In this webinar of The Business Storm, listen in as Jeffrey Hayzlett, Chairman of the C-Suite Network and former Fortune 100 CMO discusses with Thomas White, Co-Founder and CEO of the C-Suite Network on how to “Maximize Your Brand on the Lowest Budget Possible”. Uncover the importance of being an authentic brand and generating content. Jeffrey Hayzlett presents the 4 steps to the secret formula for maximizing your brand that you can start incorporating into your brand development strategy today. Listen Now!
Author: Doug Gollan / Source: Forbes
Bill Gates, a pretty smart guy, supposedly once said something along the lines, “Now, you can throw away your travel agent’s phone number.” Whether he believed it or not is hard to say. At the time he would have been plugging his new online travel agency (OTA) Expedia. Certainly, Gates wasn’t wrong in forecasting that online sales of travel would become significant. However, things have changed quite a bit in the two decades since he made that proclamation.
It’s a long list, but even back in the Nineties, there were airline delays and bankruptcies throwing the proverbial monkey wrench into travel plans. However, the system had a bit more flexibility. American Airlines used to keep extra aircraft at its hubs as back ups in case of mechanical delays. Not anymore. Remember when if one shuttle flight filled up, they would bring out a spare plane so you didn’t have to wait for an extra hour? Airline load factors have increased from the 60% range into the 80s, meaning that flights on popular routes are often sold out for days at a time. Front line employees used to have the ability to grant waivers and favors when you had extra baggage or needed to change a flight. Not so much these days. Hotels and rental car companies were more liberal with their cancellation policies. In many cases, you didn’t have to show your credit card until you showed up. Today, both entities seem to be copying the airlines to make it as onerous as possible for you to change plans. Yes, there are two sides to every story, and they have legitimate reasons for stricter rules, but it is what it is. Good luck trying to get an exception.
At any rate, the travel agents that Gates dogged have done okay. Yes, there was attrition, but as Chris Cahill, CEO of Accor’s luxury hotel brands says, “The crème rises to the top.” Those that are here today seem to be doing very well. It’s estimated there are about 100,000 travel agents in the U.S. today. Here in Las Vegas, there were over 5,700 attendees at Virtuoso Travel Week, including over 2,700 of the group’s travel advisors. Virtuoso is an umbrella for independently owned agencies that generate over $21 billion in travel sales. Not shabby for a group often labeled as dinosaurs. Still, Expedia, Inc., which now includes the likes of Hotwire, Hotels.com, Travelocity and Orbitz has grown both organically and through acquisition to $72 billion.
The agents, or advisors as they call themselves, have very few concerns about online competitors these days. Multiple research surveys show consumer use of retail travel agents – read human beings, has been on the rise for at least five years and is strongest with Millennials, a good sign for the future. Virtuoso’s CEO Matthew Upchurch says this younger consumer segment is very comfortable paying for expertise such as personal trainers, and view agents as folks who can help them improve the quality of their travel experiences. Booking travel isn’t as easy as Gates wanted you to think and as many of us have found out. Google’s own research shows it takes 32 visits to 10 different websites to book a single airline ticket. Add to that the OTA’s spotty record of customer service.
You might read this and say so what? I don’t use a travel agent, and am happy I am doing it myself. Well, you might want to reconsider. While agents are happy with their growing business and profits, a soon to be released survey of agents who sell luxury travel by Travel Market Report, a daily e-newsletter and website for agents, shows agents aren’t concerned about their future, but 73% are concerned that suppliers will be able to maintain service standards as the market expands.
Interestingly, Upchurch says a key reason to use agents is advocacy. In other words, when you use a travel agent, you have somebody who has relationships decision makers at the airlines, hotels, cruises lines and car rental companies you will be traveling…
Author: Blake Morgan / Source: Forbes
There’s been a lot of hype about AI, robots, chatbots and the like. And I admit I cover this topic. I speak on this topic as well – and I think it’s an exciting time in technology. But I’m getting tired of the hype of artificial intelligence.
While we talk up robots, AI and there is nothing like being served by someone who seems to genuinely want to be serving. One reason we are so excited about robots, automation and self-service technology is we’ve been trying to create space between brands and customers for years. Dealing with customers can be expensive, resource draining and time consuming.
