4 items tagged "research "

  • 7 Tips to integrate new research technologies in your organization

    7 Tips to integrate new research technologies in your organization

    The pandemic has accelerated the adoption of emerging research technologies. However, it's important to consider not just your current needs but also what these technologies actually do. Here are seven key tips for brands and agencies to consider when adopting new ResTech.

    ResTech is booming amid the pandemic. Recognising that they need ongoing insights at a time of overwhelming uncertainty and change, brands are investing in tech to get a real-time and accurate understanding of their customers.

    In today’s unpredictable landscape, using tech is no longer optional in research. And in many projects, emerging tech can play an important role in driving business outcomes.

    How ResTech is used alongside traditional techniques

    Let me demonstrate with an example. A global beverage brand approached us recently to get a more nuanced understanding of the desirability of its drinks. Pre-pandemic, the obvious techniques would have been in-person ethnography and in-person interviews and focus groups – techniques that are slow and time-consuming but appropriate for this type of work.

    New tech bridged the gap. We recruited hundreds of consumers for in-home missions and used the Rival Technologies platform to get quantitative and qualitative responses on their behaviours and attitudes. After gathering hundreds of videos in just a few days, we used facial coding software and voice tonal analysis to capture sentiment beyond what consumers said. Select participants joined us for virtual in-depth interviews, with real-time transcription software and text analytics aiding the analysis. The project involved five languages in seven countries and was executed from end to end in just a few weeks.

    This example illustrates the need to use mixed methodologies and numerous platforms to get a richer and more complete understanding of people’s attitudes, behaviours, and underlying motivations. Through our experience, we know that maximising the ROI of emerging research technologies is more of an art than a science.

    Seven strategies for integrating ResTech

    Below, find a few key learnings to consider.

    1. Purpose-fit the tech you are considering.

    Tech is never a complete replacement for a traditional research method. Don’t try to force a technology just because it’s cool. Business and research objectives should take precedence. Tech will do some parts of your research project better than traditional approaches, but it will also do other things poorly. Don’t compromise data quality for the sake of innovation.

    2. Prepare for a conversation.

    Tech companies care about the length of the relationship. Their business model is built around yearly subscriptions.

    On the other hand, agencies (and even client-side teams) think of tech on a project-by-project basis. Research teams care about sample size and operational costs, which are a lower focus for a software company. This puts the tech vendors and researchers at cross purposes.

    There’s no single way of doing this correctly, but understanding the tech company’s model and explaining what you’re after is a good first step.

    3. Find the right balance.

    Established vendors are safe bets, but their solutions operate within a box – customisations are often time-consuming and expensive, if at all possible.

    Newer players have more flexibility and can deliver value in surprising ways. Set aside budget and time to play with emerging methodologies because this is where innovation happens.

    That said, aim to have traditional methods as a fallback. Some Reach3 clients, for example, have done parallel studies using Rival and existing vendors to understand the differences between the two. Eventually, brands discover that the new technology delivers deeper, richer insights, but having the traditional tech available provides peace of mind in the early stages.

    4. Get support.

    DIY has its place and time, but if you’re using new tech, you need to make sure the vendor has invested enough in customer success. No matter how great the technology is, if it’s new in the market, you’ll likely run into unforeseen issues. Having solid operations and customer success teams that can help bring your creative ideas to life with their tech is crucial.

    5. Budget appropriately.

    Traditionally, research projects are costed with treating all costs as direct costs and then a services cost is overlayed on top to arrive at a profit. Tech is not a direct cost – it’s an enabler that helps you do something exponentially. As an agency, if you use the traditional costing model, you will price yourself out of the running.

    While your first project will have a steep learning curve, over time there will be efficiencies and eventually, tech will save time and money.

    6. Be clear about liability.

    When working with customer data, be cognisant of who will take on the liability of the tech. Typically, the relationship is between the service provider (tech company) and the client (who owns/is responsible for the customer data). The research agency is not a natural fit in the mix. If you’re on the agency side, engage your client, the tech vendor, and your legal counsel early to navigate this discussion more smoothly.

