Open Password – Dienstag, den 12. Februar 2019
Alan Turing – Artificial Intelligence – Communication Professionals – Human Intelligence – Turing Test – ELIZA – PARRY – Fourth Industrial Revolution – B2B-Applications – Dartmouth Summer Research Project – AMEC – Kris Hammond – Northwestern University – IBM – Watson – Google – Deep Learning – Nigel Shadbolt – University of Oxford –Machine Learning – Natural Language Processing – Deep Learning – Investopedia – Artificial Neural Networks – Automatic Speech Recognition – Visual Art Processing – Content-based Recommendation Systems – Bioinformatics – Algorithms – Communications Professionals – Corporate Communications – Anthony Petrucci – HID Global – PR Agencies – Online Media Monitoring – Wendy Marx – Marx Communications – Media Relations – John Bara – Mintigo – Crisis Communication – Saif Ajani – Keyhole – Investor Relations – Zach Rothberg – AlphaSense – SXSW – Ray Kurzweil – Technological Singularity – Robots – Ethics – Victor Frankenstein – Adam Briggle – University of North Texas – Google – Elon Musk – OpenAI initiative – Center for Humane Technology – Tristan Harris – Elsevier – Deutsche Nutzer – Deutsche Bibliotheken – Questel – Concur IP – Wiley – Jesse Wiley – Reddit – Tencent – Open-Access-Zeitschriften – Plan S – Mc Clatchy – Facebook – Blockchain – Chainspace – Questel
LexisNexis zur Künstlichen Intelligenz
The Rise of AI und How it
will Impact Communications Professionals
Von Leela Hauser, LexisNexis
The famed mathematician Alan Turing, who is often called the father of modern computer science, set out to design an experiment nearly 70 years ago that would evaluate whether machines could think. In his landmark 1950 research paper, “Computing Machinery and Intelligence,” Turing details his test of a machine’s ability to exhibit intelligent behavior that is indistinguishable from a human’s intelligence.
The Turing Test, as it would come to be known by subsequent generations of technology researchers, challenged scientists with a provocative and prescient question: “Is a computer’s response indistinguishable from a human response?”
The standard interpretation of the Turing Test involves three parties. Player C acts as the interrogator and is given the task of posing a series of questions to Player A and Player B. Based exclusively on the answers to the questions, Player C tries to determine which player (A or B) is a computer and which is a human. “Passing” the Turing Test results when Player C cannot tell the difference between A and B. This scientific idea—the capability of a machine to imitate intelligent behavior—ushered in the concept of Artificial Intelligence (AI).
In the decades that have passed since Turing’s groundbreaking research, there have been a series of attempts to develop technologies that would pass the Turing test. Early precursors to “chatbots”—programs known as ELIZA (1966) and PARRY (1972) — claimed levels of success with AI, but validation tests came up short. Various software developers created more advanced chatbots in the 1990s to simulate human conversation with impressive results, elevating the potential of AI.
Today, AI may be the most exciting area of technology innovation…and the most fear-inducing as well. It is sometimes referred to as “The Fourth Industrial Revolution,” a lofty description that conjures up the economic triumphs of prior generations, each of which produced short-term displacement of workers whose manual labor was automated or industrialized in some way.
The consumer applications that leverage AI are ubiquitous—speech recognition and mobile check deposits are two examples that most of us can relate to on a day-to-day basis—but it is the use of AI for business-to-business applications that has raised the stakes to a new level.
In particular, communications professionals are now giving their full attention to AI and its potential for our industry. In AMEC’s Global Business Insights Study 2018 Report, nearly 8 in 10 AMEC members (79 percent) said they view AI as something they take seriously for their business and another 14 percent agreed that AI is important for the industry, if not their own business.
The purpose here is to provide readers with a primer on AI so it becomes clear what AI is and what it is not, a review of the three major types of AI technology in business use today, and an exploration of how AI will impact communications professionals within five specific disciplines over the course of the next several years. Our goal is to demystify the rise of AI and to shine a bright light on how communications professionals can leverage the power of AI to work smarter and faster.
What is AI?
It appears that the earliest use of the term “Artificial Intelligence” can be found in 1955, when a group of researchers from various disciplines developed a proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Consider the foreshadowing in that document’s description of the potential application of AI to “automatic computers”:
The speeds and memory capacities of present computers may be insufficient to simulate many of the higher functions of the human brain, but the major obstacle is not lack of machine capacity, but our inability to write programs taking full advantage of what we have.
