Tag Archives: science communication

Scientists Should Share Their Stories: More Important Now Than Ever

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On November 9, I woke up to a new world—a world that seemed more uncertain, more dangerous, and more hostile to logic and facts than existed just a few hours before. I’m referring, of course, to the recent U.S. presidential election. What this change in administration means for those of us in science is unclear. But many in the scientific community are worried about their jobs, their research funding, science literacy, the environment, and many other things. It’s taken me several days of reading and thinking about the potential impact of the election on science to get to a point where I can move forward.

In this post, I’d like to offer some thoughts about moving forward and emphasize the role of video as a powerful tool for scientists to communicate about the important work they do. Since I started this blog in 2012, I’ve written about many aspects of video-making and why video is so effective as a communication medium. I feel now, more than ever, that the scientific community need to make their voices heard; and they need to use 21st century communication tools such as video and social media if they want to reach beyond their ivory towers…and be heard.

A Disturbing Trend

For me, perhaps the most disturbing aspect of the campaign rhetoric was the way in which facts were ignored and conspiracy theories were embraced. When science was mentioned, it seemed that opinions based on falsehoods were accepted as facts, and facts (climate change, for example) were dismissed as hoaxes. This, despite all the efforts of government science agencies, science societies, and individual scientists to debunk false claims about climate, vaccines, evolution, and other politicized topics and to communicate the importance of credible science to society.

Just as disturbing is the message these actions send about how the scientific community may be viewed in the future. Scientists have traditionally been viewed by the American public as trustworthy (4 in 10 Americans express a high degree of confidence in the scientific community) and the scientific enterprise as essential to society (9 in 10 Americans agree that science and technology will create more opportunities for future generations) (NSF Science and Engineering Indicators 2016). But when our country’s leaders dismiss credible scientific evidence in favor of quackery, they are signaling that the sources of that evidence (scientists) are not to be trusted and that science is not important to the future of the country.

The Post-Truth Era

The scientific community will face some big challenges in the next few years—not the least of which will be countering anti-science and pseudo-science movements, which will be emboldened by the outcome of the election. We’ve already seen the rise of fake news sites on Facebook, with speculations about how they may have influenced the election. The public engagement with false stories on Facebook skyrocketed during the latter months of the campaign. Fake news reported on sites that made up stories about the candidates (e.g., the Pope endorsed Trump; Clinton sold arms to ISIS) outperformed real news. Such movements are fed by the larger political culture in which debate is won not by the facts, but by appeals to emotion. Factual rebuttals are ignored, while falsehoods are repeated ad nauseam.

This cultural shift has prompted the coining of new words that encapsulate the way “truth” is viewed. For example, the Oxford Dictionary has just announced its word of the year: post-truth, which means “as relating to or denoting circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion or personal belief”. As explained on the dictionary’s website, the “post” part of the term doesn’t mean “after an event” such as in post-war, but instead refers to a time when the concept is no longer relevant. In this case, the concept that is no longer relevant is the truth. Stephen Colbert had earlier introduced a word with a similar focus: truthiness (defined as ‘the quality of seeming or being felt to be true, even if not necessarily true’).

These new words illuminate a disturbing phenomenon, on broad display during the campaign—one that scientists (and science communicators) will find difficult to counter. I say difficult because what scientists deal in is the truth, and they are flummoxed when scientific facts are ignored in favor of myths or when the honesty of science practitioners is questioned. How do you counter someone who refuses to acknowledge hard facts or who questions the motivations of scientists?

Don’t Just Inform, Engage

One response to expressions of disbelief in scientific evidence is to double down on the facts and data, as if more scientific evidence will shatter misguided opinions. However, that knee-jerk reaction doesn’t work. This deficit model of science communication has been mostly discredited as ineffective (i.e., giving people more information does not necessarily change their minds). Credible scientific data will influence only those who are receptive to it (and seek it out); however, scientific evidence alone won’t budge those who are emotionally tied to a particular position. If people are unswayed by facts, then scientists and science communicators must pay attention to people’s opinions and attitudes about scientific topics. This idea is not new, of course. Science communicators have been saying for some time that it’s important to do more than just inform; it’s necessary to engage people emotionally and on a personal level. That doesn’t mean abandoning scientific evidence….it means developing messages that resonate with people on an emotional or personal level.

One person who does this well is Dr. Katharine Hayhoe, climate scientist:

We clearly need to continue explaining science to the public, but in a way that captures people’s attention, acknowledges their concerns and personal beliefs, and sustains the public trust in the scientific enterprise. Science agencies, societies, and organizations must continue to serve as clearing houses for objective science information and continue to challenge claims unsupported by scientific data. But what can you, an individual science professional, do?