    7. Experiment with care.

    You will occasionally come across a piece of tech that is amazing at doing something specific, which may seem like an exciting add-on to a research methodology. Be cautious when doing this. Unless you have proper dev support, expanding the application of the tech is a more gargantuan task than it might seem. Doing constrained, smaller experiments can help you validate new use cases without taking on a huge burden.

    Maximizing the value of new tech

    Deploying innovative research tech should never be scary or disruptive. If you’re a corporate researcher, the easiest way to test new techniques is to work with agencies who are already experts in new technologies you’re interested in.

    In the end, ResTech is best deployed by researchers who understand the business issue and know how to integrate technology in a fashion that automates and accelerates insights-generation so humans can do what they do best: use insights to create game-changing business recommendations.

    Author: Bala Rajan

    Source: GreenBook

  • Exploring the risks of artificial intelligence

    shutterstock 117756049“Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.”

    These words, articulated by Neil Armstrong at a speech to a joint session of Congress in 1969, fit squarely into most every decade since the turn of the century, and it seems to safe to posit that the rate of change in technology has accelerated to an exponential degree in the last two decades, especially in the areas of artificial intelligence and machine learning.

    Artificial intelligence is making an extreme entrance into almost every facet of society in predicted and unforeseen ways, causing both excitement and trepidation. This reaction alone is predictable, but can we really predict the associated risks involved?

    It seems we’re all trying to get a grip on potential reality, but information overload (yet another side affect that we’re struggling to deal with in our digital world) can ironically make constructing an informed opinion more challenging than ever. In the search for some semblance of truth, it can help to turn to those in the trenches.

    In my continued interview with over 30 artificial intelligence researchers, I asked what they considered to be the most likely risk of artificial intelligence in the next 20 years.

    Some results from the survey, shown in the graphic below, included 33 responses from different AI/cognitive science researchers. (For the complete collection of interviews, and more information on all of our 40+ respondents, visit the original interactive infographic here on TechEmergence).

    Two “greatest” risks bubbled to the top of the response pool (and the majority are not in the autonomous robots’ camp, though a few do fall into this one). According to this particular set of minds, the most pressing short- and long-term risks is the financial and economic harm that may be wrought, as well as mismanagement of AI by human beings.

    Dr. Joscha Bach of the MIT Media Lab and Harvard Program for Evolutionary Dynamics summed up the larger picture this way:

    “The risks brought about by near-term AI may turn out to be the same risks that are already inherent in our society. Automation through AI will increase productivity, but won’t improve our living conditions if we don’t move away from a labor/wage based economy. It may also speed up pollution and resource exhaustion, if we don’t manage to install meaningful regulations. Even in the long run, making AI safe for humanity may turn out to be the same as making our society safe for humanity.”

    Essentially, the introduction of AI may act as a catalyst that exposes and speeds up the imperfections already present in our society. Without a conscious and collaborative plan to move forward, we expose society to a range of risks, from bigger gaps in wealth distribution to negative environmental effects.

    Leaps in AI are already being made in the area of workplace automation and machine learning capabilities are quickly extending to our energy and other enterprise applications, including mobile and automotive. The next industrial revolution may be the last one that humans usher in by their own direct doing, with AI as a future collaborator and – dare we say – a potential leader.

    Some researchers believe it’s a matter of when and not if. In Dr. Nils Nilsson’s words, a professor emeritus at Stanford University, “Machines will be singing the song, ‘Anything you can do, I can do better; I can do anything better than you’.”

    In respect to the drastic changes that lie ahead for the employment market due to increasingly autonomous systems, Dr. Helgi Helgason says, “it’s more of a certainty than a risk and we should already be factoring this into education policies.”

    Talks at the World Economic Forum Annual Meeting in Switzerland this past January, where the topic of the economic disruption brought about by AI was clearly a main course, indicate that global leaders are starting to plan how to integrate these technologies and adapt our world economies accordingly – but this is a tall order with many cooks in the kitchen.

    Another commonly expressed risk over the next two decades is the general mismanagement of AI. It’s no secret that those in the business of AI have concerns, as evidenced by the $1 billion investment made by some of Silicon Valley’s top tech gurus to support OpenAI, a non-profit research group with a focus on exploring the positive human impact of AI technologies.

