First, I want to apologize. My Kermit the Frog post was not entirely sincere. This particular post of mine has been viewed more than 10 million times, which is far more than I expected. But I did expect something. Social networks have never been the realm of good faith or authenticity; trolls and other engagement baiters have been able to engineer their own virality for years and years, simply by correctly predicting what large numbers of people will respond to.

In the fall of 2016--the year in which the proportion of online adults using social media reached eighty per cent--I published an Op-Ed in the Times that questioned the popular conception that you need to cultivate a strong social-media brand to succeed in the job market. "I think this behavior is misguided," I wrote. "In a capitalist economy, the market rewards things that are rare and valuable. Social media use is decidedly not rare or valuable." I suggested that knowledge workers instead spend time developing useful skills, with the goal of distinguishing themselves in their chosen fields.

Automated driving systems can be helpful in a wide range of societal challenges, e.g., mobility-on-demand and transportation logistics for last-mile delivery, by aiding the vehicle driver or taking over the responsibility for the dynamic driving task partially or completely. Ensuring the safety of automated driving systems is no trivial task, even more so for those systems of SAE Level 3 or above. To achieve this, mechanisms are needed that can continuously monitor the system's operating conditions, also denoted as the system's operational design domain. This paper presents a safety concept for automated driving systems which uses a combination of onboard runtime monitoring via connected dependability cage and off-board runtime monitoring via a remote command control center, to continuously monitor the system's ODD. On one side, the connected dependability cage fulfills a double functionality: (1) to monitor continuously the operational design domain of the automated driving system, and (2) to transfer the responsibility in a smooth and safe manner between the automated driving system and the off-board remote safety driver, who is present in the remote command control center. On the other side, the remote command control center enables the remote safety driver the monitoring and takeover of the vehicle's control. We evaluate our safety concept for automated driving systems in a lab environment and on a test field track and report on results and lessons learned.

A couple of weeks ago I wrote that fears that "AI will kill us" are exagerated, being fears about something that "literally ceases to exist the moment anyone pulls the plug off the computers it runs on. AI itself cannot "kill" anything more real than avatars in some simulation. AI is the most vulnerable, helpless thing ever." By pure chance, yesterday I found a post discussing exactly "why we can't just unplug it: by the time we discover it is a superintelligence it will have spread itself across many computers and built deep and hard defenses for these. That could happen for example by manipulating humans into thinking they are building defenses for a completely different reason."

FastText is incredibly rapid at word vector model training. You can train 100 million words in under a minute. The training and testing of deep neural network models can be time-consuming. Both of these methods use a linear classifier to train the model. FastText trains the model using a hierarchical classifier.

In order to build a working full-stack application, there are so many moving parts to think about. And you'll need to make many decisions that will be critical to the success of your app. For example, what language will you use and what platform will you deploy on? Are you going to deploy a containerised software on a server, or make use of serverless functions to handle the backend? Do you plan to use third-party APIs to handle complex parts of your application, like authentication or payments? Where do you store the data? In addition to all this, you'll also need to think about the user interface, design and usability of your application, and much more. This is why complex large applications require a multifunctional development team collaborating to build the app. One of the best ways to learn how to develop full stack applications is to build projects that cover the end-to-end development process. You'll go through designing the architecture, developing the API services, developing the user interface, and finally deploying your application. So this tutorial will take you through the process of building an AI chatbot to help you learn these concepts in depth. Important Note: This is an intermediate full stack software development project that requires some basic Python and JavaScript knowledge. I've carefully divided the project into sections to ensure that you can easily select the phase that is important to you in case you do not wish to code the full application.

"Just use AI!" While you may have heard this before, using artificial intelligence can seem like a lot of work. But it doesn't have to be, and there are many AI APIs out there ready for you to leverage. Check out some of them in Postman's latest featured list, Artificial Intelligence APIs, then get started straight away by forking any or all of these popular APIs to your own workspace. OpenAI is a non-profit AI research company whose goal is to advance digital intelligence. They've been widely talked about recently when they announced Codex, an AI that translates natural language to code.

But before delving into'behind-the-scenes' of US banking industry meeting ATM, let's turn back time for a second -- on March 27th, 1998, in the New Tech 1998 conference in Denver, Colorado. Here, Neil Postman, a prominent American cultural critic and professor at New York University, gave a keynote lecture. Professor Postman has been a long-time scholar of how new technologies relate to human society, and the book'Amusing Ourselves to Death', a 1985 book that rose to stardom, shows how television technology is destroying public discourse and turning everything into entertainment. I think it has something to do with how we feel about the impact of today's media and how our lives exposed to it are deteriorating. Since this book, Professor Postman has strongly criticized the tendency to respond to all social problems through technical solutions.

"Just as athletes can't win without a sophisticated mixture of strategy, form, attitude, tactics, and speed, performance engineering requires a good collection of metrics and tools to deliver the desired business results."-- The current trend of leveraging the powers of ML in business has made data scientists and engineers design innovative solutions/services and one such service have been Model As A Service (MaaS). We have used many of these services without the knowledge of how it was built or served on web, some examples include data visualization, facial recognition, natural language processing, predictive analytics and more. In short, MaaS encapsulates all the complex data, model training & evaluation, deployment, etc, and lets customers consume it for their purpose. As simple as it feels to use these services, there are many challenges in building such a service e.g.: how do we maintain the service?

Rasa is an open source machine learning framework for automated text and voice-based conversations. Understand messages, hold conversations, and connect to messaging channels and APIs. The basic pre-requisite here is Anaconda. It helps to handle packages and keep different code dependencies from meddling with each other. If you use pip install rasa[full] it will install all dependencies (spaCy and MITIE) of Rasa for every configuration making your life easier.