That obstacle was chipped at for decades and is now being removed a bit more each day with new B2B applications of AI technologies.
Modern definitions of AI tend to focus on the “theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and translation between languages,” according to the English Oxford Living Dictionary.
Kris Hammond, professor of computer science and journalism at Northwestern University, observes that AI tends to break into different camps, based on how the technology aligns with the way people work. The two most basic camps are:
- “Strong AI” — The program can adapt and learn, genuinely simulating human reasoning; and
- “Weak AI” — The program needs to be trained by a human in order to behave like humans and work effectively.
Most AI development today falls into the Weak AI camp. These applications use human reasoning as a guide to innovate products and services, not to replicate the human mind in the form of a machine. Prominent examples might include IBM’s Watson and Google’s Deep Learning as systems that are not exactly like the brain, but inspired by it, as developers seek to harness the potential of AI to advance human problem-solving and productivity.
“Those working with AI today make it a priority to define the field for the problems it will solve and the benefits the technology can have for society,” according to Forbes.
Primary Applications of AI
One of the reasons that the mention of AI provokes visceral reactions among many professionals is that there is widespread confusion over the various terms that circle around the idea of artificial intelligence. It seems to come with such eerie nomenclature—for example, “machine learning”—that it causes many people to glaze over or, worse yet, even take a step backwards.
For example, many people are distracted by the science fiction images of Terminator-like killer robots rising up to destroy humans and occupy the planet. Experts want to dissuade our fears and point us back to reality.
“The danger is clearly not that robots will decide to put us away and have a robot revolution,” said Sir Nigel Shadbolt, professor of computer science at the University of Oxford. Prof. Shadbolt told The Guardian that he believes AI “will bring overwhelming benefits to humanity, revolutionizing cancer diagnosis and treatment, and transforming education and the workplace.”
In order to achieve the potential of AI, data scientists use tools such as Machine Learning, Natural Language Processing and Deep Learning to create processes that feed into specific AI applications today.
Machine Learning. Machine learning is perhaps the most widely recognized example of AI technology because it is the one most frequently used in businesses. The focus of these applications is the use of data to train computer algorithms that can perform tasks that are very difficult for humans to accomplish at scale. For example, the ability to recognize specific objects buried among thousands of images—and within seconds—is not possible with the human eye, but machines can learn to do it if computer algorithms are trained properly.
Natural Language Processing. Natural language processing relies on machine learning algorithms to make sense of human language patterns. This is a powerful AI technology application because it enables machines to translate sentences from one language to another, simply by learning the subject and the context in the original language. Natural language processing overcomes the limitations of previous-generation software tools that were unable to solve for inconsistent grammar, ambiguous sentence structure and other machine translation limitations.
Deep Learning. This is perhaps the least understood sub-field within AI, which is likely a reflection of the ongoing research and development still underway. Deep Learning refers to a class of machine learning algorithms that are capable of “learning unsupervised from data that is unstructured or unlabeled,” according to Investopedia. It achieves this function by imitating the workings of the human brain in processing data—sometimes known as Artificial Neural Networks—and creating patterns for use in decision making. In essence, Deep Learning is a more advanced method of Machine Learning that leverages more computing power to run complex and layered models not otherwise possible by human operation. Examples of Deep Learning breakthroughs include automatic speech recognition, visual art processing, content-based recommendation systems and bioinformatics.
To sum up: Machine Learning applications are focused on the use of data to train algorithms that can perform challenging tasks that are difficult for humans to do at scale; Natural Language Processing applications deploy the power of algorithms to understand natural human language; and Deep Learning is the next level of machine learning methods that relies on neural learning, as opposed to task-specific algorithms. The nomenclature may not be simple and is certainly not intuitive, but it need not be off-putting for business professionals who appreciate the power of each of these primary sub-fields of AI.
How AI Will Impact Communications
LexisNexis reviewed insights from experts within five professional disciplines inside the worlds of media and communications, evaluating their predictions for how AI will impact the field in the coming years. The consensus is that the opportunities are extraordinary for those professionals who are willing to embrace the potential of AI in communications.
