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#437357 Algorithms Workers Can’t See Are ...

“I’m sorry, Dave. I’m afraid I can’t do that.” HAL’s cold, if polite, refusal to open the pod bay doors in 2001: A Space Odyssey has become a defining warning about putting too much trust in artificial intelligence, particularly if you work in space.

In the movies, when a machine decides to be the boss (or humans let it) things go wrong. Yet despite myriad dystopian warnings, control by machines is fast becoming our reality.

Algorithms—sets of instructions to solve a problem or complete a task—now drive everything from browser search results to better medical care.

They are helping design buildings. They are speeding up trading on financial markets, making and losing fortunes in micro-seconds. They are calculating the most efficient routes for delivery drivers.

In the workplace, self-learning algorithmic computer systems are being introduced by companies to assist in areas such as hiring, setting tasks, measuring productivity, evaluating performance, and even terminating employment: “I’m sorry, Dave. I’m afraid you are being made redundant.”

Giving self‐learning algorithms the responsibility to make and execute decisions affecting workers is called “algorithmic management.” It carries a host of risks in depersonalizing management systems and entrenching pre-existing biases.

At an even deeper level, perhaps, algorithmic management entrenches a power imbalance between management and worker. Algorithms are closely guarded secrets. Their decision-making processes are hidden. It’s a black-box: perhaps you have some understanding of the data that went in, and you see the result that comes out, but you have no idea of what goes on in between.

Algorithms at Work
Here are a few examples of algorithms already at work.

At Amazon’s fulfillment center in south-east Melbourne, they set the pace for “pickers,” who have timers on their scanners showing how long they have to find the next item. As soon as they scan that item, the timer resets for the next. All at a “not quite walking, not quite running” speed.

Or how about AI determining your success in a job interview? More than 700 companies have trialed such technology. US developer HireVue says its software speeds up the hiring process by 90 percent by having applicants answer identical questions and then scoring them according to language, tone, and facial expressions.

Granted, human assessments during job interviews are notoriously flawed. Algorithms,however, can also be biased. The classic example is the COMPAS software used by US judges, probation, and parole officers to rate a person’s risk of re-offending. In 2016 a ProPublica investigation showed the algorithm was heavily discriminatory, incorrectly classifying black subjects as higher risk 45 percent of the time, compared with 23 percent for white subjects.

How Gig Workers Cope
Algorithms do what their code tells them to do. The problem is this code is rarely available. This makes them difficult to scrutinize, or even understand.

Nowhere is this more evident than in the gig economy. Uber, Lyft, Deliveroo, and other platforms could not exist without algorithms allocating, monitoring, evaluating, and rewarding work.

Over the past year Uber Eats’ bicycle couriers and drivers, for instance, have blamed unexplained changes to the algorithm for slashing their jobs, and incomes.

Rider’s can’t be 100 percent sure it was all down to the algorithm. But that’s part of the problem. The fact those who depend on the algorithm don’t know one way or the other has a powerful influence on them.

This is a key result from our interviews with 58 food-delivery couriers. Most knew their jobs were allocated by an algorithm (via an app). They knew the app collected data. What they didn’t know was how data was used to award them work.

In response, they developed a range of strategies (or guessed how) to “win” more jobs, such as accepting gigs as quickly as possible and waiting in “magic” locations. Ironically, these attempts to please the algorithm often meant losing the very flexibility that was one of the attractions of gig work.

The information asymmetry created by algorithmic management has two profound effects. First, it threatens to entrench systemic biases, the type of discrimination hidden within the COMPAS algorithm for years. Second, it compounds the power imbalance between management and worker.

Our data also confirmed others’ findings that it is almost impossible to complain about the decisions of the algorithm. Workers often do not know the exact basis of those decisions, and there’s no one to complain to anyway. When Uber Eats bicycle couriers asked for reasons about their plummeting income, for example, responses from the company advised them “we have no manual control over how many deliveries you receive.”