Tell Your Story

One way you can help is to tell your story about how you conduct science or why you think your work is important to society as a whole. By telling stories, you can help the average person, who has never met a scientist, understand what we do and why we do it. Most scientists are hard-working, dedicated people who are passionate about their work. Their stories are rarely heard by the general public, though, but they would go a long way toward putting a human face on science and making an emotional connection.

There are many ways to tell your story. You can write about your desire to protect our natural resources or to find a cure for a deadly disease and post it on your website, on LinkedIn, or on a social media outlet. Or you can film yourself doing field research in a rainforest or conducting an experiment in your laboratory and explain what motivated you to study that particular subject. These don’t have to be full-blown memoirs or documentaries. A short blog post on Facebook or LinkedIn can convey a lot about you and your scientific passions. An increasing number of scientists and science students are sharing their research with Tweets, sometimes accompanied by a video clip. Such brief messages require little time to craft and post. A video clip attached to a Tweet can show the organism or habitat you are studying or illustrate how and where scientists work.

The point is to convey information about science and scientists without lecturing or challenging people’s personal beliefs. People don’t like to be lectured or to feel they are being talked down to or that their strongly-held beliefs are being questioned. Instead, show that you are excited to share your science with them….that you want them to share in the joy you felt when you discovered a new species, for example, or developed a new test to detect a deadly disease. Find common concerns between you and your potential audience and make the point that you are both seeking the same outcome (food security, better medical treatments, stronger economy, more jobs, cleaner environment). Then you can explain how your science will help make that happen. In telling your story, don’t be afraid to show your enthusiasm, curiosity, determination, or excitement about your research. Even if they question your scientific conclusion, they will appreciate your passion for your work and the integrity with which you conducted it.

Here are a few more ideas for making an emotional connection:

  • Share your joy about doing science.
  • Describe what you like most about being a scientist or your particular science discipline.
  • Talk about a challenge that you faced and how you overcame it.
  • Describe a failure and what you learned from it.
  • Show where you work (laboratory or field) and explain what you like about it.
  • Demonstrate your passion for your scientific topic and why you think it is important.
  • Describe how your curiosity led you to a discovery.
  • Talk about scientific integrity and how you strive to avoid bias.
  • Point out the challenge of finding sufficient funding to conduct your research.
  • Show how your research is helping a local community cope with a health or environmental issue.
  • Have citizens, resource managers, farmers, doctors, or other end users of science information describe the importance of your research to them.

Use Video To Connect With People

I think that one of the best ways to engage people is through the use of video. With video, you can more easily reach people who don’t have the time or patience to read a long essay. You can also more easily show your passion or other motivating force in a video. Yes, you can write about how passionate you are about coral reefs or mangrove forests, but actually seeing and hearing you express your feelings is much more effective and memorable. By showing your human side, you will automatically connect with people. By describing your successes and failures or what drives you to spend 12 hour days in the laboratory, you appeal to people’s fundamental emotions. People will recognize that you are not the arrogant know-it-all that they expected. When you develop a rapport with people, they become more receptive to your science information. And many people are now looking for science information in the form of video; YouTube is touted as the second largest search engine. Why not take advantage of this trend?

According to the NSF report, Science and Engineering Indicators 2016, only 46% of Americans have a good understanding of the process of scientific inquiry (how to conduct an experiment, for example). There are many ways to use video to inform the public about science and the scientific method. Here’s a nice example of a video that shows how tropical ecologists conducted a study of frogs, including the logistical challenges they faced:

The above video provides a glimpse into how scientists formulate a scientific question, design a study to answer the question, and then to conduct the study and analyze the results. This information is provided in a way that is interesting and personable and makes the point that scientists are driven by a strong sense of curiosity and a desire to understand how the world works.

There are many other great examples of videos that explain science, celebrate science, and defend science. Most are freely accessible on media-sharing sites and generally explain science in a way that the average person can understand. As I’ve tried to convey on this blog and website, making a video is no longer something only professional filmmakers can do. Anyone with a smartphone and an inexpensive movie-editing app can create an effective and compelling video. I hope more science professionals take advantage of these and other technologies to share their knowledge with the world.

