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Coleco Chameleon, now just Chameleon, vanishes

Almost true to its namesake, what was once looked upon as a hero in the retro and indie gaming scene has seemingly faded into the background. But unlike the lizard, this Chameleon has truly vanished, at least as far as the Internet goes. Coleco Chameleon, or should we just say Chameleon now, and its parent group RetroVGS, have disappeared without a trace, after a barrage of accusations and controversy. Though no formal statement has been given yet, it is probably safe to presume that the Chameleon is dead.

The Chameleon console, then still called “Retro VGS”, was almost like a dream come true when it launched on Indiegogo late last year. It promised the world to lovers of retro style games, with the possibility of playing beloved console classics and offering a platform to build up new ones in the graphical and gameplay styles of old. The campaign didn’t meet its rather ambitious funding goal, so it tried yet again, this time on Kickstarter. But even before it could, it was hit hard by controversy from which it may not be able to recover.

Based on RetroVGS’ recounting of its experience at Toy Fair last month, you’d think things were going the Chameleon’s way. But it might have been far too early to count the chicken before they hatched and its public appearance may have done it more harm than good. While it did put out what looked like a working console, many, both on the Internet and on the floor, started suspecting the Chameleon was not it was supposed to be, that RetroVGS in fact used the guts of a model 2 SNES crammed inside an Atari Jaguar shell. RetroVGS would later produce an image “clear” shell prototype, which the Web was quick to point out as yet another fraud. The board inside resembled a video capture PCI card for PCs. RetroVGS pulled the picture down, only adding to the impression of their guilt.

Things seem to have finally come to a head. Retro magazine managing editor David Giltinan left the company because of ongoing problems with the console. Coleco Holdings, who owns the Coleco brand, has pulled the name out from the console. Atari COO Todd Shallbetter added that there was no agreement between the company and RetroVGS to have release old Atari 2600 games for the console.

With all odds against it, RetroVGS has disappeared from the Internet, both its website and its Facebook page are MIA. Whether RetroVGS was intentionally trying to fool people from the beginning, or if they simply were hard pressed to show progress when they really had nothing ready, is something we’ll have to wait and see. It is definitely a tragedy considering how this case could very well affect more popular and more reliable Retro products, like its magazines.

VIA developer Frank Cifaldi on Twitter: “Evidence suggests the new Coleco prototype at Toy Fair might literally be a SNES Jr. duct-taped into a Jaguar shell.”

VIA: Engadget

You're reading Coleco Chameleon, Now Just Chameleon, Vanishes : Sometimes You Just Gotta Ask

Back in March I looked at the new Ask and wondered if it might begin to compete with Google, Yahoo and MSN given all of the improvements mentioned in Barry Diller’s key-note speaker presentation at the Search Engine Conference in New York.

However, just yesterday my daughter asked me a question that I couldn’t answer. (Let’s face it, parents don’t always have the answers). The question she posed was the age-old child’s conundrum, “Why is the sky blue?”

Well, I wasn’t sure exactly what to say since science was never my strong suit. So I suggested we pose the question on the Internet to see what we could discover together.

The third result on Google was for Sky Blue (the movie).

Google Answers was, of course, the first line offering to have their researchers answer our question in return for payment. That;s definitely out of the question since researching has always been my strong suit.

But what I really liked about Ask’s results was that the related search inspired my daughter to learn more. She saw the expanded results and went on to also discover why grass is green, why the ocean is blue and why clouds are white.

Just for the sake of comparison, I posed the same question on Yahoo and MSN.

Yahoo also offered a link to a why is the sky blue science fair project related search as well as a link to learn about why the sky is blue on Yahooligans.

MSN didn’t attempt an answer at all. I thought they might have drawn from the encyclopedia results they touted at one time, but there was no attempt to answer the question directly on the results page. They also served up a few sponsored results, including two to eBay. The seventh result on the page was for Online Casino Help which, in MSN’s defense, is strangely located at

All in all, my daughter learned that

The sun’s rays hit the Earth’s atmosphere, where the light is scattered by nitrogen and oxygen molecules in the air. The blue wavelength of this light is affected more than the red and green wavelengths, causing the surrounding air to appear blue.

