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Fitbit adds sleep tool

Fitbit has released a set of sleep tools to help Fitbit users improve their sleep consistency and overall health.

Sleep plays a critical role in health and wellbeing, from protecting against cardiovascular disease, diabetes and obesity, to boosting neurocognitive functions, mental health and longevity. Available today on the free Fitbit app and compatible with all Fitbit devices that track sleep, Fitbit’s new Sleep Schedule feature helps guide you to get a more consistent pattern of sleep with:

  • Personalised sleep goals based on your sleep data to achieve your optimal amount of sleep each night
  • Customised bedtime and wakeup targets to establish sleep consistency
  • Reminders to stay on schedule, and a sleep schedule history to chart your progress

These tools are the first in a series of new sleep features being developed in collaboration with Fitbit’s new panel of leading sleep experts that includes Drs. Michael Grandner at the University of Arizona, Allison Siebern at Stanford University, and Michael Smith at Johns Hopkins University.

The Fitbit app is a vital part of the Fitbit platform – consisting of devices, apps, social and motivational features, advice and personalised coaching – which is continually getting smarter and easier to use with features like automatic sleep tracking and exercise recognition to make tracking your health and fitness effortless. Working in harmony, the Fitbit platform helps people make behavioural changes to be more active, exercise more, eat smarter, track their sleep and manage their weight. While many people understand the benefits and importance of a good night’s sleep for their health, getting enough sleep (7 to 9 hours) and regularly going to bed around the same time each night can be a challenge.

According to Fitbit’s sleep experts, adhering to a consistent sleep routine is one of the most important things people can do to improve their sleep: “If you’re constantly changing your sleep routine, it can have the same effect as giving yourself jetlag because you are continually changing your circadian rhythm, also known as your internal clock, which can negatively impact your health and wellness,” said Michael Grandner, PhD, MTR, CBSM. “To improve your physical performance, mental health and cognitive functions, you should aim to get a sufficient amount of sleep each night and be consistent with the times you go to sleep and wake up each day. Fitbit’s new Sleep Schedule tool makes it easier for people to see how much sleep they’re actually getting in order to establish a healthy routine – this has the potential to help millions of people around the world improve their sleep and overall wellbeing, which is really exciting.”

Research has shown that getting enough sleep can also positively impact how much you exercise the next day and is vital to post-training recovery, playing an integral role in the body’s ability to repair itself. Additionally, Fitbit data also shows a correlation between consistent bedtimes and daily active minutes, especially for users who go to bed early each night. Users who sleep an average of 7 to 9 hours nightly also have a lower body mass index (BMI) than those who sleep only 3 to 4 hours per night, while those who are overweight or obese (BMI over 25) on average sleep over an hour (70 minutes) less per week than those with a normal BMI (BMI 18.5-25).

“What’s great about the new Fitbit Sleep Schedule feature is that it looks at your sleep data from your Fitbit device you’re wearing day and night, analyses it for patterns and creates a personalised schedule just for you,” said Tim Roberts, Executive Vice President, Interactive at Fitbit. “This is a great example of how we’re providing guidance using Fitbit data to help millions of people develop healthier habits and routines, and is just the first in a series of new sleep features that we’re working on to help our users improve their health through data and coaching.”

Enhanced Sleep Tracking Features 

The new Sleep Schedule features on the Fitbit app will help you meet your sleep goals and maintain a more consistent pattern of sleep through these tools:

  • Sleep Goal: Based on your sleep data from your Fitbit tracker, you can follow the app’s personalised recommendations or set your target number of hours to make sure you’re getting enough sleep each night.
  • Bedtime and Wake Up Targets: Based on your sleep goal and past sleep behaviour from your Fitbit tracker, the app will recommend target bedtime and wake up times. You can customise these based on your personal preferences and schedule.
  • Bedtime and Wake Up Reminders: To help you reach your sleep goal and regularly go to bed and wake up more consistently, you can receive push notification reminders on your smartphone. You can also set a silent wake alarm on your Fitbit tracker based on your wake up target.
  • Sleep Schedule History Chart: Track your sleep consistency over time to determine if you’re meeting your goals or if you need to adjust your sleep schedule.

About Fitbit’s Sleep Experts

Fitbit established a panel of leading sleep experts to provide a wealth of academic expertise as it develops innovative and effective sleep features for its users. Their expertise spans a variety of sleep-related topics including health, chronic diseases and insomnia.

