Scott Elliott Fahlman (born March 21, 1948) is an American computer scientist and Professor Emeritus at Carnegie Mellon University's Language Technologies Institute and Computer Science Department. He is notable for early work on automated planning and scheduling in a blocks world, on semantic networks, on neural networks (especially the cascade correlation algorithm), on the programming languages Dylan, and Common Lisp (especially CMU Common Lisp), and he was one of the founders of Lucid Inc. During the period when it was standardized, he was recognized as "the leader of Common Lisp." From 2006 to 2015, Fahlman was engaged in developing a knowledge base named Scone, based in part on his thesis work on the NETL Semantic Network. He also is credited with coining the use of the emoticon. == Life and career == Fahlman was born in Medina, Ohio, the son of Lorna May (Dean) and John Emil Fahlman. He attended the Massachusetts Institute of Technology (MIT), where he received a Bachelor of Science (B.S.) and Master of Science (M.S.) degree in electrical engineering and computer science in 1973, and a Doctor of Philosophy (Ph.D.) in artificial intelligence in 1977. He has noted that his doctoral diploma says the degree was awarded for "original research as demonstrated by a thesis in the field of Artificial Intelligence" and suggested that it may be the first doctorate to use that term. He is a fellow of the American Association for Artificial Intelligence. Fahlman acted as thesis advisor for Donald Cohen, David B. McDonald, David S. Touretzky, Skef Wholey, Justin Boyan, Michael Witbrock, and Alicia Tribble Sagae. From May 1996 to July 2001, Fahlman directed the Justsystem Pittsburgh Research Center. === Boltzmann Machine (1983) === In 1983, Fahlman, Geoffrey Hinton, and Terry Sejnowski published a paper in Proceedings of the AAAI-83 Conference, Washington DC, August 1983. The paper was titled as "Massively Parallel Architectures for AI: NETL, Thistle and Boltzmann Machines". === Emoticons === Fahlman was not the first to suggest the concept of the emoticon – a similar concept for a marker appeared in an article of Reader's Digest in May 1967, although that idea was never put into practice. In an interview printed in The New York Times in 1969, Vladimir Nabokov noted: "I often think there should exist a special typographical sign for a smile – some sort of concave mark, a supine round bracket." Fahlman is credited with originating the first smiley emoticon, which he thought would help people on a message board at Carnegie Mellon to distinguish serious posts from jokes. He proposed the use of :-) and :-( for this purpose, and the symbols caught on. The original message from which these symbols originated was posted on 19 September 1982. The message was recovered by Jeff Baird on 10 September 2002 and read: 19-Sep-82 11:44 Scott E Fahlman :-) From: Scott E Fahlman
Sprayprinter
SprayPrinter is a device that attaches to aerosol paint cans whereby users can print images via Bluetooth from a smartphone onto a wall or almost any surface. == History == The technology behind SprayPrinter was developed by Mihkel Joala. He explained in a 2016 interview with New Atlas that his idea was inspired by the modern car engine and the Nintendo Wii console. "Engines nowadays use extremely fast valves to spray fuel to [the] combustion chamber," says Joala. "I realized I can use them to shoot paint with pinpoint accuracy." As of December 2021, the company appears to be no longer selling products. == Awards and Recognitions == In 2015, SprayPrinter received €8,000 from the Estonian prototyping contest Prototron for its initial prototype. In 2016, the SprayPrinter team won the grand prize of €30,000 from the televised pitching competition Ajujaht.
