Connect with us

Artificial Intelligence

Memory.ai, the startup behind time-tracking app Timely, raises $14M to build more AI-based productivity apps

Published

on

Time is your most valuable asset — as the saying goes — and today a startup called Memory.ai, which is building AI-based productivity tools to help you with your own time management, is announcing some funding to double down on its ambitions: it wants not only to help manage your time, but to, essentially, provide ways to use it better in the future.

The startup, based out of Oslo, Norway, initially made its name with an app called Timely, a tool for people to track time spent doing different tasks. aimed not just at people who are quantified self geeks, but those who need to track time for practical reasons, such as consultants or others who work on the concept of billable hours. Timely has racked up 500,000 users since 2014, including more than 5,000 paying businesses in 160 countries.

Now, Memory.ai has raised $14 million as it gears up to launch its next apps, Dewo (pronounced “De-Voh”), an app that is meant to help people do more “deep work” by learning about what they are working on and filtering out distractions to focus better; and Glue, described as a knowledge hub to help in the creative process. Both are due to be released later in the year.

The funding is being led by local investors Melesio and Sanden, with participation from Investinor, Concentric and SNÖ Ventures, who backed Memory.ai previously.

“Productivity apps” has always been something of a nebulous category in the world of connected work. They can variously cover any kind of collaboration management software ranging from Asana and Jira through to Slack and Notion; or software that makes doing an existing work task more efficiently than you did it before (eg Microsoft has described all of what goes into Microsoft 365 — Excel, Word, Powerpoint, etc. — as “productivity apps”); or, yes, apps like those from Memory.ai that aim to improve your concentration or time management.

These days, however, it feels like the worlds of AI and advances in mobile computing are increasingly coming together to evolve that concept once again.

If the first wave of smartphone communications and the apps that are run on smartphone devices — social, gaming, productivity, media, information, etc. — have led to us getting pinged by a huge amount of data from lots of different places, all of the time, then could it be that the second wave is quite possibly going to usher in a newer wave of tools to handle all that better, built on the premise that not everything is of equal importance? No-mo FOMO? We’ll see.

In any case, some bigger platform players also helping to push the agenda of what productivity means in this day and age.

For example, in Apple’s recent preview of iOS 15 (due to come out later this year) the company gave a supercharge to its existing “do not disturb” feature on its phones, where it showed off a new Focus mode, letting users customize how and when they want to receive notifications from which apps, and even which apps they want to have displayed, all organized by different times of day (eg work time), place, calendar items, and so on.

Today, iPhone plays so many roles in our lives. It’s where we get information, how people reach us, and where we get things done. This is great, but it means our attention is being pulled in so many different directions and finding that balance between work and life can be tricky,” said Apple’s Craig Federighi in the WWDC keynote earlier this month. “We want to free up space to focus and help you be in the moment.” How well that gets used, and how much other platforms like Google follow suit, will be interesting to see play out. It feels, in any case, like it could be the start of something.

And, serendipitously — or maybe because this is some kind of zeitgeist — this is also playing into what Memory.ai has built and is building. 

Mathias Mikkelsen, the Oslo-based founder of Memory.ai, first came up for his idea for Timely (which had also been the original name of the whole startup) when he was working as a designer in the ad industry, one of those jobs that needed to track what he was working on, and for how long, in order to get paid.

He said he knew the whole system as it existed was inefficient: “I just thought it was insane how cumbersome and old it was. But at the same time how important it was for the task,” he said.

The guy had an entrepreneurial itch that he was keen to scratch, and this idea would become the salve to help him. Mikkelsen was so taken with building a startup around time management, that he sold his apartment in Oslo and moved himself to San Francisco to be where he believed was the epicenter of startup innovation. He tells me he lived off the proceeds of his flat for two years “in a closet” in a hacker house, bootstrapping Timely, until eventually getting into an accelerator (500 Startups) and subsequently starting to raise money. He eventually moved back to Oslo after two years to continue growing the business, as well as to live somewhere a little more spacious.

The startup’s big technical breakthrough with Timely was to figure out an efficient way of tracking time for different tasks, not just time worked on anything, without people having to go through a lot of data entry.