  • Michael Grandner, PhD, MTR, CBSM, the director of the Sleep and Health Research Program at the University of Arizona, is certified in Behavioural Sleep Medicine and focuses his research on how sleep and sleep-related behaviours are related to cardiovascular disease, diabetes, obesity, neurocognitive functioning, mental health and longevity.
  • Allison Siebern, PhD, CBSM, a consulting assistant professor at Stanford University Sleep Medicine Center and director of the Sleep Health Integrative Program at the Fayetteville VA Medical Center in North Carolina, is board certified in behavioural sleep medicine by the American Academy of Sleep Medicine. She has over a decade of clinical and research expertise in the field of sleep, including examining the factors associated with successful treatment outcomes using Cognitive Behaviour Therapy for Insomnia (CBTi).
  • Michael Smith, PhD, CBSM, is a professor of Psychiatry, Neurology, and Nursing at the Johns Hopkins University, School of Medicine. He is also the director of the Center for Behaviour and Health, founder of Johns Hopkins’ Behavioural Sleep Medicine Program and co-directs the NIH-funded Center for Sleep-Related Symptom Science. His research focuses on the neurobehavioural causes, consequences, and treatments of insomnia and sleep loss with an emphasis on the interface between sleep and pain.

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What’s left after the machines take over?

KIERAN FROST, research manager for software in sub-Saharan Africa for International Data Corporation, discusses the AI’s impact on the workforce.

One of the questions that we at the International Data Corporation are asked is what impact technologies like Artificial Intelligence (AI) will have on jobs. Where are there likely to be job opportunities in the future? Which jobs (or job functions) are most ripe for automation? What sectors are likely to be impacted first? The problem with these questions is that they misunderstand the size of the barriers in the way of system-wide automation: the question isn’t only about what’s technically feasible. It’s just as much a question of what’s legally, ethically, financially and politically possible.

That said, there are some guidelines that can be put in place. An obvious career path exists in being on the ‘other side of the code’, as it were – being the one who writes the code, who trains the machine, who cleans the data. But no serious commentator can leave the discussion there – too many people are simply not able to or have the desire to code. Put another way: where do the legal, financial, ethical, political and technical constraints on AI leave the most opportunity?

Firstly, AI (driven by machine learning techniques) is getting better at accomplishing a whole range of things – from recognising (and even creating) images, to processing and communicating natural language, completing forms and automating processes, fighting parking tickets, being better than the best Dota 2 players in the world and aiding in diagnosing diseases. Machines are exceptionally good at completing tasks in a repeatable manner, given enough data and/or enough training. Adding more tasks to the process, or attempting system-wide automation, requires more data and more training. This creates two constraints on the ability of machines to perform work:

  1. machine learning requires large amounts of (quality) data and;
  2. training machines requires a lot of time and effort (and therefore cost).

Let’s look at each of these in turn – and we’ll discuss how other considerations come into play along the way.

Speaking in the broadest possible terms, machines require large amounts of data to be trained to a level to meet or exceed human performance in a given task. This data enables the bot to learn how best to perform that task. Essentially, the data pool determines the output.

However, there are certain job categories which require knowledge of, and then subversion of, the data set – jobs where producing the same ‘best’ outcome would not be optimal. Particularly, these are jobs that are typically referred to as creative pursuits – design, brand, look and feel. To use a simple example: if pre-Apple, we trained a machine to design a computer, we would not have arrived at the iMac, and the look and feel of iOS would not become the predominant mobile interface. 

This is not to say that machines cannot create things. We’ve recently seen several ML-trained machines on the internet that produce pictures of people (that don’t exist) – that is undoubtedly creation (of a particularly unnerving variety). The same is true of the AI that can produce music. But those models are trained to produce more of what we recognise as good. Because art is no science, a machine would likely have no better chance of producing a masterpiece than a human. And true innovation, in many instances, requires subverting the data set, not conforming to it.

Secondly, and perhaps more importantly, training AI requires time and money. Some actions are simply too expensive to automate. These tasks are either incredibly specialised, and therefore do not have enough data to support the development of a model, or very broad, which would require so much data that it will render the training of the machine economically unviable. There are also other challenges which may arise. At the IDC, we refer to the Scope of AI-Based Automation. In this scope:

  • A task is the smallest possible unit of work performed on behalf of an activity.
  • An activity is a collection of related tasks to be completed to achieve the objective.
  • A process is a series of related activities that produce a specific output.
  • A system (or an ecosystem) is a set of connected processes.

As we move up the stack from task to system, we find different obstacles. Let’s use the medical industry as an example to show how these constraints interact. Medical image interpretation bots, powered by neural networks, exhibit exceptionally high levels of accuracy in interpreting medical images. This is used to inform decisions which are ultimately made by a human – an outcome that is dictated by regulation. Here, even if we removed the regulation, those machines cannot automate the entire process of treating the patient. Activity reminders (such as when a patient should return for a check-up, or reminders to follow a drug schedule) can in part be automated, with ML applications checking patient past adherence patterns, but with ultimate decision-making by a doctor. Diagnosis and treatment are a process that is ultimately still the purview of humans. Doctors are expected to synthesize information from a variety of sources – from image interpretation machines to the patient’s adherence to the drug schedule – in order to deliver a diagnosis. This relationship is not only a result of a technicality – there are ethical, legal and trust reasons that dictate this outcome.