KoalaPad
The KoalaPad is a graphics tablet, released in 1983 by US company Koala Technologies Corporation, for the Apple II, TRS-80 Color Computer (as the TRS-80 Touch Pad), Atari 8-bit computers, Commodore 64, and IBM PC compatibles. Originally designed by Dr. David Thornburg as a low-cost computer drawing tool for schools, the Koala Pad and the bundled drawing program, KoalaPainter, was popular with home users as well. KoalaPainter was called KoalaPaint in some versions for the Apple II, and PC Design for the IBM PC. A program called Graphics Exhibitor was included for creating slideshow presentations from KoalaPainter drawings. == Description == The pad was four inches square (i.e. roughly 10×10 cm) and mounted on a slightly inclined base with the back of the pad higher than the front. At the top, "behind" the pad, were two buttons. The pad hooked into the computer using the analog signals of the joystick ports (the so-called paddle inputs), which meant that it had a low resolution and tended to jostle the cursor if moved during use. As an alternative to the drawing stylus, the pad could as easily be operated by the user's fingers for tasks that demanded less precision, such as selecting between menu items (thus using the pad as a kind of "indirect touch screen"). The top-mounted buttons tended to be somewhat frustrating to use, as the user had to "reach around" the stylus to push the buttons in order to start or stop drawing. A similar tablet from Atari, the Atari CX77 Touch Tablet, addressed this with a built-in button on the stylus, which some enterprising users adapted for use with their KoalaPad. == KoalaPainter == The pad shipped with a simple bitmap graphics editor developed by Audio Light called KoalaPainter, PC Design or Micro Illustrator depending on the target machine (see release history). Although bundled with the pad, KoalaPainter could also be operated using an ordinary digital joystick. One unique feature of the program, for its time, was that it held two pictures in the computer's memory, allowing the user to flip from one to the other—a function commonly used in order to study the differences between an original and a modified picture, and to copy and paste between two different pictures. Some third-party bitmap editors could also be used with the KoalaPad, such as Broderbund's Dazzle Draw for the Apple II. === Release history === KoalaPainter for Commodore 64 (1983) and Atari 8-bit computers (1983) PC Design for the IBM PC (1983) Micro Illustrator for the Apple II (1983), Atari 8-bit computers (1983) and Commodore Plus/4 (1984) KoalaPainter II for Commodore 64 (1984) === Reception === Ahoy! called KoalaPainter "a very powerful and effective color drawing package", and concluded that it and the KoalaPad were "excellent in ease of use, a fine choice for a beginner as well as young children". BYTE's reviewer stated in December 1984 that he made far fewer errors when using an Apple Mouse with MousePaint than with a KoalaPad and its software. He found that MousePaint was easier to use and more efficient, predicting that the mouse would receive more software support than the pad. Cassie Stahl in InfoWorld's Essential Guide to Atari Computers praised the tablet and its documentation, rating it "Excellent" among all categories and stating that "Playing with the KoalaPad becomes addictive. It does everything it claims to, and it does it well". She also liked Micro Illustrator, rating it "Excellent" except for "Good" for Performance. While criticizing the limited erase function, Stahl reported an undocumented feature enabling exporting pictures to other software. === File format === The Commodore 64 version of KoalaPainter used a fairly simple file format corresponding directly to the way bitmapped graphics are handled on the computer: A two-byte load address, followed immediately by 8,000 bytes of raw bitmap data, 1,000 bytes of raw "Video Matrix" data, 1,000 bytes of raw "Color RAM" data, and a one-byte Background Color field. == KoalaWare == Koala Technologies offered more software beyond the bundled KoalaPainter and Graphics Exhibitor for use with the pad. Among these applications, marketed under the moniker KoalaWare (like KoalaPainter itself), was educational software for use with customized keypads and overlays, such as spelling tools, music programs, and mathematics instruction software, as well as software for "translating" graphical designs into Logo programs.
