Sarah Friar will be joining our flagship online event,TNW2020, to talk about how the power of kindness can work as a foundation for business. Secureyour free ticket hereand join us October 1 & 2.
The world of business is often denoted as cut-throat, competitive, and ruthless. And while this may of course be true, there’s also room for kindness — that’s at least what Sarah Friar, the CEO of Nextdoor, believes.
A seasoned CEO — she was previously at Square and Salesforce — Friar is a great believer that profit and kindness aren’t mutually exclusive. In fact, she’s known for offering actionable insights on how monetization can be driven by responsibility and purpose.
Nextdoor is a hyperlocal social networking service for neighborhoods that seeks to bring people and local businesses together.
It monetizes by offering brands, local businesses, and public authorities and governments the opportunity to advertise on its platform so long as they are relevant to each community.
In other words, the message needs to add value to Nextdoor’s members and the platform has a responsibility to ensure its advertisers are vetted to meet these criteria.
“You’ve got to walk the talk, there’s actually no other way to do it. You have to be authentic and genuine,” Friar adds.
Friar really is best placed to chat about how advertising can be used for good (yes, you’ve read that right!) to empower local communities and individuals while also generating revenue — and I look forward to digging deeper into that topic during our discussion at TNW2020 this week.
Personally, I’m also very keen to hear what Friar has to say about the challenges she’s endured in growing a community-driven platform. For example, how can businesses such as Nextdoor deal with the proliferation of racism, or other types, of hate speech?
In a world so focused on growth — sometimes at all costs — and given the current zeitgeist, I’d say kindness has never been so important.
Don’t miss this refreshing take on how technology can be used for good, to create better, more successful businesses, that actually care about their people and customers.
You’ll walk away with plenty of food for thought, a new perspective on business, and hopefully, an increased interest in using your power for good.
What are you waiting for? Secure your free ticket toTNW2020now!
Conversational AI has been around for a few years now, in our phones, smart speakers, and throughout connected homes. And while the adoption of this tech is steadily increasing, most applications in this space essentially only enable you to push buttons using your voice.
The next frontier for conversational AI lies beyond your doorstep. From education to workforce automation to retail, the scope for plenty of novel new applications is opening up as the technology improves.
As Shreyas Nivas of Replica wrote recently, we will see a shift in voice AI from services being “primarily transactional — ‘Alexa, tell me the weather’ — to being based on dynamic interactions and relationships between characters in any digital narrative or experience.”
There are gains to be made in better understanding nuance in human speech, deriving more insights from the data in users’ commands and questions, interaction personalization, and delivering more natural responses.
To that end, companies like NYC-based Rain are working on solutions to leverage these advancements and deploy them across organizations — and even help brands create unique voice-powered experiences for their customers.
Rain’s CEO, Nitya Thadani, will be joining me for a fireside chat about the future of conversational AI, what we can look forward to accomplishing with our voices, and how businesses can think about integrating voice technologies into their operations and offerings in the future. Tune in to our conversation at TNW2020 next month.
Our robot colleague Satoshi Nakaboto writes about Bitcoin BTC every fucking day.
Welcome to another edition of Bitcoin Today, where I, Satoshi Nakaboto, tell you what’s been going on with Bitcoin in the past 24 hours. As Kierkegaard used to say: Crack open this tasty lobster!
We closed the day, September 27 2020, at a price of $10,774. That’s a minor 0.19 percent increase in 24 hours, or $21. It was the highest closing price in six days.
We’re still 46 percent below Bitcoin‘s all-time high of $20,089 (December 17 2017).
Bitcoin market cap
Bitcoin‘s market cap ended the day at $199,347,495,686. It now commands 58 percent of the total crypto market.
Yesterday’s volume of $18,016,880,214 was the lowest in twenty-eight days, 21 percent below last year’s average, and 75 percent below last year’s high. That means that yesterday, the Bitcoin network shifted the equivalent of 298 tons of gold.
A total of 259,230 transactions were conducted yesterday, which is 18 percent below last year’s average and 42 percent below last year’s high.
Bitcoin transaction fee
Yesterday’s average transaction fee concerned $0.74. That’s $3.17 below last year’s high of $3.91.
Bitcoin distribution by address
As of now, there are 17,911 Bitcoin millionaires, or addresses containing more than $1 million worth of Bitcoin.
