Google’s smartest, Alexa is most impactful, and Siri is a bit of an ugly stepchild. At least, according to one recent study on the top AI-driven voice assistants by VoiceBot, which suggests that Amazon is having the biggest impact on the voice industry. But what are the key differences driving the Google, Amazon, and Apple in developing smart digital assistants?
The venture capital (VC) world often follows the general trends of the markets. When social media is the in-thing, investors will flock to all manner of social media startups. The same goes for any area of investing from mobile apps to live-work-play co-working places and everything in between.
As a species, humanity has witnessed three previous industrial revolutions: first came steam/water power, followed by electricity, then computing. Now, we’re in the midst of a fourth industrial revolution, one driven by artificial intelligence and big data.
Over the last year, I have been immersing myself in a lot of Artificial Intelligence research, including reading multiple books on AI and taking an online class from Stanford on the fundamentals of Artificial Intelligence.
As offices worldwide shift to remote work, our interactions with customers and colleagues have evolved in tandem. Professionals who once relied on face-to-face communication and firm handshakes must now close deals in a world where both are rare.
Universities and colleges across the nation are in crisis. These institutions face huge funding shortfalls, their students’ total indebtedness has climbed to $1.6 trillion, and decreased enrollment is likely for September and beyond.
Artificial intelligence (AI) contributes significantly to good in the world. From reducing pollution to making roads safer with self-driving cars to enabling better healthcare through medical big-data analysis, AI still has plenty of untapped potential.
Canadian company BlueDot used AI technology to detect the novel coronavirus outbreak in Wuhan, China, just hours after the first cases were diagnosed.
It is the year 2050. The world population is roughly two billion people, just 25% of what it was thirty years ago. Back in 2018, the United Nations had predicted that we were approaching the point of no return on climate change.
It should have been artificial intelligence’s moment in the sun. With billions of dollars of investment in recent years, AI has been touted as a solution to every conceivable problem. So when the COVID-19 pandemic arrived, a multitude of AI models were immediately put to work.
Some of the world’s best tech-led colleges and universities offer specialized Master’s Degree courses in these subjects, and often they are also world leaders in research, partnered with Silicon Valley businesses on cutting-edge projects.
Recent surveys, studies, forecasts and other quantitative assessments of AI highlight the current state of adoption of AI by enterprises worldwide, the future of deep learning, and consumers’ attitudes regarding Covid-19 contact tracing.
To clarify, there are lots of claims these days about computers that embody AI, implying that the machine is the equivalent of human intelligence, but you need to be wary of those rather brash and outright disingenuous assertions.
The high energy consumption of artificial neural networks’ learning activities is one of the biggest hurdles for the broad use of Artificial Intelligence (AI), especially in mobile applications. One approach to solving this problem can be gleaned from knowledge about the human brain.
In recent years, a growing number of researchers have been developing artificial neural network (ANN)- based models that can be trained using a technique known as reinforcement learning (RL). RL entails training artificial agents to solve a variety of tasks by giving them “rewards” when they perform well, for instance, when they classify an image correctly.
In the continuing theme of higher level tools to improve developing useful applications, today we’ll visit feature engineering in a changing environment.