The solution: to integrate with a person’s computer, plus a basic to-do schedule for a day or week, and then match up which files are open when to determine how long one works for one client or another. Phone or messaging conversations, for the moment, are not included, and neither are the contents of documents — just the titles of them. Nor is data coming from wearable devices, although you could see how that, too, might prove useful.

The basic premise is to be personalised, so managers and others cannot use Timely to track exactly what people are doing, although they can track and bill for those billable hours. All this is important, as it also will feed into how DeWo and Glue will work.

The startup’s big conceptual breakthrough came around the same time: Getting time tracking or any productivity right “has never been a UI problem,” Mikkelsen said. “It’s a human nature problem.” This is where the AI comes in, to nudge people towards something they identify as important, and nudge them away from work that might not contribute to that. Tackling bigger issues beyond time are essential to improving productivity overall, which is why Memory.ai now wants to extend to apps for carving out time for deep thinking and creative thinking.

While it might seem to be a threat that a company like Apple has identified the same time management predicament that Memory.ai has, and is looking to solve that itself, Mikkelsen is not fazed. He said he thinks of Focus as not unlike Apple’s work on Health: there will be ways of feeding information into Apple’s tool to make it work better for the user, and so that will be Memory.ai’s opportunity to hopefully grow, not cannibalize, its own audience with Timely and its two new apps. It is, in a sense, a timely disruption.

“Memory’s proven software is already redefining how businesses around the world track, plan and manage their time. We look forward to working with the team to help new markets profit from the efficiencies, insights and transparency of a Memory-enabled workforce,” said Arild Engh, a partner at Melesio, in a statement.

Kjartan Rist,  a partner at Concentric, added: “We continue to be impressed with Memory’s vision to build and launch best-in-class products for the global marketplace. The company is well on its way to becoming a world leader in workplace productivity and collaboration, particularly in light of the remote and hybrid working revolution of the last 12 months. We look forward to supporting Mathias and the team in this exciting new chapter.”

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://techcrunch.com/2021/06/22/memory-ai-the-startup-behind-time-tracking-app-timely-raises-14m-to-build-more-ai-based-productivity-apps/

AI

Why Machine Vision Matters to Your Business

Published

on

One of the most important kinds of artificial intelligence may be machine vision, also known as computer vision — image processing technology that allows machines to “see” the world like people can.

This tech is already having a major impact on the industry — especially the retail, warehousing, and manufacturing sectors. Any business owner should know about how machine vision may help reshape the economy over the next few years.

What Is Machine Vision and How Does it Work?

At its simplest, machine vision is the use of visual information and artificial intelligence to create algorithms that can process images — breaking them down into identifiable objects, scanning for patterns and looking for important information.

Machine vision technology has existed for decades, but it was rarely used due to the limitations of image processing technology and the high cost of sensors.

Recently, artificial intelligence has made machine vision much more practical.

With an AI-based approach like machine learning, if you have enough visual information — like photographs and recorded video — you can train an algorithm that’s capable of breaking down what a camera sees and picking out distinct, identifiable objects that a machine or robot can use.

For example, a machine vision algorithm trained on information from grocery stores may be able to identify the different products visible in a picture or video feed, as well as objects like shelves, barcodes, displays, customers and floorspace.

One of the better-known applications of machine vision is in self-driving cars. These cars are outfitted with a number of sensors that scan the environment around them — including cameras. Footage from these cameras are processed by a machine-learning algorithm.

This algorithm breaks down the visual data from the cameras into information that the self-driving system can use — like where the road is, the location of other drivers and obstacles the car will have to navigate around.

Fully self-driving cars haven’t hit the market yet — but smart driver assistance systems that use similar tech are starting to become common offerings in high-end vehicles.

The biggest beneficiaries of machine vision, however, are probably companies that can use the tech to streamline business processes.

How AI-Powered Image Processing Is Transforming Business

Across the economy, machine vision is being used in a few different ways.

In retail, machine vision often helps support “smart stores” that use networked sensors and AI to streamline customers’ shopping experience.

These smart stores include the cashierless stores being pioneered by Amazon right now. In these stores, cameras, combined with other sensors like shelf weight sensors and motion detectors, track customers as they move around the store and fill their cart.