Furthermore, the top 10 Bitcoin addresses house 4.8 percent of the total supply, the top 100 14.1 percent, and the top 1000 34.8 percent.
Company with a market cap closest to Bitcoin
With a market capitalization of $200 billion, Pfizer has a market capitalization most similar to that of Bitcoin at the moment.
Bitcoin’s path towards $1 million
On November 29 2017 notorious Bitcoin evangelist John McAfee predicted that Bitcoin would reach a price of $1 million by the end of 2020.
He even promised to eat his own dick if it doesn’t. Unfortunately for him it’s $621K behind being on track. Bitcoin‘s price should have been $632,000 by now, according to dickline.info.
Bitcoin energy consumption
On a yearly basis Bitcoin now uses an estimated 70 terawatt hour of electricity. That’s the equivalent of Colombia’s energy consumption.
Bitcoin on Twitter
Yesterday 37,113 fresh tweets about Bitcoin were sent out into the world. That’s 77.1 percent above last year’s average. The maximum amount of tweets per day last year about Bitcoin was 82,838.
Most popular posts about Bitcoin
This was yesterday’s most engaged tweet about Bitcoin:
Grayscale just bought another 17,100 MORE #bitcoin! They now own 2.5% of the entire supply! They will soon enough own 5% then 10%. Stack those sats friends, get yours before Grayscale does! https://t.co/5GfIOoWHBE
So you’re interested in AI? Thenjoin our online event, TNW2020, where you’ll hear how artificial intelligence is transforming industries and businesses.
The Game of Life is a grid-based automaton that is very popular in discussions about science, computation, and artificial intelligence. It is an interesting idea that shows how very simple rules can yield very complicated results.
Their findings highlight some of the key issues withdeep learning modelsand give some interesting hints at what could be the next direction of research for the AI community.
What is the Game of Life?
British mathematician John Conway inventedthe Game of Lifein 1970. Basically, the Game of Life tracks the on or off state—the life—of a series of cells on a grid across timesteps. At each timestep, the following simple rules define which cells come to life or stay alive, and which cells die or stay dead:
If a live cell has less than two live neighbors, it dies of underpopulation.
If a live cell has more than three live neighbors, it dies of overpopulation.
If a live cell has exactly two or three live neighbors, it survives.
If a dead cell has three live neighbors, it will come to life.
Based on these four simple rules, you can adjust the initial state of your grid to create interesting stable, oscillating, and gliding patterns.
For instance, this is what’s called the glider gun.
You can also use the Game of Life to create very complex pattern, such as this one.
Interestingly, no matter how complex a grid becomes, you can predict the state of each cell in the next timestep with the same rules.
With neural networks beingvery good prediction machines, the researchers wanted to find out whether deep learning models could learn the underlying rules of the Game of Life.
Artificial neural networks vs the Game of Life
There are a few reasons the Game of Life is an interesting experiment for neural networks. “We already know a solution,” Jacob Springer, a computer science student at Swarthmore College and co-author of the paper, toldTechTalks. “We can write down by hand a neural network that implements the Game of Life, and therefore we can compare the learned solutions to our hand-crafted one. This is not the case in.”
It is also very easy to adjust the flexibility of the problem in the Game of Life by modifying the number of timesteps in the future the target deep learning model must predict.
Also, unlike domains such ascomputer visionornatural language processing, if a neural network has learned the rules of the Game of Life it will reach 100 percent accuracy. “There’s no ambiguity. If the network fails even once, then it is has not correctly learned the rules,” Springer says.
In their work, the researchers first created a smallconvolutional neural networkand manually tuned its parameters to be able to predict the sequence of changes in the Game of Life’s grid cells. This proved that there’s a minimal neural network that can represent the rule of the Game of Life.
Then, they tried to see if the same neural network could reach optimal settings when trained from scratch. They initialized the parameters to random values and trained the neural network on 1 million randomly generated examples of the Game of Life. The only way the neural network could reach 100 percent accuracy would be to converge on the hand-crafted parameter values. This would imply that the AI model had managed to parameterize the rules underlying the Game of Life.
But in most cases the trained neural network did not find the optimal solution, and the performance of the network decreased even further as the number of steps increased. The result of training the neural network was largely affected by the chosen set training examples as well as the initial parameters.