Moving Beyond 2016

If you’re like me, you’ll be glad when this year comes to an end. It’s been stressful, to say the least, especially the past two weeks since the election. But where do we go from here? Those of us in science know how important the scientific enterprise is to our personal health, our environment, our economy, and our way of life. However, we’ve not done a great job of sharing our science with those outside the scientific community or explaining why science is important to society. This situation is slowly changing, but there remains a lot of resistance. I’m repeatedly told by colleagues that they don’t have time or they don’t see the benefit of communicating beyond the traditional outlets of science journals and conferences. They also express disinterest in using social media. I think it’s becoming clearer to everyone, though, why we should be concerned with informing and engaging the public, the media, and policy-makers.

To avoid feeling helpless in the face of uncertainty, I’ve tried to think of positive ways to move forward. Writing this blog post has helped me process some of the things that bothered me about the campaign and to think about ways to help fellow scientists who are wondering what they can do. I think that by simply telling our stories as scientists, we can begin (or continue) a conversation with the public. By showing our humanity, we send the message that we are not all that different…that we have similar concerns and questions about the world and are seeking ways to make the planet better for everyone. Communicating effectively is not easy, however. If you are considering engaging the public, sharing your experiences as a scientist through social media or on media-sharing platforms is a great way to get started.

More information about and tools for communicating can be found at the AAAS Center for Public Engagement with Science and Technology.

Can Artificial Intelligence Help Scientists Be Better Communicators?

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This post is part of a series about Artificial Intelligence (AI). In this concluding post, I explore the possibilities of AI to help scientists be better communicators.

As I’ve talked about before, many scientists have difficulty communicating their work in a way that is interesting and compelling, both intellectually and emotionally. This situation is improving, as more people recognize the importance of addressing the growing anti-science movement in the U.S. and the need for credible and articulate scientists to state the case for science. Once upon a time, scientists could safely remain in their ivory towers and talk among themselves about science. But no longer. Scientists are increasingly called upon to talk to the media (the AAAS has even published media interview tips for scientists), to testify before Congressional committees, to give public lectures, and to explain the “broader impacts” of their research on society. Consequently, efforts are underway to train the next generation of scientists to be better communicators (e.g., through academic programs focused on science communication). Science students today also seem to have a greater interest in developing better communication skills than when I was a student (just my personal impression).

Despite the advances in communication technology and emphasis on the new media to communicate information, though, I find that students still struggle with many of the same issues that plagued earlier generations when it comes to explaining science. And as the volume of science information grows exponentially, staying abreast of the literature and communication technology will be increasingly difficult for these future scientists.

The following are a few ways in which AI may help.

Designing a More Effective Science Message

One of the difficulties faced by a science communicator is how to design a message that resonates with a particular target audience. Few scientists are trained in communication theory and often rely on their default mode—explaining their work as they would for a technical audience. But what if the intended audience is not trained in science? How would you know if your message is appropriate in content and tone? AI might help, for example with the tone. The IBM Watson Tone Analyzer “uses linguistic analysis to detect three types of tones from text: emotion, social tendencies, and language style. Emotions identified include things like anger, fear, joy, sadness, and disgust. Identified social tendencies include things from the Big Five personality traits used by some psychologists. These include openness, conscientiousness, extroversion, agreeableness, and emotional range. Identified language styles include confident, analytical, and tentative.” One of the intended uses is to optimize a message intended for a particular audience. A message that shows strong emotions and is less analytical in style may be perceived more favorably by the general public, for example. You can try it out by inserting a piece of text into a dialog box and get an analysis of the overall tone of the message as well as a sentence by sentence breakdown. There are links to additional information about what a particular tone conveys and how to improve the tone of a message.

Finding Appropriate Material for Your Science Message

AI may be particularly useful in reducing the time involved in finding material to include in a message as well as to locate media that can be freely reused (such as in the public domain) or purchased for a fee. I know I spend a lot of time searching for footage, images, animations and music that I can freely use in a science video. Because I can’t review everything available, I probably miss a lot of really good material. Search engines can locate photos or videos posted on the Internet based on provided keywords and criteria (e.g., size, resolution, format). However, I may still end up with thousands of candidate media, not necessarily ranked according to what I might need. Artificial intelligence systems may improve such searches. Google, which used algorithms (rules set by humans) in the past to respond to search queries, is transitioning to deep neural networks, which can learn to respond to new search queries and other tasks such as figure out where a photo was taken. Improvements in search tools could make finding the right media for a video or other information product much easier.