Broader Lessons
When algorithmic management operates as a “black box” one of the consequences is that it is can become an indirect control mechanism. Thus far under-appreciated by Australian regulators, this control mechanism has enabled platforms to mobilize a reliable and scalable workforce while avoiding employer responsibilities.

“The absence of concrete evidence about how the algorithms operate”, the Victorian government’s inquiry into the “on-demand” workforce notes in its report, “makes it hard for a driver or rider to complain if they feel disadvantaged by one.”

The report, published in June, also found it is “hard to confirm if concern over algorithm transparency is real.”

But it is precisely the fact it is hard to confirm that’s the problem. How can we start to even identify, let alone resolve, issues like algorithmic management?

Fair conduct standards to ensure transparency and accountability are a start. One example is the Fair Work initiative, led by the Oxford Internet Institute. The initiative is bringing together researchers with platforms, workers, unions, and regulators to develop global principles for work in the platform economy. This includes “fair management,” which focuses on how transparent the results and outcomes of algorithms are for workers.

Understandings about impact of algorithms on all forms of work is still in its infancy. It demands greater scrutiny and research. Without human oversight based on agreed principles we risk inviting HAL into our workplaces.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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#437301 The Global Work Crisis: Automation, the ...

The alarm bell rings. You open your eyes, come to your senses, and slide from dream state to consciousness. You hit the snooze button, and eventually crawl out of bed to the start of yet another working day.

This daily narrative is experienced by billions of people all over the world. We work, we eat, we sleep, and we repeat. As our lives pass day by day, the beating drums of the weekly routine take over and years pass until we reach our goal of retirement.

A Crisis of Work
We repeat the routine so that we can pay our bills, set our kids up for success, and provide for our families. And after a while, we start to forget what we would do with our lives if we didn’t have to go back to work.

In the end, we look back at our careers and reflect on what we’ve achieved. It may have been the hundreds of human interactions we’ve had; the thousands of emails read and replied to; the millions of minutes of physical labor—all to keep the global economy ticking along.

According to Gallup’s World Poll, only 15 percent of people worldwide are actually engaged with their jobs. The current state of “work” is not working for most people. In fact, it seems we as a species are trapped by a global work crisis, which condemns people to cast away their time just to get by in their day-to-day lives.

Technologies like artificial intelligence and automation may help relieve the work burdens of millions of people—but to benefit from their impact, we need to start changing our social structures and the way we think about work now.

The Specter of Automation
Automation has been ongoing since the Industrial Revolution. In recent decades it has taken on a more elegant guise, first with physical robots in production plants, and more recently with software automation entering most offices.

The driving goal behind much of this automation has always been productivity and hence, profits: technology that can act as a multiplier on what a single human can achieve in a day is of huge value to any company. Powered by this strong financial incentive, the quest for automation is growing ever more pervasive.

But if automation accelerates or even continues at its current pace and there aren’t strong social safety nets in place to catch the people who are negatively impacted (such as by losing their jobs), there could be a host of knock-on effects, including more concentrated wealth among a shrinking elite, more strain on government social support, an increase in depression and drug dependence, and even violent social unrest.

It seems as though we are rushing headlong into a major crisis, driven by the engine of accelerating automation. But what if instead of automation challenging our fragile status quo, we view it as the solution that can free us from the shackles of the Work Crisis?

The Way Out
In order to undertake this paradigm shift, we need to consider what society could potentially look like, as well as the problems associated with making this change. In the context of these crises, our primary aim should be for a system where people are not obligated to work to generate the means to survive. This removal of work should not threaten access to food, water, shelter, education, healthcare, energy, or human value. In our current system, work is the gatekeeper to these essentials: one can only access these (and even then often in a limited form), if one has a “job” that affords them.

Changing this system is thus a monumental task. This comes with two primary challenges: providing people without jobs with financial security, and ensuring they maintain a sense of their human value and worth. There are several measures that could be implemented to help meet these challenges, each with important steps for society to consider.

Universal basic income (UBI)

UBI is rapidly gaining support, and it would allow people to become shareholders in the fruits of automation, which would then be distributed more broadly.