- Corporate Communications. AI will deliver extraordinary new capabilities to corporate communications managers, providing them with better insights and predictive analytics to guide their decision making. It will enable the development of tools that can predict oncoming issues, identify inconsistent corporate messaging and inform corporate communications leaders of potential external communications problems. By helping their teams anticipate and interpret probable business scenarios, AI may give corporate communications professionals a more influential voice in their organization’s board room.
“We as corporate communications professionals should expect more from ourselves and our teams,” writes Anthony Petrucci, senior director of corporate communications at HID Global. “I challenge my colleagues in this field to embrace the opportunities AI presents to augment our communications function in the long term, rather than being defensive, reactionary or ignorant that change will happen.”
- PR Agencies. Many PR agencies are already embracing the use of AI to accomplish certain work that requires significant economies of scale. For example, several AI powered tools deliver high-quality media lists and comprehensive online media monitoring. Emerging applications of AI for PR consultants that are now on the horizon include tools that predict media coverage trends, software that will help PR counselors determine the optimal time to start a campaign for a client and the most compelling messaging to use, and dynamic metrics to measure effectiveness with identifying and reaching key influencers.
“Far from stealing our jobs, AI can make us better at them,” says Wendy Marx, president of PR agency Marx Communications. “The future of AI and PR is bright and positive—a future that we should embrace, rather than fear.”
- Media Relations. AI will help with the practice of media relations in several ways, beyond the obvious application of building comprehensive media lists. AI tools are entering the marketplace that will improve automation in the distribution of news releases, story pitches and other content. It will also enable more targeted media pitching as practitioners are able to hone their messages for a narrow audience or individual reporter. And the bottom line is that AI will help professionals make more successful pitches.
“If publicists aren’t getting a true read on what the overall trends are, they will pitch the journalist population ineffectively,” said John Bara, president of Mintigo, a predictive marketing technology company, in Adweek. “Savvy publicists will use AI to their advantage by zeroing in on very specific journalists. Rather than doing a napalm strike of pitching, the effective publicists will use these technologies to find the right journalists, at the right time, on the right channel.”
- Crisis Communications. While it’s true that no two crises are identical, most communications professionals who have experience managing crisis communications teams can recite the fundamental rules of the playbook. The challenge that is often elusive is knowing which flash points are likely to go away quietly and which ones are likely to snowball into full-blown crises that will damage a brand. New AI-powered tools can monitor multiple data points (e.g., news coverage, social media buzz, etc.) and help you predict with a high degree of accuracy if a crisis is going to spiral into a problem that will require an aggressive communications response.
“What brands or PR agencies fail to understand is how big a crisis is going to be,” said Saif Ajani, chief executive officer of Keyhole, a hashtag analytics company, in a column that appeared in Venture Beat. “The challenge for PR professionals is how do you know what’s going to go massive? Within 24 or 72 hours we can actually tell you how massive a crisis is going to be in the next 30 days.”
- Investor Relations. AI technologies have the potential to radically improve the way investor relations professionals work by delivering new intelligent search platforms that understand complex business concepts in ways that previous generations of search engines simply could not. That means that professionals will be better equipped to instantly locate crucial information pertaining to a company’s financial communications or disclosures—for example, “guidance” is a term with very specific meaning in investor relations, but not the conventional meaning ascribed by traditional search engines. By instantly finding the precise information they need across millions of documents, investor relations professionals will be able to act faster on valuable intelligence.
“AI technology is here to enhance the IR workflow,” writes Zach Rothberg, vice president of investor relations services for AlphaSense, in NIRI Houston News. “Investor relations officers who embrace AI can save hours of time and deliver key insights that help them perform the high-value tasks that will ultimately elevate their investor relations programs.“
The potential “Fourth Industrial Revolution” in human history—the rise of Artificial Intelligence—is the driving force behind an exciting new generation of technologies that are transforming the way people live, work and connect in community. With respect to business-to-business applications, it is helping companies operate more efficiently and achieve levels of productivity not otherwise possible.
However, it is not without its share of controversy regarding the ethical implications of how the technology is developed and deployed. At SXSW 2018, a number of sessions tackled the ethics, morals and social implications of the rapid advances in AI—including how to build “more moral machines.” And the prediction by noted futurist Ray Kurzweil that technological singularity will take place in 2045 (e.g., the point in time when AI will surpass human intelligence) has provoked a scientific debate about the ethics of AI and the potential for robots that demand their own rights.