She also learned that grass appears green because all of the colors of the rainbow are absorbed into the leaves of the grass except green and that clouds are white because the water droplets in the clouds are large enough to scatter the seven colors which combine to produce white

The other questions were not as neatly answered by Ask on their search results page, but if we’d not searched Ask, we’d have never even thought of the related questions; at least not that day anyway.

I learned that I just might have been right to think that Ask is ready to join the other players on the search playing field. I predicted that Ask might easily take over the second position, following Google’s huge market share. If Ask continues to concentrate on search, produce talked-about commercials and provide reliable search results, they might just give Google a run for the money. I’m certainly convinced that Ask did a better job of answering my daughter’s question. And there are a lot of daughters out there.

Lisa Melvin is a Search Marketing Specialist at chúng tôi an SEO Agency in Maryland, and has been helping clients maximize their search engine visibility since 1998.

Getting A Job Just Got Easier

Getting a Job Just Got Easier Career center’s new name reflects expanded mission

Having just earned a master’s degree in molecular biology, Laura Owens suddenly found herself questioning her career choice. Uncertain about the best job for her skills, she turned—“out of desperation,” she says—to BU’s Career Services office.

“The help I received was outstanding,” recalls Owens (GRS’10). The center’s six months of “really specialized assistance” produced career options she doubts she could have come up with herself. She now plans to study to be a dietitian.

Yet many students, she says, are “only vaguely aware” of BU’s career-planning help. That is in the process of changing. As luck would have it, President Robert A. Brown approved an expansion of Career Services just before the Great Recession hit. Today the office is officially being renamed the Center for Career Development, reflecting the expansion of the services being provided. The newly renamed center will offer more employer contacts, more job-hunting and networking help for students, and a centralized online bank of employment and internship opportunities.

Scheduled to relocate to the new East Campus Student Center when it is finished next year, the center (formerly Career Services) had 10 staffers when director Kimberly DelGizzo arrived at BU a year and a half ago; now there are 12, a number she hopes to grow by two or three this fiscal year, and ultimately to perhaps 20.

With more staff, “we’re doing more of what we’ve been doing, but we’re hoping to make it more visible and accessible,” DelGizzo says. Owens’ observation about students underusing services is spot-on: “We had employers posting internships and jobs and saying to us, ‘We’re not receiving enough résumés,’” according to DelGizzo.

That’s starting to change. Early in DelGizzo’s tenure, students who wanted to talk to a counselor got in quickly. But last semester, the wait time for an appointment was several weeks, she says. The higher demand, while good, necessitates more staff to move traffic faster. “Currently, we have five people doing career counseling for 32,000 students,” she says.

Among the new changes in service to students:

Online job postings. The center, which serves students University-wide as well as alumni, has partnered with career offices in several schools—the College of Communication, the College of Engineering, the School of Management, and the School of Hospitality Administration—in a new online system to manage employers’ job and internship postings, giving employers the option of tapping all BU students or targeting specific schools

Expanded Career Expo. The center is working to increase the number of employers who show up at this twice-a-year event. Eleanor Cartelli, the center’s associate director of marketing and communications, says 700 students mingled with more than 80 recruiters at the fall expo; the goal is to get up to 100 recruiters to participate in this semester’s expo, on February 16. “We’re really working hard to get a lot more students in the door” this time, she says.

She also wants to change the mindset that career services are only for seniors in the spring before they graduate. It’s never too early to think about a career, she says; for example, snaring an internship during college could lead to a job after, since “oftentimes employers look at their internship pool” for possible hires. Her counselors can also brainstorm about other pursuits, from undergraduate research to community service to leadership in a student organization to study abroad, that can enhance a student as a prospective hire later on, she says.

More pitches to employers. The center has just added a new position for a staff member to serve as a liaison with employers. It did a first-of-its-kind survey of all but the freshman class last fall, asking students how they had spent the summer. Knowing students’ interests and experience, DelGizzo says, will guide the center’s pitches to lure employers.

The center’s expansion comes as unemployment among college graduates hit a 40-year high of 5.1 percent in November. (A college degree remains a sound investment, as joblessness among those with just a high school diploma was almost twice that rate, and was more than three times as high among high school dropouts.)

Some individual BU colleges currently track how many of their graduates score jobs after Commencement, but the center has never tracked the graduating class as a whole, DelGizzo says.

Starting with this May’s graduates, that too will change.

Rich Barlow can be reached at [email protected].