The idea is: “how can we push as many people as possible through the same experience while keeping our operating costs as low as possible?” But let’s have a straight talk about robots and automation. Would you want a robot giving you an important medical diagnosis? Would you want to go to a theater and watch a cast of robots perform for you? Let’s talk about stuff robots can’t do and calm down with this dystopian view of the future where people lose their usefulness.
10 Things Robots Can’t Do
1. A robot can’t look you in the eye
Machines will not destroy man as long as we remember machines are in service to mankind. Last night I was watching the film “Hidden Figures” – in the movie the actor who plays astronaut John Glenn says regarding the IBM mainframe that shot out potentially incorrect trajectory information for his landing “you can’t trust something you cant look in the eyes.” He’s right. Would you trust something that can’t interpret events, actions, or tones as much as a human? Machines are in service to us. It should always be that way. When we let technology loose without much management or interference from a human – it usually doesn’t end well.
2. Consider the feelings of the other person
If an interactive voice response (IVR) program (a phone tree) could consider and interpret the feelings of customers on the other end of the phone, the robot might malfunction in shock. Many people have thrown their phone on the ground, tired of an IVR that doesn’t understand what they (the customer) are requesting. Or the IVR doesn’t offer what the customer requests. Customer service can be a messy business because you are dealing with human emotions. There’s a gray area. As customer frustration and anger goes up, a human being should be able to gauge that, sincerely apologize and offer some kind of appropriate service recovery. Service recovery means I will give you X for your inconvenience. Robots cannot consider feelings – genuinely, like a person can. Today millions of companies settle for terrible IVR experiences, keeping their customers at bay – never realizing how many customers they lose overnight because of these hard of hearing robots.
3. Make a person feel seen or heard
Sometimes working in customer service is like being a psychologist….
Author: Ed Moyle / Source: E-Commerce Times
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Ask any security practitioner about ransomware nowadays, and chances are good you’ll get an earful. Recent outbreaks like Petya and WannaCry have left organizations around the world reeling, and statistics show that ransomware is on the rise generally.
For example, 62 percent of participants surveyed for ISACA’s recent “Global State of Cybersecurity” survey experienced a ransomware attack in 2016, and 53 percent had a formal process to deal with it. While ransomware is already a big deal, it is set to become an even bigger deal down the road.
One of the questions organizations ask is what steps they can take to keep themselves protected. Specifically, what can organizations do to make sure that their organization is prepared, protected and resilient in the face of an outbreak?
A strategy that can work successfully is the long-tested “tabletop exercise” — that is, conducting a carefully crafted simulation (in this case, a ransomware situation) to test organizational response processes and validate that all critical elements are accounted for during planning.
This strategy works particularly well for ransomware because it encourages direct, frank and open discussions about a key area that is often a point of contention during an incident: the ransom itself.
What Is a Tabletop Exercise?
Invariably, in the context of an actual ransomware incident, someone will suggest paying the ransom. Sometimes it’s a business team that sees the ransom as a small price to pay to get critical activities back on track. In other cases, it might be executives who are eager to defer what is likely to be a long and protracted disruption to operations. Either way, paying the ransom can seem compelling when the pressure is on and adrenaline is high.
However, most law enforcement and security professionals agree that there are potential downsides to paying the ransom. First, there is the possibility that attackers won’t honor their end of the deal. A victim might pay them but lose its data anyway. Even if the attacker should follow through, there is the danger of creating a perception that the organization is a soft touch, which could induce attackers to retarget it down the road.
An organization might make a decision when feeling ransomware pressure that it would not make when thinking it through calmly in the abstract. That is why working through the issues ahead of time can be valuable.
The exercise prompts discussions about these topics and fosters calm and rational decision-making. Further, it helps familiarize critical personnel with response procedures, pre-empting “hair on fire” behavior if…
Author: Tiernan Ray / Source: barrons.com
Recently, I spoke with phone with Stan Swete, the chief technology officer of Workday (WDAY), the cloud computing company that competes with Oracle (ORCL) and other traditional enterprise software vendors.