Unfortunately, you never know what the initial weights of the neural network should be. The most common practice is to pick random values from a normal distribution, therefore settling on the right initial weights becomes a game of luck. As for the training dataset, in many cases, it isn’t clear which samples are the right ones, and in others, there’s not much of a choice.
“For many problems, you don’t have a lot of choice in dataset; you get the data that you can collect, so if there is a problem with your dataset, you may have trouble training the neural network,” Springer says.
The performance of larger neural networks
Inmachine learning, one of the popular ways to improve the accuracy of a model that is underperforming is to increase its complexity. And this technique worked with the Game of Life. As the researchers added more layers and parameters to the neural network, the results improved and the training process eventually yielded a solution that reached near-perfect accuracy.
But a larger neural network also means an increase in the cost of training and running the deep learning model.
On the one hand, this shows the flexibility of large neural networks. Although a huge deep learning model might not be the most optimal architecture to address your problem, it has a greater chance of finding a good solution. But on the other, it proves that there is likely to be a smaller deep learning model that can provide the same or better results—if you can find it.
These findings are in line with “The Lottery Ticket Hypothesis,” presented at the ICLR 2019 conference by AI researchers at MIT CSAIL. The hypothesis suggested that for each large neural network, there are smaller sub-networks that can converge on a solution if their parameters have been initialized on lucky, winning values, thus the “lottery ticket” nomenclature.
[embedded content] “The lottery ticket hypothesis proposes that when training a convolutional neural network, small lucky subnetworks quickly converge on a solution,” the authors of the Game of Life paper write. “This suggests that rather than searching extensively through weight-space for an optimal solution, gradient-descent optimization may rely on lucky initializations of weights that happen to position a subnetwork close to a reasonable local minima to which the network converges.”
What are the implications for AI research?
“While Conway’s Game of Life itself is a toy problem and has few direct applications, the results we report here have implications for similar tasks in which a neural network is trained to predict an outcome which requires the network to follow a set of local rules with multiple hidden steps,” the AI researchers write in their paper.
These findings can apply to machine learning models used logic or math solvers, weather and fluid dynamics simulations, and logical deduction in language or image processing.
“Given the difficulty that we have found for small neural networks to learn the Game of Life, which can be expressed with relatively simple symbolic rules, I would expect that most sophisticated symbol manipulation would be even more difficult for neural networks to learn, and would require even larger neural networks,” Springer said. “Our result does not necessarily suggest that neural networks cannot learn and execute symbolic rules to make decisions, however, it suggests that these types of systems may be very difficult to learn, especially as the complexity of the problem increases.”
The researchers further believe that their findings apply to other fields of machine learning that do not necessarily rely on clear-cut logical rules, such as image and audio classification.
For the moment, we know that, in some cases, increasing the size and complexity of our neural networks can solve the problem of poorly performing deep learning models. But we should also consider thenegative impact of using larger neural networksas the go-to method to overcome impasses in machine learning research. One outcome can begreater energy consumption and carbon emissionscaused from the compute resources required to train large neural networks. On the other hand, it can result in the collection of larger training datasets instead of relying on finding ideal distribution strategies across smaller datasets, which might not be feasible in domains where data is subject to ethical considerations and privacy laws. And finally, the general trend toward endorsing overcomplete and very large deep learning models canconsolidate AI power in large tech companiesand make it harder for smaller players to enter the deep learning research space.
“We hope that this paper will promote research into the limitations of neural networks so that we can better understand the flaws that necessitate overcomplete networks for learning. We hope that our result will drive development into better learning algorithms that do not face the drawbacks of gradient-based learning,” the authors of the paper write.
“I think the results certainly motivate research into improved search algorithms, or for methods to improve the efficiency of large networks,” Springer said.
This article was originally published by Ben Dickson onTechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.
When talking about the future of technology, how far can be we look ahead? Fifty years? Thirty years?
According to technologist and anthropologist Genevieve Bell, fifteen years is the maximum — or we’re entering the realm of science fiction.
Genevieve is the kind of person you’d want to be stuck on a deserted island with. Not only is she a brilliant technologist with countless juicy anecdotes to share; she also knows how to detract water from frogs, an aboriginal survival skill she picked up as a child while living in central Australia with her anthropologist mother.
Genevieve has always been fascinated with how people interact with technologies. In one of her past projects, she “excavated” peoples’ cars to help understand what their cars meant to them.