Creating Media for Your Science Message

Another way AI might help is in creating new media such as art or music that can be used in an information product. If you need a painting or jingle but are not artistic or musically inclined, you may one day be able to generate what you need using an AI system trained to do this. An example of artwork created by an AI system and a 3-D printer is a new painting by Rembrandt…or rather one created by a computer based on information from 346 of Rembrandt’s paintings. The video below shows the amazing process by which this 3-D painting was created:

There are also efforts to develop AI systems that can compose music. Google is apparently working on such a system, although not everyone is impressed with the result. If you want to play around with a music-composing system (based on language algorithms), check out Wolfram Tones. You can select a music style and change up the instruments and other aspects to create a unique tune.

Teaching Science Professionals to Communicate Like Normal People

Scientists are traditionally taught to maintain a serious demeanor when speaking to an audience of our peers so that we are judged to be credible sources of information. But this approach doesn’t work so well with the average person. By hiding our emotions, we can come across on camera as “robotic”, for lack of a better term. So it’s rather ironic to consider if an AI can help scientists be better communicators.

The computer, Watson, was trained to “recognize” different emotions displayed in a film and to assess and rank the ones that would work best in a movie trailer about that film. In the same way, an AI system could be trained to evaluate video footage showing scientists or students conducting their research or discussing the challenges they faced and select the best clips in terms of conveying emotion (enthusiasm, humor, curiosity, tension). But we don’t really need a computer to tell us which footage shows a particular emotion—we are much better at this than any machine or program currently available.

However, an in-depth analysis of a person’s on-camera delivery of information might be used to train science professionals to be better communicators. A video clip could be fed into a computer like Watson to be assessed on the basis of both content and tone. The speaker would be evaluated and scored according to various criteria. They could then try to alter some aspect of their performance and see how it affects their scores. This immediate feedback from a machine might be a faster, more efficient, and less painful way for someone to improve their communication skills. A problem could be identified early and  eliminated before it becomes a habit.

I’m not aware of any system that can analyze a video of a person speaking, but there are AI-based personality tests that use what someone has written (an essay, a letter). One example is the Watson-based service, Personality Insights. You can see the outcome for famous people (Gandhi, Barack Obama) or you can insert your own text. I gave it a try by inserting the text from one of my blog posts. Here’s what it said about me:

“You are unconventional, somewhat indirect and skeptical. You are authority-challenging: you prefer to challenge authority and traditional values to help bring about positive changes. You are philosophical: you are open to and intrigued by new ideas and love to explore them. And you are unstructured: you do not make a lot of time for organization in your daily life. Your choices are driven by a desire for discovery. You are relatively unconcerned with both tradition and taking pleasure in life. You care more about making your own path than following what others have done. And you prefer activities with a purpose greater than just personal enjoyment.“

This analysis is eerily correct about some aspects of my personality. However, I got different results when I tried different text. For example, a second blog post generated the statement that my “choices are driven by a desire for organization”–opposite to the preceding analysis. Other aspects remained the same: authority-challenging, love of discovery, going my own way rather than following others. It’s necessary to provide sufficient text for a strong analysis, and the service warns you if you’ve given too little text. It also provides a more in-depth breakdown of the various traits that were analyzed.

As I said above, I don’t think there are any AI systems that can analyze a person’s performance in a video. However, it seems that this might be possible using a combination of existing AI systems such as the movie trailer and the personality test described above.

Conclusion

In this post, I’ve mentioned only a few ways AI might be used to improve science communication. Some of these systems, such as better search engines for locating media, are already available to us. Others will need more work and testing. I started this series with a hypothetical, futuristic scenario about a scientist using an AI system to create a video proposal. I don’t know if such a thing would ever exist or even be of widespread use in the scientific community. But it was fun thinking about it and learning more about AI systems.

This post is final part of a series about Artificial Intelligence (AI) and its potential role in science communication. You can find the first post in the series here.

What Does Artificial Intelligence Say About Human Creativity?

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pexels-photo-largeThis post is part of a series about Artificial Intelligence (AI) and its potential role in science communication. In this post (part 4), I talk about creativity and how it relates to AI.

In the previous posts, I’ve been talking about the computer Watson and how it helped create a trailer for the movie Morgan. Is this “cognitive movie trailer” evidence of AI creativity or the potential to mimic human creativity? In other words, can a human be replaced by a machine—in this case a trailer editor who uses skill and imagination to create something new?

Let’s first consider what creativity is. The dictionary defines creativity as the ability to make new things or think of new ideas. But is it a trait only exhibited by humans? Is it an attribute that some people have and others don’t? Is it an occasional mental state that we enter? Can one learn to be more creative? I’m not sure of the answers to all these questions, but perhaps it’s more helpful to ask what creativity is not. It’s not problem solving, which is a process whereby a “rule” or “algorithm” is applied to solve a problem. Being able to understand and apply a rule is different from discovering the rule.

