What it makes: “Virtual employee” that schedules meetings over e-mail. Users copy “Clara” on meeting requests. It e-mails the other party, determines the best meeting times based on users’ specified preferences, and sends calendar invites once details are finalized. Cost ranges from $199 to $499 per month per user, with custom pricing available for teams. A more expensive version of Clara can make restaurant reservations for in-person meetings through OpenTable and RSVP to event invites on users’ behalf. In use at hundreds of companies, including Stripe, Houzz, and AngelList.
Target customer: Professionals who participate in meetings.
How it works: Machine learning and natural-language processing. Uses both to understand the intent and context of users’ e-mails, automatically answer if straightforward, and “predict” responses if complicated. Messages are the product of algorithms, but human contractors get involved in complex situations.
What it makes: Customer-service automation platform. Tool can answer basic questions such as “Does this car come in green?” on its own, in a conversational way, using text messaging, social media, e-mail, or live chat. For more complicated queries such as “Is this car the most eco-friendly vehicle you sell?” it assists human agents with intelligent prompts, such as “Yes, this car is electric and manufactured using renewable raw materials.” Corporate customers pay monthly based on degree of automation and usage. Actively deployed with several Fortune 1000 clients, including Unilever.
Target customer: Large company call centers in financial services, airlines, or other industries.
How it works: Deep learning, machine learning, natural-language processing, and neural networks. Asks corporate customers for customer-service-related chat logs, e-mail transcripts, and Facebook and Twitter messages and feeds them into its algorithm. Process creates customer-service software for clients.
What it makes: Conversational software interface, a.k.a. “chatbot,” that automates simple, repetitive tasks inside Slack, the popular corporate messaging app. Most commonly used to collect work status updates and lunch order requests from groups. Bot polls team members via text, aggregates their responses, and distributes written reports to each person. Users can also program Howdy to ask anything they want by typing questions into a written script. When activated, the bot runs the script and poses the questions as chat messages. Free while in beta, but will eventually carry monthly fee.
Target customer: Teams of up to 20 people that need help automating their day-to-day processes and organizing their information.
How it works: Machine learning and natural-language processing. Uses natural-language processing to understand what users are requesting (in their Slack messages) and trigger the correct (prewritten) text. Uses machine learning to train and improve its system.
What it makes: Financial institutions embed this personal assistant in their mobile apps to improve customer experience. Can answer more than 1,000 banking-related questions and understand conversational voice and text commands. Enables consumers to check their account balances, understand spending patterns, search transactions, transfer funds, and locate nearby ATMs. Subscription fee is annual and based on usage. Company is a spinoff of SRI International, the research institute that helped develop the technology behind Apple’s Siri, though the two systems are independent.
Target customer: Retail and commercial banks, wealth management and credit-card providers.
How it works: AI reasoning, machine learning with deep neural networks, natural-language processing, and speech recognition. Uses natural-language processing to rapidly identify users’ intentions and AI reasoning to help users achieve their objectives in the most efficient manner.
What it makes: Scheduling robot lets teams arrange meetings by typing plain English commands within Slack. Understands phrases such as “We want to have a meeting around noon, before July 4” and analyzes employees’ calendars to suggest the most convenient times. Can help book flights through U.K.-based comparison engine Skyscanner. Shows users shortest flight between origin and destination, cheapest flight, cheapest nonstop flight, and earliest arrival. Currently free, but large corporate clients may eventually have to subscribe. Used by 3,100 companies, including AOL and Nike.
Target customer: Any company that uses Slack.
How it works: Uses natural-language processing to infer users’ intentions from their messages and the context and desired format of the meeting in question. Uses machine learning to discern users’ preferences. Uses AI reasoning to identify what meeting times would best accommodate attendees given time zones.
What it makes: Intelligent agent named Amy (or Andrew, if you prefer) that schedules meetings over e-mail. Similar to Clara (see above) in functionality (but with less human intervention), these assistants currently schedule tens of thousands of meetings each month. Users include employees at LinkedIn, Spotify, and Uber. Service still in closed beta, but planned pricing will be lower than Clara’s—around $9 a month for unlimited meeting scheduling and the ability to personalize the assistant’s name and e-mail address, with a free option for people who schedule a limited number of meetings.
Target customer: Professionals who participate in meetings.
How it works: Uses natural-language processing to analyze incoming e-mails for information about people, times, locations, and the sender’s intentions, and format the data in a way the intelligent agent can digest. Uses deep learning to interpret the intentions of incoming e-mails and their relevance to a meeting.
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