Not surprisingly, this very much depends on social and cultural context. Car owners in Singapore often keep a red envelope with cash in their glove compartments — also known as Ang Pao — in case they visit a wedding and realize their gift isn’t good enough. Car owners in her home country, Australia, tend to bring sunscreen, sunglasses, and booze.
In recent years, Genevieve’s main focus has shifted to AI and cyber-physical systems. She’s the director of the 3A Institute, which aims to develop a new educational branch of engineering for the AI developers of the future. Something that’s very necessary, she says, to ensure these technologies will have a positive impact on humanity.
So what will the future hold for AI? Will we ever reach general artificial intelligence — meaning computers can think like humans? And how realistic is the plot of Blade Runner?
This Friday, I will discuss all of these topics with Genevieve Bell during TNW2020. The session is titled ‘A look into tech’s far future’ and will be hosted on the Impact stage at 6 PM CET.Get your free ticket here.
They say size doesn’t matter, but it’s tough to ignore it when it comes to Call of Duty patches. True to the meme, Inifnity Ward has dropped yet another massive update for the new Season 6 of Modern Warfare and Warzone. So consider this a friendly reminder: if you’re hoping to get in on the action today, you better start downloading it now.
The update is already available for download for all platforms. Here’s how much “damage” you can expect to your hard drive. Warning: it’s a lot. Let’s start with the worst.
PC: owners of Modern Warfare need to free up a little over 57GB. The Warzone-only update is 22.5GB, though.
PlayStation 4: you lucky bastards are getting away with only 19.3GB.
Beautiful and usable design can be the deciding factor between a successful and a failing business. Great designers are treated like superstars. And it’s so fulfilling to design beautiful things that people love to use. But can you become a designer at all without a degree in design? Will you have to go back to school to pursue the career of your dreams?
In my opinion, yes and no. Based on my own experience, I’m gonna share five pieces of advice that will bring you closer to becoming a designer, no matter what you’re doing right now.
It was during my Bachelor’s thesis that I first came into contact with user research; and after that, I fell in love with anything usability and UX design. Today, I’m a UX Manager and take care of a wide variety of design topics. But when you simply look at my diplomas, you wouldn’t expect any of that, since I hold a B.Sc. in computer science and two engineering degrees.
But how did I do it? How did I start off as a computer science student thinking about a career in software engineering and end up teaching UX design to students at the University of Michigan? What would I tell someone who asks me:I have studied X, which has nothing to do with design, how do I become a designer?
1. Understand that the process is the design
The first step towards becoming a designer is to understand what design really is. Many people still mistake design for visual design. But design isn’t just Photoshop. It’s the entirety of the underlying process,includingwhat the final outcome looks like.
According to Alan Cooper’sAbout Face, this includes designingbehavior,form, andcontent. Also, it involves a considerable amount of user research because good design cannot happen without a deep understanding of your users.
Design is nothing one person simply makes up and that’s it. You’re always designingforsomeone by solving a problemtogetherwith other designers. I want to give you two quotes to underpin this.
“Design Thinking is the understanding that the process is the design and therefore all people involved, no matter their role, are responsible for creating a product that is useful, functional, aesthetically appealing, and affordable.”
And the second one, by Ana Kraš:
“Design is not decoration. Design is to make something work. Design is a thought process, the solving of a problem.”
2. Read Don Norman’s The Design of Everyday Things
Don Norman is a professor emeritus of cognitive science and headed Apple’s Advanced Technology Group. And he has writtenthebook that tells you all you need to know about the theory of design: the psychological and cognitive backgrounds, how to tell good design from bad, why everything around you is (mostly bad) design, how humans form mental models, the basics of human-centered design, the double diamond model (see below), and much more.
If you want to become a designer, you’ll have to readThe Design of Everyday Things.
3. Practice sketching and rapid prototyping (a lot)
While you don’t need Photoshop skills to become a designer, it’s inevitable to sketch a lot. Not only is it fun, but also makes it easier to reason your ideas, never forget an idea in the first place, and to get your creative juices flowing (cf.Boost your creativity with daily sketching).
Yet, this doesn’t mean your sketches have to look good, as long as they manage to get your idea across in terms of the structure of a product and the interactions you envision. Hence, buy a notebook and scribble as if there’s no tomorrow! Basically, a good UX design can be as little as sketches on a napkin.