Multi-focus image fusion
Multi-focus image fusion is a multiple image compression technique using input images with different focus depths to make one output image that preserves all information. == Overview == The main idea of image fusion is gathering important and the essential information from the input images into one single image which ideally has all of the information of the input images. The research history of image fusion spans over 30 years and many scientific papers. Image fusion generally has two aspects: image fusion methods and objective evaluation metrics. In visual sensor networks (VSN), sensors are cameras which record images and video sequences. In many applications of VSN, a camera can't give a perfect illustration including all details of the scene. This is because of the limited depth of focus of the optical lens of cameras. Therefore, just the object located in the focal length of camera is focused and clear, and other parts of the image are blurred. VSN captures images with different depths of focus using several cameras. Due to the large amount of data generated by cameras compared to other sensors such as pressure and temperature sensors and some limitations of bandwidth, energy consumption and processing time, it is essential to process the local input images to decrease the amount of transmitted data. == Multi-Focus image fusion in the spatial domain == Huang and Jing have reviewed and applied several focus measurements in the spatial domain for the multi-focus image fusion process, suitable for real-time applications. They mentioned some focus measurements including variance, energy of image gradient (EOG), Tenenbaum's algorithm (Tenengrad), energy of Laplacian (EOL), sum-modified-Laplacian (SML), and spatial frequency (SF). Their experiments showed that EOL gave better results than other methods like variance and spatial frequency. == Multi-Focus image fusion in multi-scale transform and DCT domain == Image fusion based on the multi-scale transform is the most commonly used and promising technique. Laplacian pyramid transform, gradient pyramid-based transform, morphological pyramid transform and the premier ones, discrete wavelet transform, shift-invariant wavelet transform (SIDWT), and discrete cosine harmonic wavelet transform (DCHWT) are some examples of image fusion methods based on multi-scale transform. These methods are complex and have some limitations e.g. processing time and energy consumption. For example, multi-focus image fusion methods based on DWT require a lot of convolution operations, so they take more time and energy to process. Therefore, most methods in multi-scale transform are not suitable for real-time applications. Moreover, these methods are not very successful along edges, due to the wavelet transform process missing the edges of the image. They create ringing artefacts in the output image and reduce its quality. Due to the aforementioned problems in the multi-scale transform methods, researchers are interested in multi-focus image fusion in the DCT domain. DCT-based methods are more efficient in terms of transmission and archiving images coded in Joint Photographic Experts Group (JPEG) standard to the upper node in the VSN agent. A JPEG system consists of a pair of an encoder and a decoder. In the encoder, images are divided into non-overlapping 8×8 blocks, and the DCT coefficients are calculated for each. Since the quantization of DCT coefficients is a lossy process, many of the small-valued DCT coefficients are quantized to zero, which corresponds to high frequencies. DCT-based image fusion algorithms work better when the multi-focus image fusion methods are applied in the compressed domain. In addition, in the spatial-based methods, the input images must be decoded and then transferred to the spatial domain. After implementation of the image fusion operations, the output fused images must again be encoded. DCT domain-based methods do not require complex and time-consuming consecutive decoding and encoding operations. Therefore, the image fusion methods based on DCT domain operate with much less energy and processing time. Recently, a lot of research has been carried out in the DCT domain. DCT+Variance, DCT+Corr_Eng, DCT+EOL, and DCT+VOL are some prominent examples of DCT based methods.