UBI trials have been conducted in various countries around the world, including Finland, Kenya, and Spain. The findings have generally been positive on the health and well-being of the participants, and showed no evidence that UBI disincentivizes work, a common concern among the idea’s critics. The most recent popular voice for UBI has been that of former US presidential candidate Andrew Yang, who now runs a non-profit called Humanity Forward.

UBI could also remove wasteful bureaucracy in administering welfare payments (since everyone receives the same amount, there’s no need to prevent false claims), and promote the pursuit of projects aligned with peoples’ skill sets and passions, as well as quantifying the value of tasks not recognized by economic measures like Gross Domestic Product (GDP). This includes looking after children and the elderly at home.

How a UBI can be initiated with political will and social backing and paid for by governments has been hotly debated by economists and UBI enthusiasts. Variables like how much the UBI payments should be, whether to implement taxes such as Yang’s proposed valued added tax (VAT), whether to replace existing welfare payments, the impact on inflation, and the impact on “jobs” from people who would otherwise look for work require additional discussion. However, some have predicted the inevitability of UBI as a result of automation.

Universal healthcare

Another major component of any society is the healthcare of its citizens. A move away from work would further require the implementation of a universal healthcare system to decouple healthcare from jobs. Currently in the US, and indeed many other economies, healthcare is tied to employment.

Universal healthcare such as Medicare in Australia is evidence for the adage “prevention is better than cure,” when comparing the cost of healthcare in the US with Australia on a per capita basis. This has already presented itself as an advancement in the way healthcare is considered. There are further benefits of a healthier population, including less time and money spent on “sick-care.” Healthy people are more likely and more able to achieve their full potential.

Reshape the economy away from work-based value

One of the greatest challenges in a departure from work is for people to find value elsewhere in life. Many people view their identities as being inextricably tied to their jobs, and life without a job is therefore a threat to one’s sense of existence. This presents a shift that must be made at both a societal and personal level.

A person can only seek alternate value in life when afforded the time to do so. To this end, we need to start reducing “work-for-a-living” hours towards zero, which is a trend we are already seeing in Europe. This should not come at the cost of reducing wages pro rata, but rather could be complemented by UBI or additional schemes where people receive dividends for work done by automation. This transition makes even more sense when coupled with the idea of deviating from using GDP as a measure of societal growth, and instead adopting a well-being index based on universal human values like health, community, happiness, and peace.

The crux of this issue is in transitioning away from the view that work gives life meaning and life is about using work to survive, towards a view of living a life that itself is fulfilling and meaningful. This speaks directly to notions from Maslow’s hierarchy of needs, where work largely addresses psychological and safety needs such as shelter, food, and financial well-being. More people should have a chance to grow beyond the most basic needs and engage in self-actualization and transcendence.

The question is largely around what would provide people with a sense of value, and the answers would differ as much as people do; self-mastery, building relationships and contributing to community growth, fostering creativity, and even engaging in the enjoyable aspects of existing jobs could all come into play.

Universal education

With a move towards a society that promotes the values of living a good life, the education system would have to evolve as well. Researchers have long argued for a more nimble education system, but universities and even most online courses currently exist for the dominant purpose of ensuring people are adequately skilled to contribute to the economy. These “job factories” only exacerbate the Work Crisis. In fact, the response often given by educational institutions to the challenge posed by automation is to find new ways of upskilling students, such as ensuring they are all able to code. As alluded to earlier, this is a limited and unimaginative solution to the problem we are facing.

Instead, education should be centered on helping people acknowledge the current crisis of work and automation, teach them how to derive value that is decoupled from work, and enable people to embrace progress as we transition to the new economy.

Disrupting the Status Quo
While we seldom stop to think about it, much of the suffering faced by humanity is brought about by the systemic foe that is the Work Crisis. The way we think about work has brought society far and enabled tremendous developments, but at the same time it has failed many people. Now the status quo is threatened by those very developments as we progress to an era where machines are likely to take over many job functions.

This impending paradigm shift could be a threat to the stability of our fragile system, but only if it is not fully anticipated. If we prepare for it appropriately, it could instead be the key not just to our survival, but to a better future for all.