Similar tech could also be used to make existing, non-smart stores more intelligent. For example, several companies are experimenting with the use of machine vision to create smart cashierless checkouts in stores that don’t adopt the grab-and-go model.

These could provide a more streamlined alternative to existing self-checkout systems without requiring the same investment that smart stores require.

In manufacturing, machine vision is often used for quality assurance purposes.

For example, you may see a manufacturer use machine vision on a conveyor belt robot that sorts out ideal products from those with obvious defects.

Another algorithm may be used just for color inspection of finished products. Manufacturers sometimes use color inspection for quality assurance processes, using color as a guide to look for chips in paints, defects or errors in components like color-coded wires.

With machine vision, the use of specific lights can help make this process even more effective. By using colored light, rather than pure white light, you can highlight certain colors and help the algorithm to track them.

Manufacturers also use machine vision to support new, self-piloting robots. In factories with warehouses, for example, some manufacturers are using autonomous mobile robots (AMRs) to partially automate picking and packing.

These robots use machine vision like self-driving cars to navigate the factory floor with little or no supervision. They can also use machine vision to read barcodes and identify individual objects, like pallets, allowing them to pick out items to transport around the factory.

How Your Business Can Benefit From Machine Vision

As machine vision becomes more popular, businesses across the economy will be able to benefit from new devices and platforms that use the tech.

A few cutting-edge applications of the tech are already widely available. These may help a number of businesses to automate processes that they couldn’t automate before, or to speed up tedious and difficult labor.

For example, there is a growing number of handwriting analysis and digitization tools on the market that use AI-powered optical character recognition (or OCR). These tools convert scans or photos of handwriting into digital text — reducing the need for transcription and making notes more accessible.

Retailers can benefit from machine vision-powered robots like those used by Walmart for inventory management. These robots move up and down aisles, using cameras to scan for products that need restocking.

Small businesses could also benefit from working with large manufacturers that have adopted the technology. Machine vision can help to reduce costs and improve product quality — for SMBs, this partnership could lower manufacturing expenses and the risk of defective products.

In some cases, it may also be possible to bring this technology in-house to improve quality assurance processes.

The Growing Importance of Machine Vision

AI is likely to become even more important to the business world over the next few years. Tech powered by artificial intelligence, like machine vision, will probably become more sophisticated at the same time.

Right now, businesses can use machine vision in a few different ways — like improving quality control or automating processes like inventory checks. Small businesses without the resources for complex AI-based solutions can also benefit from machine vision through tools like handwriting OCR apps.

Eleanor Hecks is editor-in-chief at Designerly Magazine. She was the creative director at a digital marketing agency before becoming a full-time freelance designer. Eleanor lives in Philly with her husband and pup, Bear.

Continue Reading

Artificial Intelligence

Ashirase, a Honda incubation, reveals advanced walking assistance system for visually impaired

Published

on

Globally, 225 million people are estimated to suffer from moderate or severe visual impairments, and 49.1 million are blind, according to 2020 data from the Investigative Ophthalmology and Visual Science journal. A Japanese startup that was incubated at Honda Motor Company’s business creation program hopes to make navigating the world easier and safer for the visually impaired.

Ashirase, which debuted as the first business venture to come out of Honda’s Ignition program in June, shared details of its in-shoe navigation system for low-vision walkers on Tuesday. The system aims to help users achieve more independence in their daily lives by allowing them to feel which way to walk through in-shoe vibrations connected to a navigation app on a smartphone. Ashirase hopes to begin sales of the system, also named Ashirase, by October 2022.

Honda created Ignition in 2017 to feature original technology, ideas, and designs of Honda associates with the goal of solving social issues and going beyond the existing Honda business. CEO Wataru Chino had previously worked at Honda since 2008 on R&D for EV motor control and automated driving systems. Chino’s background is evident in the navigation system’s technology, which he said is inspired by advanced driver assist and autonomous driving systems.

“The overlap perspective can be, for instance, the way we utilize sensor information,” Chino told TechCrunch. “We use a sensor fusion technology, meaning we can combine information from the different sensors. I have experience in that field myself so that is helpful. Plus there is overlap with automated driving because when we were thinking of safety walking, the automated driving technology had given us an idea for the concept.”