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Lenovo Smart Display 7 Review: Just Right

Lenovo Smart Display 7

The Lenovo Smart Display 7 is an alternative to the Google Nest Hub. These two compact smart displays are on even footing and not much separates them from a feature and performance perspective. That said, Lenovo’s display is newer, louder, and offers video chats.

This device is no longer widely available. The Lenovo Smart Display 7 is now unavailable to buy from most retailers. If you are looking for an alternative device, check out our list of the


The Lenovo Smart Display 7 is now unavailable to buy from most retailers. If you are looking for an alternative device, check out our list of the best smart displays you can buy and the best tablets you can buy

With a wider variety of prices, sizes, and colors now on offer, smart displays such as the Lenovo Smart Display 7 are sure to be popular gifting items this holiday season. Whether or not this particular Lenovo product is on your shopping list, it’s a fine little in-home assistant that fits just about anywhere.

The Lenovo Smart Display 7 goes head-to-head with Google’s own Nest Hub, as well as Amazon’s line of Echo Show smart displays. Where does the Lenovo fit in and is it worth your money? We answer this and more in Android Authority‘s Lenovo Smart Display 7 review.

Physical controls placed on the top edge let you adjust volume, turn the mic off, and, if you reach over the top, slide a switch to cover the camera. Yay, privacy!

See also: Best smart home gadgets you can buy

What can the Smart Display 7 do?

Android Things and Google Assistant are powerful, and yet still somewhat limited where smart displays are concerned.

Research suggests that listening to music is the number one activity when it comes to smart speakers. Naturally, this capability is carried over to smart displays. You can link the Smart Display 7 with the streaming music service of your choice (Google Play Music, YouTube Music, Spotify, Pandora, Deezer, etc.) and enjoy some tunes while you prepare dinner. Depending on the service, you’ll see album covers and other content on the screen itself along with playback controls. You can play/pause or fast-forward/rewind by asking or by tapping the screen. Music is simple to master, though I wish it sounded better.

No matter what I did or how I tried, I couldn’t adjust the sound of the two 5W speakers to my liking. To my ears, the speakers produced a harsh sound with the mids and highs boosted too much by far. There’s no bottom end, so don’t expect to be thump thump thumping with the Smart Display 7. It’s fine for filling the void and casual listening, and can push out some serious volume if you want it too. Moreover, you can pair it with other Google-based smart speakers to create a group. This is all managed in the Google Home app and is a cinch to set up.

The display itself is the best photo frame you can buy. Paired with your favorite albums in Google Photos, the Smart Display 7 provides an unending slide show that drifts from picture to picture throughout the day. This might be its best feature.

Watching video is a mixed bag, unfortunately, and not as simple as it should be. I have to point out that the Lenovo Smart Display 7 has the exact same limitations as other Android Things smart displays, so these shortcomings aren’t unique.

In the mood for (just about anything on) YouTube? Simply say, “Hey Google, show me Android Authority videos on YouTube,” and it’ll take you to the channel where you can tap the screen to select something to watch. There is no YouTube or video app, but the Smart Display 7 can be a cast target. This means you can use your phone to push content from Google Play Movies, Disney Plus, and Hulu to the screen. The bummer here is that the phone is a necessary intermediary, as the platform doesn’t support commands such as, “Hey Google, play The Mandalorian on Disney Plus.”

And of course, the Smart Display 7 can help you control your smart home products, set timers, add calendar appointments, search for anything, and use all the Google Assistant powers we rely on.

See also: Move music streams between smart home speakers

What do I like about the Smart Display 7?

Also read: Lenovo Smart Clock vs Google Nest Hub

Automation Without Intelligence Is Just Not Smart

Automation Without Intelligence is Just Not Smart Ben Bradley

Director of Strategic Accounts – Client Consulting

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The numbers are clear. Most marketers (nearly 70% of businesses) are using some type of Automation system. While there are many success metrics supporting the use of these systems (here and here are just a couple examples) this post isn’t about why you need to use an automation tool – it’s about how you can make it better.

The thing that that is missing from most intelligence systems is insight into what happens beyond your brands touch points. Most intelligence systems can tell you what is happening on your site, or with the brand engagements that you load.

However as marketers we need to remember the average short list of a B2B IT buyer consists of 2-3 vendors. We need to remove the mindset that the only relevant research our accounts/contacts have is with our brand.