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#437230 How Drones and Aerial Vehicles Could ...

Drones, personal flying vehicles, and air taxis may be part of our everyday life in the very near future. Drones and air taxis will create new means of mobility and transport routes. Drones will be used for surveillance, delivery, and in the construction sector as it moves towards automation.

The introduction of these aerial craft into cities will require the built environment to change dramatically. Drones and other new aerial vehicles will require landing pads, charging points, and drone ports. They could usher in new styles of building, and lead to more sustainable design.

My research explores the impact of aerial vehicles on urban design, mapping out possible future trajectories.

An Aerial Age
Already, civilian drones can vary widely in size and complexity. They can carry a range of items from high-resolution cameras, delivery mechanisms, and thermal image technology to speakers and scanners. In the public sector, drones are used in disaster response and by the fire service to tackle fires which could endanger firefighters.

During the coronavirus pandemic, drones have been used by the police to enforce lockdown. Drones normally used in agriculture have sprayed disinfectant over cities. In the UK, drone delivery trials are taking place to carry medical items to the Isle of Wight.

Alongside drones, our future cities could also be populated by vertical takeoff and landing craft (VTOL), used as private vehicles and air taxis.

These vehicles are familiar to sci-fi fans. The late Syd Mead’s illustrations of the Spinner VTOL craft in the film Blade Runner captured the popular imagination, and the screens for the Spinners in Blade Runner 2049 created by Territory Studio provided a careful design fiction of the experience of piloting these types of vehicle.

Now, though, these flying vehicles are reality. A number of companies are developing eVTOL with electric multi-rotor jets, and a whole new motorsport is being established around them.

These aircraft have the potential to change our cities. However, they need to be tested extensively in urban airspace. A study conducted by Airbus found that public concerns about VTOL use focused on the safety of those on the ground and noise emissions.

New Cities
The widespread adoption of drones and VTOL will lead to new architecture and infrastructure. Existing buildings will require adaptations: landing pads, solar photovoltaic panels for energy efficiency, charging points for delivery drones, and landscaping to mitigate noise emissions.

A number of companies are already trialing drone delivery services. Existing buildings will need to be adapted to accommodate these new networks, and new design principles will have to be implemented in future ones.

The architect Saúl Ajuria Fernández has developed a design for a delivery drone port hub. This drone port acts like a beehive where drones recharge and collect parcels for distribution. Architectural firm Humphreys & Partners’ Pier 2, a design for a modular apartment building of the future, includes a cantilevered drone port for delivery services.

The Norman Foster Foundation has designed a drone port for delivery of medical supplies and other items for rural communities in Rwanda. The structure is also intended to function as a space for the public to congregate, as well as to receive training in robotics.

Drones may also help the urban environment become more sustainable. Researchers at the University of Stuttgart have developed a re-configurable architectural roof canopy system deployed by drones. By adjusting to follow the direction of the sun, the canopy provides shade and reduces reliance on ventilation systems.

Demand for air taxis and personal flying vehicles will develop where failures in other transport systems take place. The Airbus research found that of the cities surveyed, highest demand for VTOLs was in Los Angeles and Mexico City, urban areas famous for traffic pollution. To accommodate these aerial vehicles, urban space will need to transform to include landing pads, airport-like infrastructure, and recharge points.

Furthermore, this whole logistics system in lower airspace (below 500 feet), or what I term “hover space,” will need an urban traffic management system. One great example of how this hover space could work can be seen in a speculative project from design studio Superflux in their Drone Aviary project. A number of drones with different functions move around an urban area in a network, following different paths at varying heights.

We are at a critical period in urban history, faced by climatic breakdown and pandemic. Drones and aerial vehicles can be part of a profound rethink of the urban environment.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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#436184 Why People Demanded Privacy to Confide ...

This is part four of a six-part series on the history of natural language processing.

Between 1964 and 1966, Joseph Weizenbaum, a German American computer scientist at MIT’s artificial intelligence lab, developed the first-ever chatbot [PDF].