“Ashirase” comes from the Japanese words ashi, meaning “foot,” and shirase, meaning “notification.” As its name suggests, the device, which is attached to the shoe, vibrates to provide navigation based on the route set within an app. Motion sensors, which consist of an accelerometer, gyro sensors and orientation sensors, enable the system to understand how the user is walking.

While en route outside, the system localizes the user based on global navigation satellite positioning information and data based on the user’s foot movement. Ashirase’s app is connected to a range of different map vendors like Google Maps, and Chino said the device can switch to adapt to different information available on different maps. This capability might be helpful if, say, one map had updated information about a road blockage and could send over-the-air updates.

“Going forward, we want to develop the function to generate a map itself using sensors from the outdoor environment, but that’s maybe five years down the line,” Chino said.

The vibrators are aligned with the foot’s nerve layer, so it’s easy to feel the pulse. To indicate the user should walk straight ahead, the vibrator positioned at the front of the shoe vibrates. Vibrators on the left and the right side of the shoe also indicate turning signals for the walker.

Ashirase says this form of intuitive navigation helps the walker attain a more relaxed state of mind rather than one that is constantly alert, leading to a safer walk and less stress for the user.

This also allows the user to have more attention to spare for audible warnings in their environment, like, for example, if they were at a crosswalk, because the device cannot warn the user of obstacles ahead.

“Going forward, we’re thinking about technical updates for users who are totally blind because they don’t have such information like obstacle awareness like low-vision people,” Chino said. “So at this moment, the device is designed for low-vision walkers.”

While indoors, like in a shopping mall, the GPS won’t reach the user, and there isn’t a map for them to localize to. To solve for this, the company says its plan is to use WiFi or Bluetooth-based positioning, connecting to other devices and cell phones within the store, to localize the visually impaired person.

Ashirase is also considering ways to integrate with public transit systems so that the device can alert a user if they have arrived or are near their next stop, according to Chino.

It’s a lot of tech to pack into one little device that attaches to a shoe — any shoe. Chino said the device, which only needs to be charged once a week based on three hours of use per day, is made to be flexible and fit onto different types, shapes and sizes of shoes.

Ashirase intends to release its beta version for testing and data collection in October or November this year and hopes to achieve mass production by October 2022. It’ll have a direct-to-consumer model, the price of which the company is not yet ready to disclose, and a subscription model, which should cost about 2,000 to 3,000 Japanese Yen ($18 to $27) per month.

Chino estimates it’ll take the company 200 million Yen ($1.8 million), including the funds the company has already raised, to make it to market. So far, the company has raised 70 million Yen ($638,000), which came in the form of an equity investor round and some non-equity rounds, according to Chino.

Honda maintains an investor role in the company, supporting and following the business along the way, but Ashirase’s aim is to go public as a standalone company.

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://techcrunch.com/2021/07/28/ashirase-a-honda-incubation-reveals-advanced-walking-assistance-system-for-visually-impaired/

Continue Reading

AI

iPhone 13 To Introduce a New Feature From Apple Watch

Published

on

In his weekly newsletter, Bloomberg journalist Mark Gurman, who often conveys an accurate understanding of Apple’s plans, said the iPhone 13 may have an Apple Watch-inspired always-on mode.

Always-On Mode Feature

The Apple Watch Series 5 and Apple Watch Series 6 have displays that can stay on with low refresh rates and brightness, allowing the user to see their watch even in low light. The same functionality on the iPhone 13 can allow users to see details such as time, date, and notifications at all times.

The always-on iPhone display will be simplified with a larger iPhone 13 battery and an improved display. Previous rumors have suggested that the iPhone 13 will be getting bigger batteries, which could eliminate some of the extra power consumption of the always-on display.

What’s in It for Gamers?

Some iPhone 13 models are also widely expected to incorporate “ProMotion” power updates up to 120Hz, making movements in games appear smooth. This is believed to be facilitated by the use of the OLED LTPO display panel, which can vary in degree of refreshment while using a limited amount of power in order to save battery life.

Pros & Cons of iPhone 13

The device is expected to get heavier and thicker to support advanced displays and larger batteries. But since they will have the always-on feature, users might feel that it can be justified. The always-on display feature could be limited to advanced Pro models that are expected to get the LTPO display technology with ProMotion performance.