In the case of the computer Watson, we can see that understanding what a movie trailer is and identifying the best scenes from the movie Morgan to use fall into the realm of problem solving and not creativity. A human stepped in to do the actual film editing, which additionally suggests that the “creative” aspect of putting together the trailer could only be done by a person with the requisite editing skills and imagination to sequence the clips and add other components such as music and text. However, I don’t think a human was essential to do the editing, once the scenes were selected.

A movie trailer template could have provided a guide with plascreenshot_imovie13ceholders for media and text, much the way iMovie trailers are created. In this screenshot, you can see an iMovie trailer template, which guides the choice of video clips and text. Scenes are suggested, as are text titles that form a story. Such a template could have been used along with the ten selected scenes from Morgan to produce a finished trailer. However, such an ability by an AI could not be called creative. Although some decision-making would be involved in selecting which scene to go into each placeholder, those steps would be guided by a set of rules—in other words, problem-solving, not creativity. Also, templates would produce an assembly-line of movie trailers that all follow the same format—rather than a unique trailer with sequences, pacing, music, and other features individually selected by the editor using his or her knowledge, skill, and imagination.

I think we are a long way from machines that think and create like humans. However, we are at a point where AI can be used to enhance human skills and help us perform tasks involving vast amounts of information. Artificial intelligence systems are already at work aiding, for example, analysis of medical images, detection of suspicious charges to our credit cards, or automated telephone customer service. The real question is not whether AI can replicate human thinking or creativity but how AI can help humans create new things or think of new ideas faster and more efficiently.

This post is part of a series about Artificial Intelligence (AI) and its potential role in science communication. In the next and final post (part 5), I’ll discuss how AI might help scientists be better communicators.

How Did Artificial Intelligence (AI) Help Create a Movie Trailer?

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This post is part of a series about Artificial Intelligence (AI) and its potential role in science communication. In this third post, I describe how the computer, Watson, helped create a movie trailer.

Before we get to the Watson movie trailer, let’s first think about how movie trailers are made. Movie trailers are designed to convince people to go see a particular movie. Superficially, trailers appear to be a condensed version of the film, but good trailers are carefully designed to raise expectations and to appeal to the viewer’s emotions. Most trailers follow a typical formula, modified for the genre such as Action/Adventure, Comedy, Drama/Thriller, or Horror. Many trailers begin by introducing the characters and the setting of the film. Next to appear are the obstacles that change that world and set the characters on a new course. This may be followed by increasingly exciting, funny, or tension-filled scenes to ramp up the viewer’s desire to find out what happens. The specifics—selection of clips, the way they are cut (rapid-fire or slow-reveal), the fonts used for text titles, narration, music, and other choices—differ among movie genres.

All, however, are built more or less the same way by the trailer editor. The original movie is first watched carefully and deconstructed to reveal its basic components, visual and audio. The process then slices the movie audio and video further into segments that can then be rearranged to build the trailer. Next comes the choice of the best elements to use. Is the acting superb? The cinematography? The story? Editors often select those elements that highlight the merits of the film or the ones that have the most emotional impact on a viewer.

Not surprisingly, the AI-enhanced trailer of the movie Morgan was created in much the same way as a regular trailer. The first step, however, was to train Watson to understand what a movie trailer is and what features of a movie are used in movie trailers. The IBM team did this through machine learning and Watson APIs (Application Programming Interfaces, i.e., programming instructions). Basically, each of 100 movie trailers was dissected into component scenes, which were then subjected to the following analysis: (1) Visual (identification of people, objects, and environment), (2) Audio (narrator and character voices, music), and (3) Composition (scene location, framing, lighting). Each scene was tagged with one of 24 emotions (based on visual and audio analysis) and further categorized at to type of shot and other features.

Once Watson was trained, it was fed the full-length movie, Morgan. Based on its knowledge of what makes up a movie trailer—particularly a suspenseful one, Watson then selected ten segments as the best candidates for a trailer. These ten turned out to be scenes belonging to two broad categories of emotion: tenderness or suspense. Because the system was not taught to be a movie editor, a human editor was brought in to finish the trailer. The human editor ordered the segments suggested by Watson and also added titles and music. [see reference below for additional details]

Here’s the trailer that resulted, along with some explanations of how it was done (direct link to video):

As you saw, the end result looks and sounds like a typical movie trailer. The big question is if this cognitive movie trailer does what a good trailer should: make us want to see the movie.

