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Monthly Archives: June 2017
If Your Company Isn’t Good at Analytics, It’s Not Ready for AI – Harvard Business Review
Posted: June 7, 2017 at 5:16 pm
Executive Summary
Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics end up paralyzed. So how can companies tell if they are really ready for AI and other advanced technologies? First, managers should ask themselves if they have automated processes in problem areas that cost significant money and slow down operations. Next, managers should ensure they have structured analytics as well as centralize data processes so that the way data is collected is standardized and can be entered only once. After these standard structured analytics are in place, they can integrated with artificial intelligence.
Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them.
By contrast, companies with strong basic analytics such as sales data and market trends make breakthroughs in complex and critical areas after layering in artificial intelligence. For example, one telecommunications company we worked with can now predict with 75 times more accuracy whether its customers are about to bolt using machine learning. But the company could only achieve this because it had already automated the processes that made it possible to contact customers quickly and understood their preferences by using more standard analytical techniques.
So how can companies tell if they are really ready for AI and other advanced technologies?
First, managers should ask themselves if they have automated processes in problem areas that cost significant money and slow down operations. Companies need to automate repetitive processes involving substantial amounts of data especially in areas where intelligence from analytics or speed would be an advantage. Without automating such data feeds first, companies will discover their new AI systems are reaching the wrong conclusions because they are analyzing out-of-date data. For example, online retailers can adjust product prices daily because they have automated the collection of competitors prices. But those that still manually check what rivals are charging can require as much as a week to gather the same information. As a result, as one retailer discovered, they can end up with price adjustments perpetually running behind the competition even if they introduce AI because their data is obsolete.
Without basic automation, strategic visions of solving complex problems at the touch of a button remain elusive. Take fund managers. While the profession is a great candidate for artificial intelligence, many managers spend several weeks manually pulling together data and checking for human errors introduced through reams of excel spreadsheets. This makes them far from ready for artificial intelligence to predict the next risk to client investment portfolios or to model alternative scenarios in real-time.
Meanwhile, companies that automate basic data manipulation processes can be proactive. With automated pricing engines, insurers and banks can roll out new offers as fast as online competitors. One traditional insurer, for instance, shifted from updating its quotes every several days to every 15 minutes by simply automating the processes that collect benchmark pricing data. A utility company made its service more competitive by offering customized, real-time pricing and special deals based on automated smart meter readings instead of semi-annual in-person visits to homes.
Once processes critical to achieving an efficiency or goal are automated, managers need to develop structured analytics as well as centralize data processes so that the way data is collected is standardized and can be entered only once.
With more centralized information architectures, all systems refer back to the primary source of truth, updates propagate to the entire system, and decisions reflect a single view of a customer or issue. A set of structured analytics provides retail category managers, for instance, with a complete picture of historic customer data; shows them which products were popular with which customers; what sold where; which products customers switched between; and to which they remained loyal.
Armed with this information, managers can then allocate products better, and, see why choices are made. By understanding the drivers behind customer decisions, managers can also have much richer conversations about category management with their suppliers such as explaining that very similar products will be removed to make space for more unique alternatives.
After these standard structured analytics are integrated with artificial intelligence, its possible to comprehensively predict, explain, and prescribe customer behavior. In the earlier telecommunications company example, managers understood customer characteristics. But they needed artificial intelligence to analyze the wide set of data collected to predict if customers were at risk of leaving. After machine learning techniques identified the customers who presented a churn risk, managers then went back to their structured analytics to determine the best way to keep them and use automated processes to get an appropriate retention offer out fast.
Artificial intelligence systems make a huge difference when unstructured data such as social media, call center notes, images, or open-ended surveys are also required to reach a judgment. The reason Amazon, for instance, can recommend products to people before they even know they want them is because, using machine learning techniques, it can now layer in unstructured data on top of its strong, centralized collection of structured analytics like customers payment details, addresses, and product histories.
AI also helps with decisions not based on historic performance. Retailers with strong structured analytics in place can figure out how best to distribute products based on how they are selling. But it takes machine learning techniques to predict how products not yet available for sale will do partly because no structured data is available.