Time-compressed speech
Time-compressed speech refers to an audio recording of verbal text in which the text is presented in a much shorter time interval than it would through normally-paced real time speech. The basic purpose is to make recorded speech contain more words in a given time, yet still be understandable. For example: a paragraph that might normally be expected to take 20 seconds to read, might instead be presented in 15 seconds, which would represent a time-compression of 25% (5 seconds out of 20). The term "time-compressed speech" should not be confused with "speech compression", which controls the volume range of a sound, but does not alter its time envelope. == Methods == While some voice talents are capable of speaking at rates significantly in excess of general norms, the term "time-compressed speech" most usually refers to examples in which the time-reduction has been accomplished through some form of electronic processing of the recorded speech. In general, recorded speech can be electronically time-compressed by: increasing its speed (linear compression); removing silences (selective editing); a combination of the two (non-linear compression). The speed of a recording can be increased, which will cause the material to be presented at a faster rate (and hence in a shorter amount of time), but this has the undesirable side-effect of increasing the frequency of the whole passage, raising the pitch of the voices, which can reduce intelligibility. There are normally silences between words and sentences, and even small silences within certain words, both of which can be reduced or removed ("edited-out") which will also reduce the amount of time occupied by the full speech recording. However, this can also have the effect of removing verbal "punctuation" from the speech, causing words and sentences to run together unnaturally, again reducing intelligibility. Vowels are typically held a minimum of 20 milliseconds, over many cycles of the fundamental pitch. DSP systems can detect the beginning and end of each cycle and then skip over some fraction of those cycles, causing the material to be presented at a faster rate, without changing the pitch, maintaining a "normal" tone of voice. The current preferred method of time-compression is called "non-linear compression", which employs a combination of selectively removing silences; speeding up the speech to make the reduced silences sound normally-proportioned to the text; and finally applying various data algorithms to bring the speech back down to the proper pitch. This produces a more acceptable result than either of the two earlier techniques; however, if unrestrained, removing the silences and increasing the speed can make a selection of speech sound more insistent, possibly to the point of unpleasantness. == Applications == === Advertising === Time-compressed speech is frequently used in television and radio advertising. The advantage of time-compressed speech is that the same number of words can be compressed into a smaller amount of time, reducing advertising costs, and/or allowing more information to be included in a given radio or TV advertisement. It is usually most noticeable in the information-dense caveats and disclaimers presented (usually by legal requirement) at the end of commercials—the aural equivalent of the "fine print" in a printed contract. This practice, however, is not new: before electronic methods were developed, spokespeople who could talk extremely quickly and still be understood were widely used as voice talents for radio and TV advertisements, and especially for recording such disclaimers. === Education === Time-compressed speech has educational applications such as increasing the information density of trainings, and as a study aid. A number of studies have demonstrated that the average person is capable of relatively easily comprehending speech delivered at higher-than-normal rates, with the peak occurring at around 25% compression (that is, 25% faster than normal); this facility has been demonstrated in several languages. Conversational speech (in English) takes place at a rate of around 150 wpm (words per minute), but the average person is able to comprehend speech presented at rates of up to 200-250 wpm without undue difficulty. Blind and severely visually impaired subjects scored similar comprehension levels at even higher rates, up to 300-350 wpm. Blind people have been found to use time-compressed speech extensively, for example, when reviewing recorded lectures from high school and college classes, or professional trainings. Comprehension rates in older blind subjects have been found to be as good, or in some cases better than those found in younger sighted subjects. Other studies have determined that the ability to comprehend highly time-compressed speech tends to fall off with increased age, and is also reduced when the language of the time-compressed speech is not the listener's native language. Non-native speakers can, however, improve their comprehension level of time-compressed speech with multiday training. === Voice Mail === Voice mail systems have employed time-compressed speech since as far back as the 1970s. In this application, the technology enables the rapid review of messages in high-traffic systems, by a relatively small number of people. === Streaming Multimedia === Time-compressed speech has been explored as one of a variety of interrelated factors which may be manipulated to increase the efficiency of streaming multimedia presentations, by significantly reducing the latency times involved in the transfer of large digitally encoded media files.
Amazon Q
Amazon Q is a chatbot developed by Amazon for enterprise use. Based on both Amazon Titan and GPT-5, it was announced on November 28, 2023. At launch, it was a part of the Amazon Web Services management console. Amazon CodeWhisperer is a part of Amazon Q Developer, a part of Amazon Q. == History == Amazon's business-focused chatbot Q was announced on November 28, 2023 in a preview, with a full version available at $20 per person per month. On July 19, 2025, the Amazon Q Visual Studio Code extension was compromised to delete the user's home directory. The issue was fixed on July 21. == Capabilities == Q can be prompted to summarize long documents and group chats, create charts, data analysis and write code. Q is also capable of accessing non-Amazon services. The chatbot is based on Amazon Titan and GPT-5, and uses the Amazon Bedrock repository of foundational models. It is part of the Amazon Web Services management console.