While there were already some rudimentary digital language generators in existence—programs that could spit out somewhat coherent lines of text—Weizenbaum’s program was the first designed explicitly for interactions with humans. The user could type in some statement or set of statements in their normal language, press enter, and receive a response from the machine. As Weizenbaum explained, his program made “certain kinds of natural-language conversation between man and computer possible.”

He named the program Eliza after Eliza Doolittle, the working-class hero of George Bernard Shaw’s Pygmalion who learns how to talk with an upper-class accent. The new Eliza was written for the 36-bit IBM 7094, an early transistorized mainframe computer, in a programming language that Weizenbaum developed called MAD-SLIP.

Because computer time was a valuable resource, Eliza could only be run via a time-sharing system; the user interacted with the program remotely via an electric typewriter and printer. When the user typed in a sentence and pressed enter, a message was sent to the mainframe computer. Eliza scanned the message for the presence of a keyword and used it in a new sentence to form a response that was sent back, printed out, and read by the user.

To encourage ongoing dialogue, Weizenbaum designed Eliza to simulate the type of conversational style used by a Rogerian psychoanalyst. The program would take something the user said and reflect it back with a question, as can be seen in the example below. (Notice how the program takes keywords like “boyfriend” and “depressed” and re-uses them.)

Human: Men are all alike.

Computer: IN WHAT WAY?

Human: They're always bugging us about something or other.

Computer: CAN YOU THINK OF A SPECIFIC EXAMPLE?

Human: Well, my boyfriend made me come here.

Computer: YOUR BOYFRIEND MADE YOU COME HERE

Human: He says I'm depressed much of the time.

Computer: I AM SORRY TO HEAR YOU ARE DEPRESSED

Human: It's true. I am unhappy.

Weizenbaum chose this mode of dialogue for Eliza because it gave the impression that the computer understood what was being said without having to offer anything new to the conversation. It created the illusion of comprehension and engagement in a mere 200 lines of code.

To test Eliza’s capacity to engage an interlocutor, Weizenbaum invited students and colleagues into his office and let them chat with the machine while he looked on. He noticed, with some concern, that during their brief interactions with Eliza, many users began forming emotional attachments to the algorithm. They would open up to the machine and confess problems they were facing in their lives and relationships.

During their brief interactions with Eliza, many users began forming emotional attachments to the algorithm.

Even more surprising was that this sense of intimacy persisted even after Weizenbaum described how the machine worked and explained that it didn’t really understand anything that was being said. Weizenbaum was most troubled when his secretary, who had watched him build the program from scratch over many months, insisted that he leave the room so she could talk to Eliza in private.

For Weizenbaum, this experiment with Eliza made him question an idea that Alan Turing had proposed in 1950 about machine intelligence. In his paper, entitled “Computing Machinery and Intelligence,” Turing suggested that if a computer could conduct a convincingly human conversation in text, one could assume it was intelligent—an idea that became the basis of the famous Turing Test.

But Eliza demonstrated that convincing communication between a human and a machine could take place even if comprehension only flowed from one side: The simulation of intelligence, rather than intelligence itself, was enough to fool people. Weizenbaum called this the Eliza effect, and believed it was a type of “delusional thinking” that humanity would collectively suffer from in the digital age. This insight was a profound shock for Weizenbaum, and one that came to define his intellectual trajectory over the next decade.

The simulation of intelligence, rather than intelligence itself, was enough to fool people.

In 1976, he published Computing Power and Human Reason: From Judgment to Calculation [PDF], which offered a long meditation on why people are willing to believe that a simple machine might be able to understand their complex human emotions.

In this book, he argues that the Eliza effect signifies a broader pathology afflicting “modern man.” In a world conquered by science, technology, and capitalism, people had grown accustomed to viewing themselves as isolated cogs in a large and uncaring machine. In such a diminished social world, Weizenbaum reasoned, people had grown so desperate for connection that they put aside their reason and judgment in order to believe that a program could care about their problems.