The conversation was prompted by my having noticed a comment by Oracle’s founder, chairman, and CTO, Larry Ellison, back in September, during Oracle’s annual analyst day meeting, regarding Workday and its technology. I set out to ask Swete about Ellison’s remarks, and what ensued was a fascinating discussion of where software is headed.
Ellison, who is rather obsessed with defeating Workday, more so than he seems to care about defeating Salesforce.com (CRM), chided Workday back in September for what he said was their attempt to build their own database, as he put it:
They [Workday] decided not only were they going to build an HCM suite which they did, they are going to also build an ERP suite, much bigger job, 10 times bigger job, easily, but they are going to build their own database. Does everyone know that Workday built their own database? Are you kidding me? It sounds like you’re talking to the guys at SAP except you are a startup.
Is Workday building a database? I asked. In response, Swete told me he has gotten the question “quite a few times,” and he made clear that the company is not building a database.
In fact, Workday runs its cloud software on MySQL, an open-source database owned and managed by Oracle. “There’s a ton of things we did differently” in designing Workday’s software, he says, but building a database is not one of them.
“It is difficult to write your own reliable ACID transaction processing database, we agree with that,” he says, referring to the acronym for “atomicity, isolation, and durability,” features that come with a relational database management system like MySQL. “That’s what we get out of the database.”
He observed that “this conversation is a little ironic,” given Workday is a customer for Oracle’s MySQL.
Workday uses MySQL for so-called online transaction processing, or OLTP, engine, the bulk of all relational database work.
“We do use an RDBMS to store all customer data,” he says. “If someone’s saying we are not, they’re wrong. It’s all persisted in an RDMBS. We actually use MySQL.”
But, “We use that in a different way from any other vendor,” he continues.
“The typical vendor uses that not only to store data, but also to describe the shape and form of the app. Workday doesn’t use the database to describe the applications. That was a purposeful choice.”
Swete explains that Workday’s having not placed the application definition inside the relational database “leads to a simplified schema in the database that we can maintain across all our updates.”
“Typically the database becomes complex with the complexity of the app; we wanted to be able to have a database that was relatively unchanging from a structural standpoint.”
Instead, the application’s definition is in a set of Java language objects that are managed “in-memory”:
Rather than have the structure of our apps mapped to table, it’s in some other structures we have. We’ve committed since day one to be an in-memory application. For reporting and most processing we need to do, we don’t need to go to the database. People think we are an in-memory database, but we’re not using an in-memory database. The reason we did in-memory was to have our applications be more responsive for reporting and analysis. So don’t have to do a bunch of SQL or go to disk to generate reports.
Developers can “describe applications” in Workday “as an object model” without recourse to SQL, the query language used to managed the database. “They can describe a worker class, say, and an org class” without SQL, he notes. “That allows customers to make change without having to incur the large upgrade projects that have been money sinks for companies for a long time.”
That approach leads to applications that are “easier to own over time,” and that can be updated “without being a major IT project” for the customer.
“It’s actually central to what we are doing. If you can simplify the architecture, you offer the hope of having applications that can be more dynamically maintained on a consistent basis for customers.”
There are so many benefits. Simplified use is one benefit. We’ve abstracted away the notion of what database we are using from applications developers. With Peoplesoft where I was for ten years is we embraced RDMBS and SQL. That was fantastically powerful in the 80s. We completely tied ourselves to that…
Author: Lara O’Reilly / Source: WSJ
Good morning. President Donald Trump’s response to the violence that erupted in Charlottesville this weekend continues to dominate the headlines and present tough questions for companies assessing the brand risk of having ties to the administration. As The Wall Street Journal reports, recent events have “sparked a new round of soul-searching in U.S. corporate boardrooms over whether they should keep working closely with the White House.” One crisis management expert in the article is recommending corporate clients avoid any official Trump advisory panel or White House gathering of business leaders altogether.