    “It’s hard to fathom how much human-level AI could benefit society, and it’s equally hard to imagine how much it could damage society if built or used incorrectly,” is the parallel message posted on OpenAI’s launch page from December 2015. How we approach the development and management of AI has far-reaching consequences, and shapes future society’s moral and ethical paradigm.

    Philippe Pasquier, an associate professor at Simon Fraser University, said “As we deploy more and give more responsibilities to artificial agents, risks of malfunction that have negative consequences are increasing,” though he likewise states that he does not believe AI poses a high risk to society on its own.

    With great responsibility comes great power, and how we monitor this power is of major concern.

    Dr. Pei Wang of Temple University sees major risk in “neglecting the limitations and restrictions of hot techniques like deep learning and reinforcement learning. It can happen in many domains.” Dr. Peter Voss, founder of SmartAction, expressed similar sentiments, stating that he most fears “ignorant humans subverting the power and intelligence of AI.”

    Thinking about the risks associated with emerging AI technology is hard work, engineering potential solutions and safeguards is harder work, and collaborating globally on implementation and monitoring of initiatives is the hardest work of all. But considering all that’s at stake, I would place all my bets on the table and argue that the effort is worth the risk many times over.

    Source: Tech Crunch

  • Is the first autonomous aircraft with an AI pilot in the making?

    Is the first autonomous aircraft with an AI pilot in the making?

    A team of researchers at Carnegie Mellon University believe they have developed the first AI pilot that enables autonomous aircraft to navigate crowded airspace.

    AI pilot could eventually pass the turing test

    Artificial intelligence can safely avoid collisions, predict the intent of other aircraft, track aircraft and coordinate with their actions, and communicate over the radio with pilots and air traffic controllers. The researchers aim to develop the AI so the behaviors of their system will be indistinguishable from those of a human pilot.

    Jean Oh, an associate research professor at CMU’s Robotics Institute (RI) and a part of the AI pilot team, stated, “We believe we could eventually pass the Turing Test,” alluding to the test of an AI’s capacity to demonstrate intelligent behavior comparable to a human.

    The AI employs both vision and natural language to convey its intent with other aircraft, whether flown or not, to engage with them as a human pilot would. To navigate safely and under social norms, adopt this conduct. Researchers trained the AI on data gathered at the Allegheny County Airport and the Pittsburgh-Butler Regional Airport, which included air traffic patterns, photographs of planes, and radio broadcasts, to achieve this implicit coordination.

    Similar to a human pilot, the AI employs six cameras and a computer vision system to identify surrounding planes. Its automatic voice recognition feature uses NLP methods to comprehend incoming radio transmissions and speak verbally with pilots and air traffic controllers.

    The development of autonomous aircraft will increase the possibilities for drones, air taxis, helicopters, and other aircraft to operate, often without a pilot at the controls, moving people and goods, inspecting infrastructure, treating fields to protect crops, and monitoring for poaching or deforestation. However, the area where these aircraft must fly is already congested with small aircraft, medical helicopters, and other aircraft.

    The FAA and NASA have suggested segmenting this urban airspace into lanes or corridors with limitations on the times, types, and numbers of aircraft permitted to use them. This could lead to air traffic bottlenecks prohibiting essential aircraft, such as medical evacuation helicopters, from reaching their destinations. It would fundamentally alter how this airspace is now used and generally operated.

    The aerospace industry has faced challenges in developing an AI to handle the frequently congested and pilot-controlled lower-altitude traffic operating under visual flight rules (VFR), even though autopilot controls are common among commercial airliners and other aircraft operating in higher altitudes under instrument flight rules (IFR). The AI used by the team is built to interact with airplanes in VFR airspace with ease.

    “This is the first AI pilot that works in the current airspace. I don’t see that airspace changing for UAVs. The UAVs will have to change for the airspace,” explained Sebastian Scherer, an associate research professor in the RI and a member of the team.

    The AI pilot has done well in flight simulators, but the company has not yet tested it on actual planes. The team sets up two flight simulators to evaluate the AI. The AI controls one, while a person is in charge of the other. They both use the same airspace. Even if the pilot at the controls lacks experience, the AI can safely maneuver around the controlled aircraft.

