If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
With tech giants pouring billions of dollars into artificial intelligence projects, it's hard to see how startups can find their place and create successful business models that leverage AI. However, while fiercely competitive, the AI space is also constantly causing fundamental shifts in many sectors. And this creates the perfect environment for fast-thinking and -moving startups to carve a niche for themselves before the big players move in. Last week, technology analysis firm CB Insights published an update on the status of its list of top 100 AI startups of 2020 (in case you don't know, CB Insight publishes a list of 100 most promising AI startups every year). Out of the hundred startups, four have made exits, with three going public and one being acquired by Facebook.
Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment. Although there are no AIs that can perform the wide variety of tasks an ordinary human can do, some AIs can match humans in specific tasks. Are artificial intelligence and machine learning the same? No, artificial intelligence and machine learning are not the same, but they are closely related. Machine learning is the method to train a computer to learn from its inputs but without explicit programming for every circumstance.
A comprehensive list of top startup companies who are building quite a reputation in the tech domain through voice tech offerings. Voice AI has been around since IBM introduced it in 1961 through IBM Shoebox. It was the first digital speech recognition tool which at its time could recognize 16 words and 9 digits. Today, using voice AI, developers can train neural network models, create human like voices, chatbots and more. The voice AI tech startups space is booming and now encompasses various avenues such as voice analytics, speech recognition, artificial voice synthesis, voice transcription, voice recognition, among others.
I found that many university courses, books or online training either taught me how to build software or how to train machine learning (ML) models, but few blended both worlds. Having worked on various AI projects in the IBM Garage, I want to share how my experience in using test-driven development (TDD) helped me build better AI-powered applications. This blog post uses an AI-powered web app as an example on how to apply TDD in AI projects. In the web app people can upload images taken inside and outside of houses. Then the website will display whether the photo was taken outside or, if taken inside, in which room.
Counselors volunteering at the Trevor Project need to be prepared for their first conversation with an LGBTQ teen who may be thinking about suicide. One of the ways they do it is by talking to fictional personas like "Riley," a 16-year-old from North Carolina who is feeling a bit down and depressed. With a team member playing Riley's part, trainees can drill into what's happening: they can uncover that the teen is anxious about coming out to family, recently told friends and it didn't go well, and has experienced suicidal thoughts before, if not at the moment. Now, though, Riley isn't being played by a Trevor Project employee but is instead being powered by AI. Just like the original persona, this version of Riley--trained on thousands of past transcripts of role-plays between counselors and the organization's staff--still needs to be coaxed a bit to open up, laying out a situation that can test what trainees have learned about the best ways to help LGBTQ teens.
In the ongoing popular (albeit shallow) debate pitting human translators against machine translation (MT), one constant is the question of quality -- how to define it, how to measure it, and how to improve it. Now, a new website, the AI Incident Database (AIID), aims to quantify the risks presented, and actual harm caused, by AI. Sean McGregor, ML architect at Syntiant and developer of the AIID, described the "collective memory of [AI systems'] failings" in a November 2020 paper. As McGregor explained, the AIID is a project of the Partnership on AI (PAI), an organization funded by tech companies and governed by a board comprising corporate partners and non-profits. The AIID is modeled on incident databases in other industries, namely aviation and cybersecurity, which promote transparency.
The Bulgarian government has adopted a "Concept for the Development of Artificial Intelligence", planned until 2030. This strategy is in line with the documents of the European Commission, considering AI as one of the main drivers of digital transformation in Europe and a significant factor in ensuring the competitiveness of the European economy and high quality of life. Specific aspects of the European vision of "reliable AI" are included, namely that technological progress is accompanied by a legal and ethical framework to ensure the security and rights of citizens. The strategy also includes details on collecting accessible high-quality data, disseminating information and equal access to the benefits of AI technologies. In the concept document, an overview is given of the three main sectors involved in AI – sectors developing AI, sectors consuming AI, and sectors enabling the development and implementation of AI.
Disruption is the new normal, so what are your options? You can keep your fingers crossed and hope for the best. You can react to rapid change and hope your competitors are also going to remain reactive instead of becoming proactive. Or, ideally, you leverage analytics, the power of AI and experimentation to drive innovations and develop winning strategies and tactics for your brands and business. These challenges are compounded by the revolutionary change that the COVID-19 pandemic has accelerated for e-commerce and other forms of virtual customer connections/relationships. Amid all this, success will depend on smart marketers and innovators who have the will and capability to adapt to meet the demands of the evolving consumer.
Black history permeates all facets of our lives--and video games are no exception. From the 8-bit days to the 4k Ray Tracing present, Black video game characters have occupied various positions; from the precarious period of early sports games in the '70s, which included titles like Heavyweight Champ and the nameless grayscale sprites, to Spider-Man: Miles Morales as the poster child for a new gaming generation today, Black representation has come a long way. Similar to other mediums, such as film, music, and literature; Black culture has been, and is, integral to grappling with our collective understanding of video game history. People of color have often been portrayed in popular media as stereotypes and tropes that speak to an underlying structure of racism, patriarchy, heteronormativity, and other forms of systemic oppression. As a Black queer gaymer, the only time I ever saw myself on the screen was through character creation, but that's just cheating in the context of this story.