Depending on which research you look at, spend on digital advertising has already eclipsed, or will overtake spend on “traditional” media this year. That inflection point, plus the heightened levels of scrutiny leveled at the online supply chain, has led to marketers increasingly taking back control of some of the digital advertising duties they once farmed out to their agencies, I reported for CMO Today. A survey from the World Federation of Advertisers found marketers have taken a range of actions over the past 12 months, such as setting up “hybrid” programmatic models — where they are building their own internal trading desks to handle a percentage of their digital ad spend — and adding clauses to their contracts about data ownership and audit rights. It’s important to note that agencies aren’t going anywhere any time soon. Marketers still benefit from their scale, expertise and experience dealing directly with digital vendors. But it can only be a good thing that more marketers are finally waking up to the fact that they need a complete handle over their digital activities.
Ad-funded internet companies are leading by example. Web companies increased their U.S. ad spending in the most-recent quarter at a “significantly faster pace” than other types of advertisers, according to Pivotal Research Group senior analyst Brian Wieser. That helped the overall U.S.ad market grow by around 5% in the second quarter, despite an estimated 1% decline in national TV advertising, double-digit declines for most print-based media and decreased spending among consumer packaged goods companies. Of course, the big spenders, like Google and Amazon, are also the online ad sellers benefiting from the wider digital advertising splurge. But all good things come to an end. Mr. Wieser says the double-digit growth in digital advertising we’ve witnessed in…
Author: Dan Costa / Source: PCMag India
This episode of Fast Forward was recorded in the IBM Watson Experience Center here in New York City. My guest was Rob High, the Vice President and Chief Technology Officer of IBM Watson.
High works across multiple teams within IBM, including engineering, development, and strategy. He is one of the most lucid thinkers in the space of artificial intelligence, and our conversation covered many of the way that technology is reshaping our jobs, our society and our lives. Read and watch our conversation below.
Dan Costa: What is the dominant misconception that people have about artificial intelligence?
Rob High: I think the most common problem that we’re running into with people talking about AI is they still live in the world where I think Hollywood has amplified this idea that cognitive computing, AI, is about replicating the human mind, and it’s really not. Things like the Turing test tend to reinforce that what we’re measuring is the idea of AI being able to compete with fooling people into believing that what you’re dealing with is another human being, but that’s really not been where we have found the greatest utility.
This even goes back to, if you look at almost every other tool that has ever been created, our tools tend to be most valuable when they’re amplifying us, when they’re extending our reach, when they’re increasing our strength, when they’re allowing us to do things that we can’t do by ourselves as human beings. That’s really the way that we need to be thinking about AI as well, and to the extent that we actually call it augmented intelligence, not artificial intelligence.
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Let’s talk a little bit about that shift, because it’s an entirely new type of computing. It’s the evolution of computing from what we both grew up with, a programmatic computing where you would use computation to reach and answer using a very complex process, to cognitive computing, which operates a little differently. Can you explain that transition?
Probably the biggest notable difference is that it’s very probabilistic, whereas programmed computing is really about laying out all the conditional statements that define the things that you’re paying attention to and how to respond to them. It’s highly deterministic. It’s highly mathematically precise. With a classic programmed computer, you can design a piece of software. Because you know what the mathematical model is that it represents, you can test it mathematically. You can prove its correctness.
Cognitive computing is much more probabilistic. It’s largely about testing the signals of the spaces that we’re focused on, whether that is vision or speech or language, and trying to find the patterns of meaning in those signals. Even then, there’s never absolute certainty. Now, this is in part because that’s the way it’s computed, but also because that’s the nature of human experience. If you think about everything that we say or see or hear, taste or touch or smell or anything that is part of our senses, we as human beings are always attempting to evaluate what that really is, and sometimes we don’t get it right.
What’s the probability that when I heard that sequence of sounds, it really meant this word? What’s the probability that when I saw this sequence of words it meant this statement? What’s the probability that when I see this shape and an image that I’m looking at that it is that object? Even for human beings, that’s a probabilistic problem, and to that extent it’s always the way that these cognitive systems work as well.
If somebody comes to you and they have a problem that they want to solve, they think that there is a cognitive computing solution to that, they come to Watson, they say, “Look, we’re going to use Watson to try and solve this problem.” Out of the box, Watson doesn’t do very much. They need to teach it how to solve their problem. Can you talk about that onboarding process?
