Previously, I wrote about my experience with Len, a bot created to deliver daily encouragement and reflection during the season of Lent. Rarely has a simple product caused so much reflection, excitement, and forward-thinking. Shortly after using Len I started researching, and eventually experimenting with bots -- nothing serious, just dabbling here and there -- and was quickly overwhelmed with its complexity and constant evolution. Every week, new articles are released with the latest tips for bot creation, case studies, statistics, recommendations on platforms, and features. To make sense of it all, I turned to Leighton Cusack, a good friend and creator of Len, the first bot I used. I've synthesized a few points from our conversation to capture the essence of his experience with chatbot creation and recommendations for others entering this space.

Leibniz's (1984) An Introduction to a Secret Encyclopedia includes the following marginal note: Principle of Physical Certainty: Everything which men have experienced always and in many ways will still happen: for example that iron sinks in water (Leibniz 1984). In our daily lives, we routinely use this principle. Thus, we know that we can pull with a string but not push with it; that a flower pot dropped from our balcony falls to the ground and breaks; that when we place a container of water on fire, water might boil after a while and overflow the container. The origin of such knowledge is a matter of constant debate. It is clear that we learn a great deal about the physical world as we grow up.

Free catered lunch and a dog-friendly office are two of the perks offered by an educational technology company in Palo Alto, Calif., that's looking to hire a machine learning engineer. The position, posted on Dice, will pay between 140,000 and 160,000 to the right candidate who's skilled in machine learning platforms as well as data mining, statistical modeling, and natural language processing. Job-seekers who possess those skills typically could expect multiple job offers, says Matt Leighton, director of recruitment at Mondo, which specializes in digital marketing and technology staffing. The job titles vary from company to company; some might post positions in search of a data scientist or machine learning engineer, others might be after a natural language processing (NLP) programmer or cognitive computing engineer. But hiring companies are seeking the same talent: "They're people who create algorithms through code that allow computers to self-learn," Leighton says.

Free catered lunch and a dog-friendly office are two of the perks offered by an educational technology company in Palo Alto, Calif., that's looking to hire a machine learning engineer. The position, posted on Dice, will pay between 140,000 and 160,000 to the right candidate who's skilled in machine learning platforms as well as data mining, statistical modeling, and natural language processing. Job-seekers who possess those skills typically could expect multiple job offers, says Matt Leighton, director of recruitment at Mondo, which specializes in digital marketing and technology staffing. The job titles vary from company to company; some might post positions in search of a data scientist or machine learning engineer, others might be after a natural language processing (NLP) programmer or cognitive computing engineer. But hiring companies are seeking the same talent: "They're people who create algorithms through code that allow computers to self-learn," Leighton says.

Commonsense reasoning about the physical world, as exemplified by "Iron sinks in water" or "If a ball is dropped it gains speed," will be indispensable in future programs. We argue that to make such predictions (namely, envisioning), programs should use abstract entities (such as the gravitational field), principles (such as the principle of superposition), and laws (such as the conservation of energy) of physics for representation and reasoning. These arguments are in accord with a recent study in physics instruction where expert problem solving is related to the construction of physical representations that contain fictitious, imagined entities such as forces and momenta (Larkin 1983). We give several examples showing the power of physical representations.