Finally, artificial intelligence systems can make more accurate forecasts based on disparate data sets. Fund managers with a strong base of automated and structured data analytics are predicting with greater accuracy how stocks will perform by applying AI to data sets involving everything from weather data to counting cars in different locations to analyzing supply chains. Some data pioneers are even starting to figure out if companies will gain or lose ground using artificial intelligence systems analyses of consumer sentiment data from unrelated social media feeds.
Companies are just beginning to discover the many different ways that AI technologies can potentially reinvent businesses. But one thing is already clear: they must invest time and money to be prepared with sufficiently automated and structured data analytics in order to take full advantage of the new technologies. Like it or not, you cant afford to skip the basics.
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If Your Company Isn't Good at Analytics, It's Not Ready for AI - Harvard Business Review
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Elon Musk says artificial intelligence will beat humans at ‘everything’ by 2030 – Fox News
Posted: at 5:16 pm
The performance of humans puny brains will be outmatched by computers within just 13 years, billionaire Elon Musk has claimed.
TheTesla and SpaceX foundersaid that artificial intelligence will beat us at just about everything by 2030.
He made the comments on Twitter, where he was responding to a new study which claims our race will be overtaken by 2060.
Probably closer to 2030 to 2040 in my opinion, he wrote.
According to the terrifying research from boffs at the University of Oxford, its not looking good for us humans.
Machines will be better than us at translating languages by 2024 and writingschool essays by 2026, they claimed.
Within ten years computers will be better at driving a truck than us and by 2031 they will be better atselling goods and will put millions of retail workers on the dole queue.
AI will write a bestselling book by 2049 and conduct surgery by 2053, the researchers suggested.
In fact, every single human jobwill be automated within the next 120 years,according to computer experts the university researchers quizzed.
It's unlikely to trouble the billionaire tech entrepreneur, however.
Musk already has plans to plug our brains into computers.
He recently launched a new neuroscience company which aims to develop cranial computers that can download thoughts and possibly even treat disorders such as epilepsy and depression,the New York Post reported.
Over the years, the 45-year-old hasconjured up new ideas for space rocketsand electric-cars, proven that they can work efficiently, and then rolled them out for public and private use.
He's even hoping to start a human colony on Mars by 2030.
He's not alone in his estimations for the great computer takeover, either.
Scientists reckon humans are on the brink of a new evolutionary shift and man as we know it "probably won't survive".
In a terrifying advance, some have warned that computers are so advanced, those developing the complex formulas that make them "tick" aren't even sure howthey work.
And because they cannot understand the mechanical brains they have built, they fear that wecould lose control of them altogether.
That means they could behave unexpectedly - potentially putting lives at risk.
Take the case of driverless cars, for example where an algorithm might behave differently to normal and cause a crash.
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Apple is finally serious about artificial intelligence – Quartz
Posted: at 5:16 pm
As research teams at Google, Microsoft, Facebook, IBM, and even Amazon have broken new ground in artificial intelligence in recent years, Apple always seemed to be the odd man out. It was too closed off to meaningfully integrate AI into the companys softwareit wasnt a part of the research community, and didnt have developer tools available for others to bring AI to its systems.
Thats changing. Through a slew of updates and announcements today at its annual developer conference, Apple made it clear that the machine learning found everywhere else in Silicon Valley is foundational to its software as well, and its giving developers the power to use AI in their own iOS apps as well.
The biggest news today for developers looking to build AI into their iOS apps was barely mentioned on stage. Its a new set of machine learning models and application protocol interfaces (APIs) built by Apple, called Core ML. Developers can use these tools to build image recognition into their photo apps, or have a chatbot understand what youre telling it with natural language processing. Apple has initially released four of these models for image recognition, as well as an API for both computer vision and natural language processing. These tools run locally on the users device, meaning data stays private and never needs to process on the cloud. This idea isnt neweven data hoarders like Google have realized the value of letting users keep and process data on their own devices.
Apple also made it easy for AI developers to bring their own flavors of AI to Apple devices. Certain kinds of deep neural networks can be converted directly into Core ML. Apple now supports Caffe, an open-source software developed by the University of California-Berkeley for building and training neural networks, and Keras, a tool to make that process easier. It notably doesnt support TensorFlow, Googles open-source AI framework, which is by far the largest in the AI community. However, theres a loophole so creators can build their own converters. (I personally expect a TensorFlor converter in a matter of days, not weeks.)