Actually, we should talk about two dimensions of this. One is that some time ago we realized that this thing called cognitive computing was really bigger than us, it was bigger than IBM, it was bigger than any one vendor in the industry, it was bigger than any of the one or two different solution areas that we were going to be focused on, and we had to open it up, which is when we shifted from focusing on solutions to really dealing with more of a platform of services, where each service really is individually focused on a different part of the problem space. It’s a component that, in the case of speech, is focused strictly on the problem of trying to take your speech and recognize what words you’ve expressed in that speech, or take an image and try and identify what’s in the image, or take language and attempt to understand what its meaning is, or take a conversation and participate in that.
First of all, what we’re talking about now are a set of services, each of which do something very specific, each of which are trying to deal with a different part of our human experience, and with the idea that anybody building an application, anybody that wants to solve a social or consumer or business problem can do that by taking our services, then composing that into an application. That’s point one.
Point two is the one that you started with, which is, all right, now that I’ve got the service, how do we get it to do the things we want it to do well? The technique really is one of teaching. The probabilistic nature of these systems is founded on the fact that they are based on machine learning or deep learning, and those algorithms have to be taught how to recognize the patterns that represent meaning within a set of signals, which you do by providing data, data that represents examples of that situation that you’ve had before where you’ve been able to label that as saying, “When I hear that combination of sounds, it means this word. When I see this combination of pixels, it means that object.” When I had those examples, I can now bring you to the cognitive system, to these cognitive services, and teach them how to do a better job of recognizing whatever it is that we want it to do.
I think one of the examples that illustrates this really well is in the medical space, where Watson is helping doctors make decisions and parsing large quantities of data, but then ultimately working with them on a diagnosis in partnership. Can you talk a little bit about how that training takes place and then how the solution winds up delivering better outcomes?
The work that we’ve done in oncology is a good example of where really it’s a composition of multiple different kinds of algorithms that, across the spectrum of work that needs to be performed, are used in different ways. We start with, for example, looking at the medical record, looking at your medical record and using the cognitive system to look over all the notes that the clinicians have taken over the years that they’ve been working with you and finding what we call pertinent clinical information. What is the information in those medical notes that are now relevant to the consultation that you’re about to go into? Taking that, doing population similarity analytics, trying to find the other patients, the other cohorts that have a lot of similarity to you, because that’s going to inform the doctor on how to think about different treatments and how those treatments might be appropriate for you and how you’re going to react to those treatments.
Then we go into what we call the standard of care practices, which are relatively well-defined techniques that doctors share on how they’re going to treat different patients for different kinds of diseases, recognizing that those are really designed for the average person. Then we lay on top of that what we call clinical expertise. Having been taught by the best doctors in different diseases what to look for and where the outliers are and how to reason about the different standard of care practices, which of those is most appropriate or how to take the different pathways through those different care practices and now apply them in the best way possible, but finally going in and looking at the clinical literature, all the hundreds of thousands, 600,000 articles in PubMed about the advances in science that have occurred in that field that are relevant to now making this treatment recommendation.
All those are different aspects of algorithms that we’re applying at different phases of that process, all of which have been taught by putting some of the best doctors in the world in front of these systems and having them use the system and correct the system when they see something going wrong, and having the system learn essentially through that use on how to improve its own performance. We’re using that specifically in the case of oncology to help inform doctors in the field about treatment options that they may not be familiar with, or even if they have some familiarity with it may not have had any real experience with and don’t really understand how their patients are going to respond to it and how to get the most effective response from their patients.
What that basically has done is democratized the expertise. We can take the best doctors at Memorial Sloan Kettering who had the benefit of seeing literally thousands of patients a year around the same disease from which they’ve developed this tremendous expertise, capture that in the cognitive system, bring that out to a community or regional clinic setting where those doctors may not have had as much time working with the same disease across a large number of different patients, giving them the opportunity to benefit from that expertise that’s now been captured in the cognitive system.
I think that idea of distributing that expertise, first of all, capturing it is a non-trivial task, but then once you’ve done that, being able to distribute it really across the planet, you’re going to have the expertise of the best doctors at Memorial Sloan Kettering being able to be delivered in China, in India, in small clinics, and I think that’s pretty extraordinary.