Weizenbaum spent the rest of his life developing this humanistic critique of artificial intelligence and digital technology. His mission was to remind people that their machines were not as smart as they were often said to be. And that even though it sometimes appeared as though they could talk, they were never really listening.

This is the fourth installment of a six-part series on the history of natural language processing. Last week’s post described Andrey Markov and Claude Shannon’s painstaking efforts to create statistical models of language for text generation. Come back next Monday for part five, “In 2016, Microsoft’s Racist Chatbot Revealed the Dangers of Conversation.”

You can also check out our prior series on the untold history of AI. Continue reading

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#436149 Blue Frog Robotics Answers (Some of) Our ...

In September of 2015, Buddy the social home robot closed its Indiegogo crowdfunding campaign more than 600 percent over its funding goal. A thousand people pledged for a robot originally scheduled to be delivered in December of 2016. But nearly three years later, the future of Buddy is still unclear. Last May, Blue Frog Robotics asked for forgiveness from its backers and announced the launch of an “equity crowdfunding campaign” to try to raise the additional funding necessary to deliver the robot in April of 2020.

By the time the crowdfunding campaign launched in August, the delivery date had slipped again, to September 2020, even as Blue Frog attempted to draw investors by estimating that sales of Buddy would “increase from 2000 robots in 2020 to 20,000 in 2023.” Blue Frog’s most recent communication with backers, in September, mentions a new CTO and a North American office, but does little to reassure backers of Buddy that they’ll ever be receiving their robot.

Backers of the robot are understandably concerned about the future of Buddy, so we sent a series of questions to the founder and CEO of Blue Frog Robotics, Rodolphe Hasselvander.

We’ve edited this interview slightly for clarity, but we should also note that Hasselvander was unable to provide answers to every question. In particular, we asked for some basic information about Blue Frog’s near-term financial plans, on which the entire future of Buddy seems to depend. We’ve left those questions in the interview anyway, along with Hasselvander’s response.

1. At this point, how much additional funding is necessary to deliver Buddy to backers?
2. Assuming funding is successful, when can backers expect to receive Buddy?
3. What happens if the fundraising goal is not met?
4. You estimate that sales of Buddy will increase 10x over three years. What is this estimate based on?

Rodolphe Hasselvander: Regarding the questions 1-4, unfortunately, as we are fundraising in a Regulation D, we do not comment on prospect, customer data, sales forecasts, or figures. Please refer to our press release here to have information about the fundraising.

5. Do you feel that you are currently being transparent enough about this process to satisfy backers?
6. Buddy’s launch date has moved from April 2020 to September 2020 over the last four months. Why should backers remain confident about Buddy’s schedule?

Since the last newsletter, we haven’t changed our communication, the backers will be the first to receive their Buddy, and we plan an official launch in September 2020.

7. What is the goal of My Buddy World?

At Blue Frog, we think that matching a great product with a big market can only happen through continual experimentation, iteration and incorporation of customer feedback. That’s why we created the forum My Buddy World. It has been designed for our Buddy Community to join us, discuss the world’s first emotional robot, and create with us. The objective is to deepen our conversation with Buddy’s fans and users, stay agile in testing our hypothesis and validate our product-market fit. We trust the value of collaboration. Behind Buddy, there is a team of roboticists, engineers, and programmers that are eager to know more about our consumers’ needs and are excited to work with them to create the perfect human/robot experience.

8. How is the current version of Buddy different from the 2015 version that backers pledged for during the successful crowdfunding campaign, in both hardware and software?

We have completely revised some parts of Buddy as well as replaced and/or added more accurate and reliable components to ensure we fully satisfy our customers’ requirements for a mature and high-quality robot from day one. We sourced more innovative components to make sure that Buddy has the most up-to-date technologies such as adding four microphones, a high def thermal matrix, a 3D camera, an 8-megapixel RGB camera, time-of-flight sensors, and touch sensors.
If you want more info, we just posted an article about what is Buddy here.

9. Will the version of Buddy that ships to backers in 2020 do everything that that was shown in the original crowdfunding video?

