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) …
What the fuck did you just fucking say about me, you little bitch? I'll have you know I graduated top of my class in the Navy Seals, and I've been involved in numerous secret raids on Al-Quaeda, and I have over 300 confirmed kills. I am trained in gorilla warfare and I'm the top sniper in the entire US armed forces. You are nothing to me but just another target. I will wipe you the fuck out with precision the likes of which has never been seen before on this Earth, mark my fucking words.
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In this post I will describe on an abstract level what "digital leadership" in my view means, about the concrete "how does this work" then later more. On Wikipedia one finds: "Digital leadership is a scientific approach to the definition of the tasks and tools of leadership in times of digitization in general and in phases of transformation into a digital company in particular". On how and what impact digitization has on the topic of leadership, one then reads the approaches that you know from agile leadership: participation, delegation, empowerment, networks, network organizations, experimentation, lifelong learning, team orientation and purpose. I always get suspicious when you get clear answers or trends to complex problems. My view on the subject is a little more nuanced.
Marketing has changed drastically over the last decade, fueled by the evolution of technology and the demands of more discerning, sophisticated and impatient consumers. Yet, despite consumers' evolving preferences for how they access products, services and information, I would argue that most brands lag behind their customers' desires and needs -- they continue to rely on high-frequency, one-way communications while striving to instill urgency to drive conversions. This seems particularly true within the world of direct marketing that is heavily reliant on one-way channels where brands push the latest sales offers in tightly packaged, largely text-based campaigns, hoping to move the needle a few percentage points. As a long-time direct marketer and a humble student of the great David Ogilvy, I have always felt that it's somehow arrogant to believe that we could compel, persuade or influence a consumer by speaking at them -- employing one-way interactions without thinking they might want to say something back. According to one company's research, the SMS marketing conversion rate on Black Friday 2020 was 3.5%, which was awesome and significantly above 2019.
Artificial intelligence depends on machine learning, and machine learning depends on machine learning engineers. What does it take to build a career in machine learning? You need to understand the educational paths and the career opportunities for entry into machine learning. You should also know about the different career paths that machine learning skills can lead to. The live webinar will include a Q&A with Ronald.
In late November the U.S. Federal Drug Administration approved Benevolent AI's recommended arthritis drug Baricitnib as a COVID-19 treatment, just nine-months after the hypothesis was developed. The correlation between the properties of this existing Eli Lilly drug and a potential treatment for seriously ill COVID-19 patients, was made with the help of knowledge graphs, which represent data in context, in a manner that humans and machines can readily understand. Knowledge graphs apply semantics to give context and relationships to data, providing a framework for data integration, unification, analytics and sharing. Think of them as a flexible means of discovering facts and relationships between people, processes, applications and data, in ways that give companies new insights into their businesses, create new services and improve R&D research. Benevolent AI, a six-year-old London-based company which has developed a platform of computational and experimental technologies and processes that can draw on vast quantities of biomedical data to advance drug development, built-in the use of knowledge graphs from day one.
Artificial intelligence has been all over headlines for nearly a decade, as systems have made quick progress in long-standing AI challenges like image recognition, natural language processing, and games. Tech companies have sown machine learning algorithms into search and recommendation engines and facial recognition systems, and OpenAI's GPT-3 and DeepMind's AlphaFold promise even more practical applications, from writing to coding to scientific discoveries. Indeed, we're in the midst of an AI spring, with investment in the technology burgeoning and an overriding sentiment of optimism and possibility towards what it can accomplish and when. This time may feel different than previous AI springs due to the aforementioned practical applications and the proliferation of narrow AI into technologies many of us use every day--like our smartphones, TVs, cars, and vacuum cleaners, to name just a few. But it's also possible that we're riding a wave of short-term progress in AI that will soon become part of the ebb and flow in advancement, funding, and sentiment that has characterized the field since its founding in 1956. AI has fallen short of many predictions made over the last few decades; 2020, for example, was heralded by many as the year self-driving cars would start filling up roads, seamlessly ferrying passengers around as they sat back and enjoyed the ride.
Work as a data scientist follows a cycle: log in, clean data, define features, test and build a model, and make sure the model is running smoothly. Sounds straightforward enough, except not all parts of the cycle are created equal: data preparation takes 80% of any given data scientist's time. No matter what project you're working on, most days you're cleaning data and converting raw data into features that machine learning models can understand. The monotonous hole of data prep blends hours together and makes each day of work feel identical to the one before it. Why can't you do this tedious process more effectively?
Once upon a time in a world where streaming was still a novelty, TV remotes were filled with what seemed like thousands of buttons. They were complicated to navigate and intimidating to use. On top of all that input overload, you also had to memorize an ever-increasing glut of channel numbers and then punch them into a number pad like you were calling someone on the phone. It was cumbersome but we accepted it. Over the past several years, however, streaming's dominance has shifted the remote's form and function from an overwhelmingly long channel changer to a simple and compact menu navigator. Roku, a pioneer which helped popularize dedicated streaming hardware more than a decade ago, is back to further cement this sea change with an upgraded remote that brings even more quality-of-life improvements.