It has a tremendous social impact on our welfare, on our health, on the things that will benefit us as a society.
On the flip side, the thing that concerns people about artificial intelligence is that it’s going to replace people, it’s going to replace jobs. It’s tied into the automation movement. The thing that strikes me is, staying in the medical space, radiologists. Radiologists look at hundreds and hundreds of slides a day. Watson or an AI-based system could replicate that same type of diagnosis and image analysis. Ten years from now, do you think there are going to be more or fewer human radiologists employed in the US? What’s the impact on industries like that?
The impact is actually about helping people do a better job. It’s really about … take it in the case of the doctor. If the doctor can now make decisions that are more informed, that are based on real evidence, that are supported by the latest facts in science, that are more tailored and specific to the individual patient, it allows them to actually do their job better. For radiologists, it may allow them to see things in the image that they might otherwise miss or get overwhelmed by. It’s not about replacing them. It’s about helping them do their job better.
It does have some of the same dynamic that every tool that we’ve ever created in society. I like to say if you go back and look at the last 10,000 years of modern society since the advent of the agricultural revolution, we’ve been as a human society building tools, hammers, shovels, hydraulics, pulleys, levers, and a lot of these tools have been most durable when what they’re really doing is amplifying human beings, amplifying our strength, amplifying our thinking, amplifying our reach.
That’s really the way to think about this stuff, is that it will have its greatest utility when it is allowing us to do what we do better than we could by ourselves, when the combination of the human and the tool together are greater than either one of them would’ve been by theirselves. That’s really the way we think about it. That’s how we’re evolving the technology. That’s where the economic utility is going to be.
I completely agree, but I do think there’s going to be industries that are obviated because of the efficiency introduced by these intelligent systems.
They’re going to be transitioned. Yeah, they’re going to be transitioned. I don’t want to diminish that point by saying it this way, but I also want to be sure that we aren’t thinking about this as the elimination of jobs. This is about transforming the jobs that people perform. I’ll give you an example. A lot…
Author: Todd Spangler / Source: Variety
In the newly created position, Dugan will manage all aspects of Hearst Magazines Digital Media technical organization around the world. He reports to HMDM global president Troy Young.
“Mike is a seasoned leader with a proven track record in the media tech space,” said Young. “His knowledge and expertise will be invaluable as we continue to develop new platforms and systems that leverage our understanding of…
MONTREAL, QUEBEC, Aug 14, 2017 (Marketwired via COMTEX) — MONTREAL, QUEBEC–(Marketwired – Aug. 14, 2017) – Geomega Resources Inc. (“GéoMégA” or the “Corporation”) (GMA) is pleased to announce that the Corporation and Innord entered into a patent ownership and royalty agreement with its Chief Technology Officer, Dr. Pouya Hajiani, to insure the long-term development and commercialization of the Corporation’s proprietary rare earths extraction and separation technologies (the “Agreement”). In addition, the Corporation announces that it has closed a first tranche of $235,000 of a non-brokered private placement (the “Offering”) of units (the “Units”). Each Unit is comprised of one unsecured convertible debenture (each a “Convertible Debenture”) in the principal amount of $1,000 and 5,000 common share purchase warrants (the “Warrants”).
The Convertible Debentures have a two (2) year maturity date (the “Maturity Date”) and bear an interest of 10% per annum, compounded quarterly and payable quarterly in arrears. The Corporation has the option to pay such interest by delivering such number of common shares of the Corporation (the “Common Shares”) as may be required, at an issue price per share based upon the 20-day volume weighted average price (“VWAP”) of the Corporation’s common shares on the TSX Venture Exchange (“TSXV”) on the due date of the quarterly interest payment. Any such interest payment in Common Shares shall be subject to the approval of the TSXV.
Each Warrant will entitle the holder to purchase one Common Share (each a “Warrant Share”) at a price of $0.10 per Warrant Share for a period of twelve (12) months from the closing of the Offering and thereafter at a price of $0.12 per Warrant Share until the date which is twenty-four (24) months from the closing of the Offering.