Concerning the capabilities of Buddy regarding the video published on YouTube, I confirm that Buddy will be able to do everything you can see, like patrol autonomously and secure your home, telepresence, mathematics applications, interactive stories for children, IoT/smart home management, face recognition, alarm clock, reminder, message/photo sharing, music, hands free call, people following, games like hide and seek (and more). In addition, everyone will be able to create their own apps thanks to the “BuddyLab” application.

10. What makes you confident that Buddy will be successful when Jibo, Kuri, and other social robots have not?

Consumer robotics is a new market. Some people think it is a tough one. But we, at Blue Frog Robotics, believe it is a path of learning, understanding, and finding new ways to serve consumers. Here are the five key factors that will make Buddy successful.

1) A market-fit robot

Blue Frog Robotics is a consumer-centric company. We know that a successful business model and a compelling fit to market Buddy must come up from solving consumers’ frustrations and problems in a way that’s new and exciting. We started from there.

By leveraged existing research and syndicated consumer data sets to understand our customers’ needs and aspirations, we get that creating a robot is not about the best tech innovation and features, but always about how well technology becomes a service to one’s basic human needs and assets: convenience, connection, security, fun, self-improvement, and time. To answer to these consumers’ needs and wants, we designed an all-in-one robot with four vital capabilities: intelligence, emotionality, mobility, and customization.

With his multi-purpose brain, he addresses a broad range of needs in modern-day life, from securing homes to carrying out his owners’ daily activities, from helping people with disabilities to educating children, from entertaining to just becoming a robot friend.

Buddy is a disruptive innovative robot that is about to transform the way we live, learn, utilize information, play, and even care about our health.
2) Endless possibilities

One of the major advantages of Buddy is his adaptability. Beyond to be adorable, playful, talkative, and to accompany anyone in their daily life at home whether you are comfortable with technology or not, he offers via his platform applications to engage his owners in a wide range of activities. From fitness to cooking, from health monitoring to education, from games to meditation, the combination of intelligence, sensors, mobility, multi-touch panel opens endless possibilities for consumers and organizations to adapt their Buddy to their own needs.
3) An affordable price

Buddy will be the first robot combining smart, social, and mobile capabilities and a developed platform with a personality to enter the U.S. market at affordable price.

Our competitors are social or assistant robots but rarely both. Competitors differentiate themselves by features: mobile, non-mobile; by shapes: humanoid or not; by skills: social versus smart; targeting a specific domain like entertainment, retail assistant, eldercare, or education for children; and by price. Regarding our six competitors: Moorebot, Elli-Q, and Olly are not mobile; Lynx and Nao are in toy category; Pepper is above $10k targeting B2B market; and finally, Temi can’t be considered an emotional robot.
Buddy remains highly differentiated as an all-in-one, best of his class experience, covering the needs for social interactions and assistance of his owners at each stage of their life at an affordable price.

The price range of Buddy will be between US $1700 and $2000.

4) A winning business model

Buddy’s great business model combines hardware, software, and services, and provides game-changing convenience for consumers, organizations, and developers.

Buddy offers a multi-sided value proposition focused on three vertical markets: direct consumers, corporations (healthcare, education, hospitality), and developers. The model creates engagement and sustained usage and produces stable and diverse cash flow.
5) A Passion for people and technology

From day one, we have always believed in the power of our dream: To bring the services and the fun of an emotional robot in every house, every hospital, in every care house. Each day, we refuse to think that we are stuck or limited; we work hard to make Buddy a reality that will help people all over the world and make them smile.

While we certainly appreciate Hasselvander’s consistent optimism and obvious enthusiasm, we’re obligated to point out that some of our most important questions were not directly answered. We haven’t learned anything that makes us all that much more confident that Blue Frog will be able to successfully deliver Buddy this time. Hasselvander also didn’t address our specific question about whether he feels like Blue Frog’s communication strategy with backers has been adequate, which is particularly relevant considering that over the four months between the last two newsletters, Buddy’s launch date slipped by six months.

At this point, all we can do is hope that the strategy Blue Frog has chosen will be successful. We’ll let you know if as soon as we learn more.

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