income segregation, per capita carbon emission, and other market research. Finally, we learn interesting relationships between cars and neighborhoods allowing us to perform the first large scale sociological analysis of cities using computer vision techniques.