    Commercially, AI could assist autonomous aircraft in carrying passengers and delivering products. To reduce weight and protect themselves against a pilot shortage, delivery drones and air taxis would ideally not fly with a pilot on board.

    The project’s lead researcher, Jay Patrikar, a Ph.D. candidate at the RI, stated, “We need more pilots, and AI can help.”

    Author: Kerem Gülen

    Source: Dataconomy

  • The rise of Online Communities as the centre of market research

    The rise of Online Communities as the centre of market research

    Once believed to be a minor player, online communities have become a central pillar of an effective research strategy now that consumers, governments, and companies rely on virtual interactions.

    Online Research Communities have been growing in importance as a central pillar of an effective research strategy for many years due to the benefits they unlock: centralized research management, ongoing engagement with important constituencies, flexibility of methodology deployment, and ROI to name just a few. However, prior to 2020, they were not necessarily strategically mission-critical.

    Like so much in our world, the impact of Covid-19 has changed that.

    As the crisis unfolded in Q1/Q2, we saw several dramatic changes that impacted research organizations rapidly:

    1. Communication became almost entirely digitally centric, with face-to-face interactions curtailed virtually to the point of complete cessation for large portions of the population.
    2. Consumer behaviors, values, and planning began to shift in response to new realities. Maslow’s Hierarchy was validated once again as many people were focused on safety and security as primary motivators in a way unseen outside of wartime.
    3. Brands, governments, NGOs, and all companies who serve them were under immense pressure to urgently engage and understand these changes in both immediate and long-term contexts.
    4. Budget pressures increased for many buyers of research, with some under extreme duress, while their information needs were only increasing.
    5. Social and emotional impacts for many people were extreme, resulting in a newfound willingness, even need, to connect with others including researchers.

    Those are just the most obvious trends, but they clearly pointed to the Online Community as a potential solution, and we saw that reflected in the business performance of the category. Recollective and virtually every company that offers solutions in the Community/Digital Qualitative space saw a massive and incredibly rapid shift to their platforms in response to new market dynamics. This shift has also spurred a new surge of innovation as supplier companies rise to the occasion to meet the evolving needs of users.

    Online Communities are here to stay

    For those of us that have been advocates of communities and virtual qualitative for many years, this shift made perfect sense, as previously outlined herein. However, the question before us now is whether this was a short-term reaction or a long-term strategic shift? Certainly, it started as the former, but I believe it is now the latter. Covid-19 has been the impetus for a “tipping point”, and there is no going back now. The key stakeholder groups have adapted to what truly is the “new normal” in the research world:

    • Buyers of research have been convinced that they can successfully duplicate the information needs of qualitative research in an online environment while saving the expense and liability issues associated with face-to-face research. Unless the research requires some level of sensory input (touch, taste, smell) or has an experiential component (car clinics, shop alongs, etc..) virtual qual is here to stay and will be the new majority use method.
    • Consumers are now comfortable engaging via video for almost all aspects of information sharing and allowing others into their lives via video. The ubiquity of cameras in a myriad of devices combined with continual enhancements to internet bandwidth makes the barrier to usage minimal in most of the developed world, and with the scaling of internet satellite systems, soon the whole world.
    • Users of research now know they can get information needs met quickly via digital channels and can cost-effectively build long-term engagement channels with constituent groups relatively easily. They are seeing the ROI of insights, especially proprietary communities and panels, and will continue to support them.

    With all these factors in mind, I think it is clear now that we will continue to see the large-scale adoption of online communities as one of the central pillars of research operations, and the development of further innovations to increase cost and speed efficiencies while empowering greater quality and impact of insights. I wish it hadn’t taken a global pandemic and all the negative aspects of this situation for so many to get here, but Necessity is the Mother of Invention and I am grateful companies like Recollective and their peers were here to help make the transition as easy as possible. The world has changed, but Online Research Communities and all the great benefits they provide are here to stay.

    Author: Leonard Murphy

    Source: Greenbook

EasyTagCloud v2.8