There is also an economic reason that dictates this outcome. The investment required to train a bot to synthesize all the required data for proper diagnosis and treatment is considerable. On the other end of the spectrum, when a patient’s circumstance requires a largely new, highly specialised or experimental surgery, a bot will unlikely have the data required to be sufficiently trained to perform the operation and even then, it would certainly require human oversight.

The economic point is a particularly important one. To automate the activity in a mine, for example, would require massive investment into what would conceivably be an army of robots. While this may be technically feasible, the costs of such automation likely outweigh the benefits, with replacement costs of robots running into the billions. As such, these jobs are unlikely to disappear in the medium term. 
Thus, based on technical feasibility alone our medium-term jobs market seems to hold opportunity in the following areas: the hyper-specialised (for whom not enough data exists to automate), the jack-of-all-trades (for whom the data set is too large to economically automate), the true creative (who exists to subvert the data set) and finally, those whose job it is to use the data. However, it is not only technical feasibility that we should consider. Too often, the rhetoric would have you believe that the only thing stopping large scale automation is the sophistication of the models we have at our disposal, when in fact financial, regulatory, ethical, legal and political barriers are of equal if not greater importance. Understanding the interplay of each of these for a role in a company is the only way to divine the future of that role.

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LG unveils NanoCell TV range

At the recent LG Electronics annual Innofest innovation celebration in Seoul, Korea, the company unveiled its new NanoCell range: 14 TVs featuring ThinQ AI technology. It also showcased a new range of OLED units.

The new TV models deliver upgraded AI picture and sound quality, underpinned by the company’s second-generation α (Alpha) 9 Gen 2 intelligent processor and deep learning algorithm. As a result, the TVs promise optimised picture and sound by analysing source content and recognising ambient conditions.

LG’s premium range for the MEA market is headlined by the flagship OLED TV line-up, which offers a variety of screen sizes: W9 (model 77/65W9), E9 (model 65E9), C9 (model 77/65/55C9) and B9 (model 65/55B9).

NanoCell is LG’s new premier LED brand, the name intended to highlight outstanding picture quality enabled by NanoCell technology. Ensuring a wider colour gamut and enhanced contrast, says LG, “NanoColor employs a Full Array Local Dimming (FALD) backlight unit. NanoAccuracy guarantees precise colours and contrast over a wide viewing angle while NanoBezel helps to create the ultimate immersive experiences via ultra-thin bezels and the sleek, minimalist design of the TV.”

The NanoCell series comprises fourteen AI-enabled models, available in sizes varying from 49 to 77 inches (model 65SM95, 7565/55SM90, 65/55/49SM86 and 65/55/49SM81).

The LG C9 OLED TV and the company’s 86-inch 4K NanoCell TV model (model 86SM90) were recently honoured with CES 2019 Innovation Awards. The 65-inch E9 and C9 OLED TVs also picked up accolades from Dealerscope, Reviewed.com, and Engadget.

The α9 Gen 2 intelligent processor used in LG’s W9, E9 and C9 series OLED TVs elevates picture and sound quality via a deep learning algorithm (which leverages an extensive database of visual information), recognising content source quality and optimising visual output.

The α9 Gen 2 intelligent processor is able to understand how the human eye perceives images in different lighting and finely adjusts the tone mapping curve in accordance with ambient conditions to achieve the optimal level of screen brightness. The processor uses the TV’s ambient light sensor to measure external light, automatically changing brightness to compensate as required. With its advanced AI, the α9 Gen 2 intelligent processor can refine High Dynamic Range (HDR) content through altering brightness levels. In brightly lit settings, it can transform dark, shadow-filled scenes into easily discernible images, without sacrificing depth or making colours seem unnatural or oversaturated. LG’s 2019 TVs also leverage Dolby’s latest innovation, which intelligently adjusts Dolby Vision content to ensure an outstanding HDR experience, even in brightly lit conditions.

LG’s audio algorithm can up-mix two-channel stereo to replicate 5.1 surround sound. The α9 Gen 2 intelligent processor fine-tunes output according to content type, making voices easier to hear in movies and TV shows, and delivering crisp, clear vocals in songs. LG TVs intelligently set levels based on their positioning within a room, while users can also adjust sound settings manually if they choose. LG’s flagship TVs offer the realistic sound of Dolby Atmos for an immersive entertainment experience.

LG’s 2019 premium TV range comes with a new conversational voice recognition feature that makes it easier to take control and ask a range of questions. The TVs can understand context, which allows for more complex requests, meaning users won’t have to make a series of repetitive commands to get the desired results. Conversational voice recognition will be available on LG TVs with ThinQ AI in over a hundred countries.

LG’s 2019 AI TVs support HDMI 2.1 specifications, allowing the new 4K OLED and NanoCell TV models to display 4K content at a remarkable 120 frames per second. Select 2019 models offer 4K high frame rate (4K HFR), automatic low latency mode (ALLM), variable refresh rate (VRR) and enhanced audio return channel (eARC).

To find out more about LG’s latest TVs and home entertainment systems, visit https://www.lg.com/ae.

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