Some of the pre-trained machine learning models that Apple offers are open-sourced Google code, primarily for image recognition.
Apple made it clear in the keynote today that every action taken on the phone is logged and analyzed by a symphony of machine-learning algorithms in the operating system, whether its predicting when you want to make a calendar appointment, call a friend, or make a better Live Photo.
The switch to machine learning can be seen in the voice of Siri. Rather than using the standard, pre-recorded answers that Apple has always relied on, Siris voice is now entirely generated by AI. It allows for more flexibility (four different kinds of inflection were demonstrated on stage), and, as the technology advances, it will sound exactly like a human anyway. (Apples competitors are not far off.)
Apple also rattled off a number of other little tweaks powered by ML, like the iPad distinguishing your palm from the tip of an Apple Pencil, or dynamically extending the battery life of the device by understanding which apps need to consume power.
Okay, so Apples really only published one paper. But it was a good one! And Ruslan Salakhutdinov, Apples new director of AI research, has been on the speaking circuit. He recently spoke at Nvidias GPU Technology Conference (although Apples latest computers use AMD chips), and will be speaking later this month in New York City, to name a few.
Apple also held a closed-door meeting with their competitors at a major AI conference late last year, shortly after Salakhutdinov was hired, to explain what it was working on in its labs. Quartz obtained some of those slides and published them here.
Is Apple a leader in AI research? Not according to most metrics. But many consider open research to be a way of recruiting top talent in AI, so we might see more papers and talks in the future.
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Elon Musk (and 350 Experts) Predict Exactly When Artificial … – Inc. – Inc.com
Posted: at 5:16 pm
Given the speed at which researchers are advancing artificial intelligence, the question has become not if A.I. will become smarter than its human creators, but when?
A team of researchers from Yale University and Oxford's Future of Humanity Institute recently set off to determine the answer. During May and June of 2016, they polled hundreds of industry leaders and academics to get their predictions for when A.I. will hit certain milestones.
The findings, which the team published in a study last week: A.I. will be capable of performing any task as well or better than humans--otherwise known as high-level machine intelligence--by 2060 and will overtake all human jobs by 2136. Those results are based on the 352 experts who responded.
Monday night, Elon Musk, who's been a consistent A.I. fear monger, chimed in on Twitter.
The entrepreneur followed up his tweet with an ominous, "I hope I'm wrong." Musk has been a vocal critic of A.I. the past several years, painting nightmare scenarios in which it becomes weaponized or outsmarts humans and leads to their extinction. He co-founded OpenAI, a non-profit that aims to ensure A.I. is used for good, in 2015.
Musk's own firm, Tesla, is one of the companies leading the charge in creating self-driving vehicles. The trucking and taxi industries employ about 2 million Americans, all of whom could soon find their jobs obsolete should vehicles become fully autonomous.
The experts polled in the study predicted that A.I. would become better at driving trucks than humans in 2027. The surveys were completed before robotics startup Otto successfully sent a self-driving truck on a 120-mile journey in October.
A.I. will surpass humans in a number of other milestones, the experts suggested: translating languages (2024), writing high-school level essays (2026), and performing surgeries (2053). They estimated that it would be able to write a New York Times bestseller in 2049.
In May, Google's AlphaGo machine won a game of Go against China's Ke Jie, widely considered to be the world's best player. An A.I. system created by scientists at Carnegie Mellon won $2 million from top poker players in a tournament in January.
It's worth noting that the predicted timelines did not vary based on the experts' levels of experience with artificial intelligence. One variable that did correlate with the predictions was the location: North American experts thought A.I. would outperform humans on all tasks within 74 years, while experts in Asia thought this would take only 30 years. The researchers who published the study didn't provide a potential explanation for the discrepancy.
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Career site Workey raises $8M to replace headhunters with artificial … – TechCrunch
Posted: at 5:16 pm
One of the ways companies fill their ranks with good employees is by scouting passive talent, or people who arent currently looking for new jobs but might be convinced with the right offer. This usually takes hours of networking, but a Tel Aviv-headquartered startup called Workey uses artificial intelligence to streamline the process by matching companies with potential candidates. Workey launched in the U.S. today and also announced that it has raised $8 million in Series A funding.
