Steven Johnson has a reputation as a research software nerd. The author of 13 nonfiction books, he's constantly looking for digital tools to streamline his creative process. So when large language models – which power text-generating AI tools such as ChatGPT – started getting attention, he was most interested in what they could mean for organising information. In 2022, an article Johnson wrote about LLMs for the New York Times caught the eye of researchers at Google Labs, the tech company's experimental AI arm, who came to him with a proposition: would he help them develop the kind of digital research assistant he'd been dreaming of? The result is NotebookLM, a note-taking tool that uses AI to help organise, summarise and answer questions about any information you give it.

Allow me to explain my toilet theory of the internet. The premise, while unprovable, is quite simple: At any given moment, a great deal of the teeming, frenetic activity we experience online--clicks, views, posts, comments, likes, and shares--is coming from people who are scrolling on their phones in the bathroom. Mindless scrolling isn't limited to the bathroom, and plenty of idle or bored swiping happens during other down moments--while waiting in line, or sitting in gridlocked traffic. Right now, somebody somewhere is probably reading an article or liking an Instagram post with a phone in one hand and an irritable infant in the other. The toilet theory is mostly a reminder to myself that the internet is a huge place that is visited countless times each day by billions of people in between and during all the mundane things they have to do.

Abandon all hope, ye who merge spreadsheet cells! Last week, at its annual I/O conference, Google spent hours detailing how large language models would help the knowledge workers of the world unload their busywork onto a legion of eager, capable neural networks. The company will soon introduce AI functions into programs such as Gmail, Google Sheets, and Google Slides that will allow users to type simple commands and receive complex outputs: entire email compositions, for example, or auto-generated tables. The future that Google is promising feels familiar--it's all about heightened convenience and one-click efficiency--and I hate it. Workplace AI feels like the purest distillation of a corrosive ideology that demands frictionless productivity from workers: The easier our labor becomes, the more of it we can do, and the more of it we'll be expected to do.

The upstart ChatGPT heralded an advent of conversational-AI platforms that can passably converse with humans based on a wide range of inputs. In addition to ChatGPT, which is made by the Microsoft-backed nonprofit OpenAI, other big tech companies are getting into the game with competing projects from Google (Bard) and Facebook (LLaMA). The rise of AI to kitchen-table prominence raises a question: Are insurance companies, which have been transforming digitally for years, ready to invest further in large language models and turn their precious customer relationships over to a chatbot? Forrester Principal Analyst Indranil Bandyopadhyay says that a big AI revolution in insurance isn't going to happen overnight. "I don't see the majority of the insurance industry going and jumping into these emerging technologies. It will take some time," Bandyopadhyay says.

Federal agencies are the latest to alert companies to potential bias in AI recruiting tools. As the AP notes, the Justice Department and Equal Employment Opportunity Commission (EEOC) have warned employers that AI hiring and productivity systems can violate the Americans with Disabilities Act. These technologies might discriminate against people with disabilities by unfairly ruling out job candidates, applying incorrect performance monitoring, asking for illegal sensitive info or limiting pay raises and promotions. Accordingly, the government bodies have released documents (DOJ, EEOC) outlining the ADA's requirements and offering help to improve the fairness of workplace AI systems. Businesses should ensure their AI allows for reasonable accommodations.They should also consider how any of their automated tools might affect people with various disabilities.

Kalendar AI, a San Francisco-based startup that's been building on top of GPT-3's language model -- developing a SaaS for automating lead generation and sales outreach to make it easier for companies to get initial meetings with prospective customers -- has raised $3.2 million in pre-seed funding from 500 Startups; The Lean Startup author, Eric Ries; VC firms Village Global and Metaplanet; and 20 angel investors (including CEOs of "popular" but undisclosed companies). "Our AI technology writes personalized invitations to ideal customers with personalized decks -- inviting them to take a meeting," explains founder and CEO Ravi Vadrevu. The SaaS was launched in February this year, although the startup itself -- which is called Kriya Inc -- was founded back in 2017 and had been bootstrapping prior to raising this pre-seed. The idea for the b2b product is to automate the time-consuming and expensive process of sales outreach, including locating and pitching leads, as well as to offer tools to streamline and enhance initial sales meetings. Kalendar AI claims to have amassed a database of 340M "ideal customer profiles" upon which it unleashes its AI sales rep bots to send "personalized" pitches (including "interactive presentations that convert into one-click meetings") to likely looking customers. "Our solution brings down the time to initiate a conversation to book an appointment from 7 days to 30 seconds from a sales perspective," claims Vadrevu, who also argues there are big productivity wins from a marketing perspective vs other channels.

Chinese tech heavyweight Lenovo Group Ltd unveiled its latest commercial tablets to meet growing demand for productivity tools in remote work, digital production, smart retail and other application scenarios in the post-pandemic era. The move came after Lenovo secured the No 1 spot in the commercial tablet market in China for three consecutive quarters. It is also part of Lenovo's broader efforts to pounce at opportunities from the commercial internet of things market. Wang Liping, vice-president of Lenovo Group and general manager of commercial business at Lenovo China, said commercial tablets, featuring large screens, easy portability, suitable computing power, multiple sensors and the integration of software and hardware, will become a new generation of productivity tools for thousands of industries. Lenovo's commercial tablet business has joined with nearly 400 partners to create a stable cooperation ecological alliance.

COVID-19 has changed the way we live and work in the longterm. For some, the long periods of time spent stuck in doors zapped their productivity levels and mental health, leaving them with no energy to work or create. For those with jobs that could work from home, they were left with a host of new challenges to overcome while also trying to do an honest, productive day's work. Time is perhaps the most valuable commodity in business, and, as a society, we're obsessed with doing the most in as little time as possible. We equate being productive at work with being successful.

In this article, I will discuss several resources that can help you master the foundations of data science. In the modern age of information technology, there is an enormous amount of free resources for data science self-study. As a matter of fact, you can design your own data science curriculum from the innumerable amount of available resources. The rising demand for data science practitioners has given rise to a proliferation of massive open online courses (MOOC). If you are going to be taking one of these courses, keep in mind that some MOOCs are 100% free, while some do require you to pay a subscription fee (it could range anywhere from $50 to $200 per course or more, varies from platforms to platforms).

As a data science educator, many people interested in getting into data science have contacted me for guidance on how to get into the field of data science. This article will discuss the recommended topics that one has to study to build essential skills in data science. The topics presented here, if studied thoroughly, will provide the minimum background needed to start doing data science. This curriculum could also be used for designing an introductory college-level course in data science. Keep in mind that knowledge acquired from courses alone will not make you a data scientist.