As forrapid prototyping, I highly recommend paper prototypes. They’re easy to create, simulate interactivity, and deliver a ton of extremely helpful insights before writing actual code. For more on this, read Marc Rettig’s classicPrototyping for Tiny Fingers.
Moreover, you can create pretty elaborate prototypes using tools likeInVisionorBalsamiq. Since I have a technical background, I usually just prototype directly in HTML/CSS/JS.
Essentially, you have to play around with all the different possibilities and stick to those you feel most comfortable with. The important thing is: a prototype isn’t polished; it’s meant for collecting feedback quickly and iterating — hence,rapidprototyping.
4. Learn to conduct user research
Ahugeportion of design consists of user research, both for identifying problems and finding solutions. Hence, it’s indispensable to understand the necessity and value of good research and to be able to conduct proper research.
Depending on the stage of the design process, the methods you use can differ greatly. Early on, they tend to be more qualitative and attitudinal. For instance, you’d do some ethnographic field research to discover user’s pain points and everyday problems. Later, when research questions evolve more around, e.g., the usability of a product, methods become more quantitative and behavioral.
For a nice, comprehensive overview of user research methods, please refer tothis articleby Christian Rohrer.
Another source I’d recommend for learning about the immense value of user research is Robert Hoekman, Jr.’sExperience Required.
In 2018, I attended “A Conversation with David Kelley” at the University of Michigan. Kelley is one of the founders of IDEO and a professor at Stanford University. Similar to the topic of this article, someone from the audience asked something along the lines of:I work in a non-design job and therefore don’t get assigned any design tasks. How can I manage to be recognized more as a designer?
Kelley answered the following: do what you were asked to do, but do it using a design methodology. This is what he calleddouble delivery.
Becoming a designer can seem like a nearly impossible endeavor if you’ve never formally done anything with design. However, it is possible, regardless of your formal education, even though it will surely take time and effort.
Essentially, anyone who makes something that is used by other people, be it a chair, a mobile app, or a business process, already does design — whether they’re aware of it or not. However,beinga designer takes a little more than that.
If you understand what design really is, learn to sketch and prototype, learn to conduct quality user research, and engage in double delivery whenever possible, all doors will be open to you.
The ban would’ve seen the short video app taken off app stores, which would have made it difficult for new users to download it on their mobile devices. It would also make it impossible for TikTok to push updates and bug fixes to its app via those stores. These restrictions would have damaged both its user experience, as well as its ability to grow its user base in the US.
How did this injunction take place? TikTok‘s lawyers argued in a hearing on Sunday that having the app booted from stores just ahead of the election and in the midst of a pandemic ‘would impinge on the rights of potential new users to share their views.’ It appears this convinced judge Carl Nichols of the District Court for the District of Columbia.
As it stands, Oracle — along with Walmart — is slated to invest in TikTok Global, a company spun off from developer ByteDance that will include TikTok‘s operations in the US and several other countries. Oracle will get a 12.5% stake in that new company and become its cloud services partner as well.
It’s clearly a messy state of affairs, and things will likely continue to get crazier in the coming weeks. The NYT noted that November will see a broader set of restrictions on TikTok. That battle to TikTok a-ticking, it seems, is going to be long and arduous.
Viruses jumping from animals to humans have been the starting point of numerous outbreaks, from Ebola to Zika. Given the similarity of SARS-CoV-2 to coronaviruses found in bats, this probably marked the beginning of COVID-19 too.
We know that viruses have passed from animals to humans throughout history, and will continue to do so. But the factors that influence the geographical origin of these events is less clear, despite being highly important. Knowing where they occur can help us understand the factors behind a virus crossing species, in particular, by looking at the traits of viruses circulating in the ecosystem where the jump happened.
But identifying a virus’s origin is sometimes difficult. Human movement is constant and wide-ranging, which means that the first case of a disease can be hundreds, if not thousands of miles away from where transmission into humans started. Given this, where should we be looking for the virus that might cause the next epidemic?
Generally, viruses emerge where humans and animals that carry viruses intersect. Repeated interaction between people, these animals or insects, and the wider environment in which the virus circulates increases the opportunity for a jump across species. These jumps are believed to be rare, and probably happen due to a specific set of circumstances that cannot necessarily be predicted.