“On the 200th anniversary of Frankenstein, we find ourselves grappling with the unintended consequences of our creations on Facebook, to artificial intelligence and human genetic engineering,” writes Adam Briggle, professor of philosophy at the University of North Texas. “Will we sail through safely or will we, like Victor Frankenstein, witness ‘destruction and infallible misery’?”
Companies such as Google are embracing this challenge by creating ethical guidelines for the use of AI and Elon Musk’s OpenAI initiative seeks to help develop safe AI technologies. Perhaps most noteworthy is the work being done by the Center for Humane Technology—founded by former Google Design Ethicist Tristan Harris—in an effort to realign technology with humanity’s best interests. The non-profit group identifies AI as one of four distinct forces that contribute to rising addiction on technology and lays out the levers that can be pulled to make sure these technologies are used for the well-being of humanity. These ambitious efforts to address the ethical debate surrounding the growth of AI technology applications seek to mitigate the risk of “unintended consequences” as the pace of innovation continues to advance.
The lesson for the communications industry is that the rise of AI will allow communications professionals to work smarter and faster, but the key to its use is human over-sight. After all, communication is intrinsically human, so the human role in AI must be the most important one in the overall decision loop.
By reviewing how the technology is implemented and modifying it to achieve the correct results, communications professionals can ensure their trust in the systems and retain their mastery over the AI tools they put to use.
Deutsche Wissenschaftler täglich
10.000-mal über Elseviers Paywall?
Researchers at German institutions that have let their Elsevier subscriptions lapse while negotiating a new deal are hitting the paywall for the publisher’s most recent articles around 10,000 times a day, according to Elsevier — which publishes more than 400,000 papers each year. But at least some German libraries involved in negotiating access to Elsevier say they are making huge savings without a subscription, while still providing any articles their academics request.
Questel verbessert Service über Patent-Lebenszyklus. Questel announces a majority investment in Concur IP, an India and US-based IP services company well known for its patent drafting and standard essential patent-related capabilities. This investment adds an essential piece to Questel’s existing suite of IP services. Questel can now support its clients throughout the entire IP lifecycle offering patentability search, patent drafting, translation, and international filing, as well as post-grant support during licensing negotiation and litigation.
Siebente Generation der Wileys in Top Management. John Wiley & Sons, Inc. announced that Jesse Wiley, a member of the seventh generation of the Wiley family, has been appointed Non-Executive Chairman of the Board. Mr. Wiley, 48, has served on the Board since 2012. He will succeed Matthew Kissner, who has served as Chairman since 2015. Mr. Kissner will step down from the Board to take on the role of EVP and Group Executive reporting into President and CEO, Brian Napack.
Nach chinesischem Investment kann Reddit 3 Milliarden Dollar wert werden. Reddit is raising $150 million to $300 million to keep the front page of the internet running, multiple sources tell TechCrunch. The forthcoming Series D round is said to be led by Chinese tech giant Tencent at a $2.7 billion pre-money valuation. Depending on how much follow-on cash Reddit drums up from Silicon Valley investors and beyond, its post-money valuation could reach an epic $3 billion.
Open-Access-Journale meistens ungeeignet für Plan S. Only a small proportion of open-access scientific journals fully meet the draft requirements of Plan S, the initiative primarily by European funders to make all papers developed with their support free to read, a study has found. Compliance with the rules could cost the remaining journals, especially smaller ones, more than they can afford.
US-Zeitungskrise continuing. The McClatchy Company offered buyouts to roughly 10 percent of its employees at the newspapers it owns across the U.S. The McClatchy Company, owner of 29 daily papers, including the Miami Herald, the Kansas City Star and the Sacramento Bee, said it is offering voluntary buyout packages to 450 employees—about 13 percent of its workforce. The buyouts follow 140 job cuts in August.
Facebook übernimmt Blockchain-Unternehmen. Facebook acquired its first company in the blockchain space, smart contract protocol development and research firm Chainspace. The move was likely made to acquire the company’s team of experienced crypto developers. The move was done to augment Facebook’s growing blockchain division. Facebook did not disclose the amount paid for the acquisition.
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