If you like science fiction films that explore questions about human engineering or artificial intelligence, then this trailer might appeal. The trailer does convey through the ten selected scenes that Morgan is an engineered creation that goes rogue—a story we’ve heard before. However, we are left in the dark about what exactly Morgan’s problem is (other than being locked up) and how the humans will deal with it. Many trailers fail by showing too much of the story. For example, the official Morgan trailer shows a lot more of the movie, which made the story sound similar to another movie, Ex Machina (an engineered human-like entity is confined in a futuristic laboratory, tested for flaws, goes amok, kills or maims one or more people, and escapes into the world). But by limiting what’s revealed, the Watson-enhanced trailer makes us think that maybe this story will differ from previous movies and be worth seeing.

I thought the computer-selected segments were interesting in that they not only conveyed a range of emotions (happiness, tenderness, suspense, fear), but many did so in a subtle way (a smile, a hand gesture, a slight gasp, a head turn). No scenes seemed to be selected from the latter part of the movie, which would have given too much of the story away. I don’t know if this was a result of the Watson system ranking scenes near the end lower than those from the beginning and middle.

In the end, I think the Watson-enhanced trailer is pretty good and perhaps better in some ways than the official trailer created entirely by a human.

For more information about the making of the Morgan movie trailer, see this article: Smith, J.R. 2016. IBM research takes Watson to Hollywood with first “cognitive movie trailer”. Think <https://www.ibm.com/blogs/think/2016/08/31/cognitive-movie-trailer/>

This post is part of a series about Artificial Intelligence (AI) and its potential role in science communication. In the next post (part 4), I’ll talk a bit about what AI means for human creativity.

What is Watson and What Does It Have to Do with Videos?

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This post is part of a series about Artificial Intelligence (AI) and its potential role in science communication. In this second post (part 2), I describe Watson, a computer that was trained to assist in the making of a movie trailer.

artificial-intelligence-elon-musk-hawkingIn the previous post (part 1), I explained that IBM’s computer system, Watson, was used to help a Hollywood film studio make a trailer for the movie, Morgan. But what is Watson? According to the IBM website, Watson is “a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data”. Translating that into everyday language: Watson is a computer that can answer tricky questions like the ones posed on the gameshow Jeopardy!. In 2011, Watson beat two reigning champions, providing answers to Jeopardy! clues—example: even a broken one of these on your wall is right twice a day; correct reply: what is a clock?—and winning $1,000,000 (which was donated to two charities).

Actually, Watson is a cluster of computers (90 servers and 2880 processor cores) running something called DeepQA software. Despite its performance on Jeopardy!, Watson does not “think” like a human and arrives at an answer to a question differently. Tons of information from various sources have been input, providing Watson with an enormous information base to analyze. For the game show, Watson used more than 100 algorithms to come up with a set of reasonable answers to a question. It then ranked those answers and searched its information database for any evidence in support of each answer. The answer with the most evidence was given the highest confidence. When the confidence was not high enough during the Jeopardy! game, though, Watson did not risk losing money by offering an answer.

Despite fears that AI will eliminate jobs or go rogue and destroy humankind, as depicted in the Terminator series, the system is viewed by developers as a way to augment human intelligence and to reduce the time spent on tasks involving large amounts of information. IBM prefers the term Augmented Intelligence (systems that enhance and scale human intelligence) to Artificial Intelligence (systems that replicate human intelligence). There are many ways in which AI can augment information-intensive fields such as medicine, telecommunications, weather forecasting, and financial services. Since the Jeopardy! match, Watson has been used to create cognitive apps and computing tools for businesses and healthcare professionals.

It’s not difficult, then, to imagine AI systems aiding scientific research and especially the communication of those findings in a more efficient way. More and more people are getting their information, particularly about science, in the form of video, but many science professionals have little time or incentive to devote to learning and using new communication tools. A system that can reduce the time involved in making a video and simultaneously enhance the quality could greatly improve communication of science and its importance to society. The first cognitive movie trailer, aided by the computer, Watson, is a “proof of concept” in this regard.

For more information about Watson and preparation for the Jeopardy! gameshow, see this article: Ferrucci, D. et al. 2010. Building Watson: An overview of the DeepQA process. Association for the Advancement of Artificial Intelligence pp. 59-79.

This post is part of a series about Artificial Intelligence (AI) and its potential role in science communication. In the next post (part 3), I’ll describe how Watson helped create a movie trailer.