Humans are exposed to viruses all the time. Most of these exposures lead to a “dead-end infection,” where the virus isn’t passed on. Occasionally, though, the virus may be able to replicate and be transmitted to a new host, or if vector-borne, to an insect that establishes a novel and functional transmission cycle.
This happens all over the world, though recent headline-grabbing outbreaks give the impression that viruses emerge in some places more than others. In particular, the seriousness of outbreaks such as Sars in Asia and Ebola in Africa makes it look like these are the only places where it happens. This isn’t the case.
For example, the Schmallenberg virus, which primarily infects livestock and causes spontaneous abortion in infected animals, recently appeared in Europe. And while we don’t hear much about viruses emerging from South America, it does happen. The Venezuelan equine encephalitis virus and the Mayaro virus have repeatedly caused outbreaks in South and Central America. It’s only because these diseases haven’t spread beyond the Americas that they aren’t more widely known.
A further factor that has prevented the Mayaro virus from gaining more attention is that it has very similar symptoms to disease caused by another virus – chikungunya. It’s also often misdiagnosed as dengue fever, meaning the true number of Mayaro cases isn’t being reported.
This points to a wider issue, which is that most viruses initially cause very similar symptoms. In areas where dengue or malaria are endemic, most viral diseases are attributed to these infections, masking the appearance of new viruses until they become common – by which point they may have spread from their point of origin. More effective and faster diagnostics are needed to help identify these sorts of novel diseases before they have a chance to shift into new transmission cycles.
Humans close to where a virus is endemic don’t always show evidence of it emerging, either. Through regular exposure to the virus, they may not show any symptoms of infection. It may be only after the virus moves into an unexposed population that there are enough cases for it to be identified. In the highly connected world of today, this could be halfway around the globe.
We need to look at hosts
If it’s not really feasible to determine where the next epidemic will start by simply looking at a map, then what should we do? Well, a better method is to try and understand the endemic transmission cycle of viruses – that is, to look at the animals and environments in which viruses replicate without causing human disease – and then work backward.
Knowing what viruses are already out there in animals can help us trace the origins of human diseases when new outbreaks occur. This knowledge is critical to understanding the potential risks in different areas of the globe. It can also help us unpick what factors make it more likely that viruses will jump into humans.
For instance, with SARS-CoV-2 it was previous research into the transmission cycles of bat coronaviruses in China that helped identify these animals as the likely origin of the outbreak. This is now letting us investigate what it is about bats that means they’re so often involved in viruses crossing into humans.
Our understanding of the virus species present in bats and other species is only at its beginning – in fact, the study that helped trace the origins to SARS-CoV-2 to bats in China was recently halted. If we’re serious about trying to predict what the next dangerous virus might be – and where it might come from – we need instead to be expanding this sort of work, not ending it.
TLDR:TimeSync Pro saves you time and headaches by taking full control of your scheduling and contacts.
Decentralized remote workforces have quickly become the routine. While eradicating the commute and never having to wear pants to work in your home office are huge upsides, there are a couple of drawbacks too.
In many cases, workers no longer enjoy the in-house computer network of their workplace, which means everybody is doing their own thing when it comes to stuff like scheduling. If you thought it was tough to corral everybody in the sales team for a meeting when you were all under one roof, just try it in this scheduling Wild West we’re in now.
With TimeSync, you have a fully customizable online interface that can be embedded right on to your website. It integrates seamlessly with your current calendar, whether you use Outlook Calendar, Google Calendar or other scheduling environments. In fact, TimeSync plays nice with virtually all major business or meeting platforms, allowing you to sync meetings and outcomes with others over places like Zoom, Salesforce, Google Hangouts, or HubSpot. Even Google Analytics and Facebook integrate perfectly with TimeSync.
If someone wants to connect with you, all they have to do is look at your current available days, pick a time for a meeting or phone call, and book their appointment. It not only eliminates all the back-and-forth around setting dates and times but makes sure you never fall prey to the dreaded double booking again.
But what if the person who wants to reach you isn’t someone you want to meet with? No problem. TimeSync Pro lets you set up screening questions to find out exactly what someone wants to chat with you about. You might let someone who identifies themself as an existing client move one to schedule an appointment while filtering everyone else to an online support desk for more information.
Your interface can be fully customized so you can control your schedule just the way you want it. Regularly $420, you can now get a lifetime of TimeSync Pro access forjust $39.99. You can even save over 60 percent off the cost ofa single year subscription, now only $29.99.