The core theme that is repeated every single time is that an IT buying account will always engage with editorial content and other vendors equally, if not more than, with your brand. It happens to all of us, it’s a natural part of the research process to gather un-biased info, and then compare solutions and vendors.  The proof, when we looked at over 4,000 confirmed IT Buying projects and their content consumption journeys we saw:

70% of their content consumption journey was with Editorial content (Tips, news, eGuides, eZines, etc.) on the TechTarget network

30% of their content consumption journey was with Vendor content on the TechTarget network

Your view into the buyer’s content journey is further reduced when you consider that the average IT buyer looked at content from about 17 vendors, and has a short list of about 2 or 3 vendors. This reduces your part of the 30% vendor content consumption quickly. Intelligence is only truly gained when you get a view into ALL this activity.

Segmentation is the Holy Grail

One of my favorite marketers Avinash Kaushik has written a lot about the value of segmentation.  A sophisticated marketer is going to segment nurture streams to offer different content and experiences based on their user’s/accounts engagements. The key to segmentation is being able to pull in as many data points as possible.

Automation without intelligence

Let’s look at the above account journey example to see how you might nurture a contact from this journey. You could segment by:

One of The 11 assets they have engaged with

The event data you have (location, contact)

The site visits they had (when maybe who if they completed a reg page).

Automation with intelligence

Automation without intelligence has already been proven to drive massive returns, now imagine how much more effective it can be if you have many additional data points to segment by:

ALL Contacts at the account that have engaged in relevant research, not just those that download Your content

Priority ranking of all accounts based on key signals and research volume

Insight into which competing vendors they are downloading content from and the volume of engagement they have with each

Size and locations of the buying team, and changes to the team over the last 30,60,90 days

Key pain points and the frequency of research on those (IE – data storage management vs. disaster recovery)

The nurture stream and lead management process could dramatically change with this example:

You could launch a competitive attack campaign based on the knowing that Named Account X has downloaded 25% of their content from Competitor Y

You could deliver thought leadership content in the perfect cadence

Expand your account reach and influence on an account level with access to net new contacts

Adjust nurture streams based the volume and types of content the brand engaged with holistically

Use “signals” such as “Late Stage Signal” to identify when account view product spec sheets, demos and other late buy cycle stage content.

Deliver content based on specific pain points

All of this is based on the account journey across your content, your competitor’s content, and editorial content so you are given a better view into the buyer’s journey.

Intelligence only works when it’s actionable

Intelligence should be the foundation of your marketing strategy. Today’s marketers need to make sure they have access actionable intelligence. Actionable intelligence is being able to message contacts based on every step every step of their content journey, even those steps that are not with you.

Account-Based Marketing, audience segmentation, B2B marketing, content, marketing intelligence

Master Computer Vision In Just A Few Minutes!

This article was published as a part of the Data Science Blogathon


Human vision is lovely and complex. It all commenced billions of ages ago when tiny organisms developed a mutation that made them sensitive to light.

Fast forward to today, and life is abundant on the planet, which all have very similar visual systems. They include eyes for capturing light, receptors in the brain for accessing it, and visual cortex processing.

Genetically engineered and balanced pieces of a system help us do things as simple as appreciating a sunrise. But this is just the beginning.

In the past 30 years, we’ve made even more strides to extending this unique visual ability, not just to us but to computers as a whole.

A little bit of History

The first type of the photographic camera invented around 1816 was a small box that held a piece of paper coated with silver chloride. When the shutter was open, the silver chloride would darken when exposed to light.

Understanding what’s in the photo is much more difficult.

Consider this picture below:

Our human brain can look at it and immediately know that it’s a flower. Our brains are tricking since we’ve got several million years’ worths of evolutionary context to directly understand what is better.


There’s no connection here, just a massive quantity of data. It turns out that context is the crux of getting algorithms to understand image content in the same way that the human brain does.

And to get this work, we practice an algorithm very comparable to how the human brain functions adopting machine learning. Machine learning enables us to adequately train the context for data so that an algorithm can learn what all these numbers in a particular group serve.

And what if we have images that are difficult for a human to classify? Can machine learning achieve better accuracy?

For example, let’s take a look at these images of sheepdogs and mops where it’s pretty hard, even for us, to differentiate between the two.