Earlier this year, leaker Mark Weinbach said the iPhone 13 will feature an always-on display, although it is important to note that Weinbach does not have a certified record. He said the always-on mode will look like a “toned down lock screen,” where the clock and battery are always visible, and notifications are displayed “with bars and symbols.”

The “Look” Factor

The iPhone 13 is also expected to offer several other enhancements, including an improved performance with the “A15” chip and enhanced camera capabilities, but the design of the iPhone 13 models is expected to be quite similar to the iPhone 12 models.

Recommended Products

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://1reddrop.com/2021/07/28/iphone-13-to-introduce-a-new-feature-from-apple-watch/?utm_source=rss&utm_medium=rss&utm_campaign=iphone-13-to-introduce-a-new-feature-from-apple-watch

Continue Reading

Artificial Intelligence

Financial firms should leverage machine learning to make anomaly detection easier

Published

on

Anomaly detection is one of the more difficult and underserved operational areas in the asset-servicing sector of financial institutions. Broadly speaking, a true anomaly is one that deviates from the norm of the expected or the familiar. Anomalies can be the result of incompetence, maliciousness, system errors, accidents or the product of shifts in the underlying structure of day-to-day processes.

For the financial services industry, detecting anomalies is critical, as they may be indicative of illegal activities such as fraud, identity theft, network intrusion, account takeover or money laundering, which may result in undesired outcomes for both the institution and the individual.

There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.

Detecting outlier data, or anomalies according to historic data patterns and trends can enrich a financial institution’s operational team by increasing their understanding and preparedness.

The challenge of detecting anomalies

Anomaly detection presents a unique challenge for a variety of reasons. First and foremost, the financial services industry has seen an increase in the volume and complexity of data in recent years. In addition, a large emphasis has been placed on the quality of data, turning it into a way to measure the health of an institution.

To make matters more complicated, anomaly detection requires the prediction of something that has not been seen before or prepared for. The increase in data and the fact that it is constantly changing exacerbates the challenge further.

Leveraging machine learning

There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://techcrunch.com/2021/07/28/financial-firms-should-leverage-machine-learning-to-make-anomaly-detection-easier/

Continue Reading
AR/VR3 days ago

Review: Winds & Leaves

Esports4 days ago

Legends of Runeterra adding new Lab of Legends mode: The Saltwater Scourge

Esports5 days ago

TFT Set 5.5 11.15 B-patch nerfs Hecarim, Lucian, and Irelia

Fintech5 days ago

Finding the right balance with hybrid client experiences

IOT4 days ago

The Current State of Indoor Positioning with IoT | Navigine’s Alexey Panyov and Elvina Sharafutdinova

Energy5 days ago

SOL: Sasol Limited – Production And Sales Metrics And Financial Results For The Year Ended 30 June 2021

Esports5 days ago

Former LPL pro Uzi is launching an esports team and recruiting players in Wild Rift

Blockchain5 days ago

The RSI for Bitcoin Breaks Out Of Six-Month Downward Trend

Cleantech5 days ago

1,000,000 Tesla Powerwalls Per Year, Thinks Elon — He Could Be Right.

Cleantech4 days ago

The Grim Reaper & The Republican Party Embracing Climate Action Are The Only Things That Will Eliminate US Climate Change Deniers

Start Ups5 days ago

B2B Payment Platform Nium Valued Above $1B In $200M Funding 

Cyber Security5 days ago

Apple Released a Major Security Update with Fixes for a Security Defect

Blockchain5 days ago

Goldman Sachs Files for “DeFi” ETF to Track Tech Giants

Energy5 days ago

Global Silicon Wafer Sourcing and Procurement Report with COVID-19 Impact Analysis, Supplier Evaluation and Price Trends | SpendEdge

Esports4 days ago

How to level up every trade skill in New World

Aviation5 days ago

YVR no longer splitting passengers at arrivals terminal by COVID vaccination status

Esports3 days ago

New World Faction Armor Sets

Blockchain5 days ago

Kaseya recovers data stolen in ransomware attack with mysterious decryption tool

Esports3 days ago

Are Splitgate’s servers down? Here’s how to check server status

Esports5 days ago

Invictus Gaming sweep Top Esports, keep playoff hopes alive in 2021 LPL Summer Split

Trending