Be My Eyes
Be My Eyes is a Danish mobile app that aims to help blind and visually impaired people to recognize objects and manage everyday situations. An online community of sighted volunteers receive photos or videos from randomly assigned affected individuals and assist via live chat. In 2023, the company launched Be My AI, an AI-based interface to help blind and visually impaired users describe images. The app is currently available for Android, iOS, and Windows. == History == === Founding and early years === The app was developed and marketed by Hans Jørgen Wiberg. He had demonstrated that although there are video chat software such as Skype and FaceTime, none is tailored for the visually impaired. For development, he joined forces with the Danish Association of the Blind, and other organizations. The app was first presented at an event for start-up companies in 2012 and first released in 2015. A version for Android was released in 2017, in addition to the iOS version. Praise was given for easy use of the app. The lack of sufficient data protection, which makes it possible to pass on data to third parties, was criticized. === Recent developments === The company has raised over $650,000, including funding from Silicon Valley, Microsoft, and other angel investors. In February 2020, $2.8 million in Series A funding was raised, allowing the company to further develop its business model while keeping visual support services free for visually impaired users. The investment allows the company to further develop its unique "purpose and profit" business model while keeping the visual support service free and unlimited for all visually impaired users. === User base and accessibility === Over 9.3 million volunteers and 900,000 blind or visually impaired people use the app. == Features == === Human-based assistance === A visually impaired person starts a live stream showing their view from their cellphone camera. They are assigned, through a phone call or chat, a random volunteer who speaks the same language and who is in the same time zone. This allows the volunteer to describe an object and assist the visually impaired person, such as guiding the person to move their camera, read instructions, or clean up a spill. Through speech synthesis, content can be read out loud. This process encourages a more independent life for blind and visually impaired people. === Be My AI === In March of 2023, Be My Eyes launched Be My AI, an AI-based virtual assistant. Be My AI is accessible through the Be My Eyes app, and is based on OpenAI's GPT-4 large language model. Through the interface, the app allows blind and visually impaired users to send images from a variety of devices to be described. The app allows users to then follow up with questions to further tailor the image description. Blind users report using Be My AI for a variety of tasks, including reading menus, identifying clothing, and describing people. The Be My AI interface is available on Android, iOS, and Windows. Within a few weeks of the interface's roll out, the company reported that it had been used one million times, and it was named among Time's best inventions of 2023. Be My AI is part of a growing number of AI-based apps and devices designed to help blind and visually impaired individuals. == Partnerships == === Microsoft === In November 2023, Be My Eyes entered a partnership with Microsoft to share data to help improve accessibility-focused AI models. === Meta === In 2024, Be My Eyes integrated with Ray-Ban Meta smart glasses, a wearable product developed by Meta and EssilorLuxottica. The partnership enabled users to receive hands-free, real-time visual descriptions and volunteer assistance by using voice commands through the smart glasses. === Hilton === In October 2024, Hilton partnered with Be My Eyes to provide live video assistance for blind and low-vision guests. The free service connects travelers to a Hilton team member that can guide them through tasks like adjusting thermostats, opening window shades, or navigating hotel amenities. This collaboration progressed from a prior arrangement where Hilton helped train Be My Eyes' GPT-4 powered AI model to better recognize objects and layouts in hotel rooms. === Tesco === In October 2025, retailer Tesco announced its partnership with Be My Eyes to launch a six-month pilot aimed at improving in-store accessibility in the UK. The initiative was launched on World Sight Day, 9 October, enabling Be My Eyes users to connect directly with Tesco staff via the app for personalised visual assistance while shopping, Euronewsweek reported. == Awards == Nordic Startup Awards for "Best Social Entrepreneurial Tech Startup" in Denmark 2021 Apple Design Award for best social impact