With the machine learning model, we can get a collection of images of sheepdogs and mops. Then, as deep as we feed it sufficient data, it will ultimately tell the variation among the two accurately.

Computer vision is taking on increasingly complex challenges and is seeing accuracy that rivals humans are performing the same image recognition tasks.

But like humans, these models aren’t perfect. So they do sometimes make mistakes. The specific type of neural network that accomplishes this is called a convolutional neural network or CNN.

Role of Convolutional Neural Networks in Computer Vision


CNN operates by dividing a picture down into more petite groups of

pixels called a filter. Every filter is a matrix of pixel values. Then, the network performs calculations on these pixels, comparing them against pixels in a specific pattern the network is looking at.

In the initial layer of a CNN, it can recognize high-level patterns like uneven edges and sweeps. Then, as the network functions more convolutions, it can classify different things like faces and animals.

How does a CNN know what to look for and if its prediction is accurate?

A large amount of labeled training data helps in the process. When the CNN starts, all of the filter values are randomized. As a decision, its first predictions present slight sense.

Each time the CNN predicts labeled data, it uses an error function to compare how close its forecast was to the image’s actual label. Based on this error or loss function, the CNN updates its filter values and starts the process again. Ideally, each iteration performs with slightly more accuracy.

What if we want to explore a video using machine learning instead of analyzing a single image?

At its essence, a video is just a sequence of picture frames. To analyze footage, we can build on our CNN for image analysis. In noiseless pictures, we can apply CNNs to recognize features.

But when we shift to video, everything gets more complicated as the items we’re recognizing might evolve overhead time. Or, more likely, there’s a context between the video frames that’s highly important to labeling.

For example, if there’s a picture of a half-full cardboard box, we might want to label it packing a box or unpacking a box depending on the frames before and after it.


Now is when CNN’s come up lacking. They can take into spatial report characteristics, the visual data in a picture, but

can’t manipulate temporal or time features like how a frame is

similar to the one before it.

To address this issue, we have to take the output of our CNN and feed it into another model that can handle our videos’ temporal nature described as a recurrent neural network or RNN.

Role Of Recurrent Neural Networks in Computer Vision


While a CNN treats groups of pixels independently, an RNN can retain information about its already processed and use that in its decision-making.

RNNs can manage various sorts of input and output data. An example of classifying videos, we train the RNN by passing it a sequence of frame descriptions

-empty box

-open box

-closing box

And finally, a label- packing.

As the RNN processes a specific sequence, it practices a loss or error function to match its predicted output amidst the correct label. Then it adjusts the weights and processes the series again until it achieves higher accuracy.

However, the challenge of these approaches to image and video models is that the amount of data we need to mimic human vision is tremendous.

If we train our model to analyze this photo of a duck, as long

as we’re given this one picture with this lighting, color, angle, and shape, we can see that it’s a duck.

Change any of that or even rotate the duck; the algorithm might not understand what it is anymore. So now, this is the signature design problem.

To get an algorithm to truly understand and recognize image content the way the human brain does, you need to feed it substantial amounts of data of millions of objects across thousands of angles, all annotated and adequately defined.

The problem is so big that if you’re a small startup or a

company lean on funding, there are just no resources available for you to do that.

Note: Consequently, technologies like Google Cloud Vision and Video can help. Google understands and filters millions of photographs and videos to train specific APIs. They introduced a network to extract all kinds of data from images and video so that your application doesn’t have to. With just one REST API request, they can access a powerful pre-trained model that gives us all sorts of data.


Billions of years since the evolution of our sense of sight, we found that computers are on their way to matching human vision. Computer vision is an innovative track that practices the latest machine learning technologies to construct software systems that help humans beyond complex areas. From retail to wildlife preservation, intelligent algorithms unlock image classification and pattern recognition problems, sometimes even thoroughly than humans.

About Author

Mrinal Walia is a professional Python Developer with a Bachelors’s degree in computer science specializing in Machine Learning, Artificial Intelligence and Computer Vision. Mrinal is also an interactive blogger, author, and geek with over four years of experience in his work. With a background working through most areas of computer science, Mrinal currently works as a Testing and Automation Engineer at Versa Networks, India. My aim to reach my creative goals one step at a time, and I believe in doing everything with a smile.

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