The Convertible Debentures will be convertible into Common Shares at the option of the holder at any time prior to the close of business on the Maturity Date based on the following conversion price, subject to adjustment in certain events (the “Conversion Price”): (i) at a price of $0.10 per Common Share if converted during the period of twelve (12) months from the closing of the Offering; and (ii) at a price of $0.12 per Common Share if converted during period following the twelve month (12) anniversary of the closing of the Offering until the date which is twenty-four (24) months from the closing of the Offering.
The Convertible Debentures will be subject to redemption, in whole or in part, by the Corporation should the Corporation realize gross proceeds from a subsequent private placement of securities or as a result of the exercise of the Warrants in an amount equal to the gross proceeds of the Offering at any time following the closing of the Offering upon giving the holders of the Convertible Debentures not less than 30 and not more than 60 days’ prior written notice, at a price equal to the then outstanding principal amount of the Convertible Debentures plus all accrued and unpaid interest up to and including the redemption date plus a redemption premium as follows: (i) 10% during the first six (6) months following the closing of the Offering; (ii) 5% from the six (6) month anniversary of the closing of the Offering to the twelve month (12) anniversary following the closing of the Offering; (iii) 3% following the twelve month (12) anniversary following the closing of the Offering until the Maturity Date. A holder of Convertible Debentures may elect to convert its Convertible Debentures by providing the Corporation with a written notice to that effect within five (5) business days of the receipt by the holder of the redemption notice.
Certain members of the board and executive management of the Corporation, being Gilles Gingras, a director of the Corporation, Kiril Mugerman, the President and Chief Executive Officer, and Ingrid Martin, the Chief Financial Officer, have participated in this first closing of the Offering in the aggregate amount of $60,000 (the “Insiders’ Participation”). The Insiders’ Participation is considered a “related party transaction” under Regulation 61-101 respecting Protection of Minority Security Holders in Special Transactions (Québec) (“Regulation 61-101”) and the corresponding Policy 5.9 of the TSXV; however, the Insiders’ Participation is exempt from the formal valuation and minority shareholder approval requirements provided under Regulation 61-101 in accordance with sections 5.5(a) and 5.7(1)(a) of said Regulation 61-101. The exemption is based on the fact that neither the market value of the Insiders’ Participation nor the consideration paid therefor exceeds 25% of the Corporation’s market capitalization. The Corporation did not file a material change report at least 21 days prior to the first closing of the Offering since the Insiders’ Participation was not determined at that moment and the Corporation wished to close the Offering on an expedited basis for sound business reasons.
The Corporation intends to use the proceeds from this Offering to advance the ongoing research and development work being undertaken by Dr. Hajiani and Innord, to secure additional Provincial and Federal supported funding for the Corporation’s and Innord’s objectives and for ongoing working capital needs.
The securities issued in connection with the Offering will be subject to a four-month hold period, in accordance with applicable securities laws.
The Patent Ownership and Royalty Agreement
The Agreement will replace the 2013 agreement (see January 14, 2014 press release) that granted Dr. Hajiani 1,000,000 common…
Author: Jeff Kagan / Source: E-Commerce Times
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There has been quite a bit of churn in the waters around Uber during the last few years. Now that founder Travis Kalanick is no longer CEO, what does the future look like for the company, workers, drivers, investors and customers? Will Uber continue to grow and lead, or has it seen its best days?
Most people connected to the company probably would say the same thing: They want calm but rapid growth. There is now a good possibility for growth if they get the right CEO and right strategy. However, Uber has been like two different companies in recent years.
On one hand, it created a new space — that is its biggest accomplishment. Whether Uber lasts or not, the space will, and that can be traced to Uber’s use of wireless and smartphone technology, like the wireless data network and the wireless Internet.
On the other hand, behavior of certain key people has not been good. Issue after issue — and there were quite a few — kept them in the headlines in a negative way, and that was a drag on the company. The company was like someone with behavioral problems. In one respect it was a superhero — but in another, was miserable and very troubled.
The Right Chief
Uber’s future depends on who it brings in as CEO to run the company and build it going forward. Can…