The round was led by PICO Partners and Magma VC and brings the total Workey has raised so far to $9.6 million, including its earlier seed funding. Workey will use the new capital to expand in the U.S., open an office in New York City, and hire people for its research and development and data science teams.
A LinkedIn study released last year found that recent college graduates are more likely to switch jobs at least twice before their early 30s than previous generations. Workey targets people who are interested in potential opportunities, but dont want to broadcast their curiosity to everyone, including their current employers. Once they sign up for the site, they create an anonymous profile that is used to find positions their background and skills qualify them for.
Workeys recommendation system then matches companies with promising candidates. If a company requests an introduction through the site, users can respond by revealing their full details. Otherwise, all rejections are anonymous. As an example, Workeys co-founders say Yahoo has found several candidates by spending 10 minutes a week on Workey.
Founded in 2015 by Ben Reuveni, Danny Shteinberg, and Amichai Schreiber, Workey has worked with more than 400 companies so far, including Yahoo, Amazon, Dell EMC, and Oracle. In a group interview by email, the trio told TechCrunch that the anonymous platform helps mitigates hiring bias, because companies dont see a candidates race, gender, ethnicity, or religion first. It also allows candidates to see how they stand in relation to the rest of the job market, which can help them during wage negotiations.
Another benefit is combatting the stigma associated with job seekers.
Like it or not, there is much truth to the belief that candidates who are currently working are more desirable than those who are out of a job and full-time job hunting, Workeys founders explained. Passive talent, those who are not actively looking but wouldnt want to miss out on their dream job, are often the most desirable candidates since they typically are already secure in their current position (likely because they perform them well).
Once they do decide to interview for a new job, Workey lets candidates track the status of their application, so they dont spend weeks in limbo waiting for an offer or rejection. The startup works mainly with tech companies right now, because it was invented by engineers for engineers, but can be adapted for other industries. Its free for job candidates and monetizes by charging companies a fee, but its founders claim that they potentially save thousands of dollars by using Workeys AI instead of headhunters or recruitment agencies.
Workey isnt the only career services startup that wants to use AI to streamline the recruitment process, which often takes months. Other companies that have developed AI tools to improve or replace headhunting, job searches, or interviews include Engage, FirstJob, Arya, and Mya. Though their services dont necessarily overlap with Workey right now, its a sign that Workeys competition is likely to increase soon. But its founders insist that one of the most exciting aspects of business today is that there is no future-proofing. Workey will continue to evolve and grow, with a continued investment in R&D to ensure that we provide users with the best possible matches enhancing their careers.
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Apple wants a piece of the artificial intelligence pie – Healthcare Dive
Posted: at 5:16 pm
Dive Brief:
AI is hot and it's no surprise Apple is looking to make a play in the space. At HIMSS17 in February, vendors including IBM Watson Health and Nant Health touted AIs potential to increase workflow and improve clinical trial matching, among other uses.
While the industry tries to wrap its collective head around what AI and machine learning are, there's been a flurrly of activity in the space. IBM Watson Health, the de facto spearhead of the AI movement in healthcare, has been on a partnering spree. Novartis and IBM Watson Healthannounced they will use patient data and cognitive computing to look into breast cancer outcomes.IBM Watson is also teaming up with CotaHealthcare and Hackensack Meridian Hospitalon a test AI-enabled decision support in cancer treatment.
With the shift to value-based payment models, providers are looking for ways to increase efficiencies and improve patient outcomes, and AI offers many opportunities to do such as streamlining diagnoses and treatments and providing clinical decision support. By 2021, the AI market in healthcare is expected to reach $6 billion, up from just $600 million three years ago.
Apples ResearchKit, which uses iPhones to collect health information and then makes the data available for research, is showing promise after scientists published data on seizures, asthma attacks and heart disease using the tool. While Apple still faces challenges applying ResearchKits results to a broader population (most consumers of Apple products are younger, well-off and well-educated), the company seems determined to carve out a niche in healthcare and AI could help its efforts.
While on the surface, facial recognition and parsing text don't directly relate to healthcare but natural language processing capabilities and image recognition do fit within areas of need for healthcare such as in medical notes or imaging.
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Meme-Gene Coevolution – Susan Blackmore
Posted: at 5:16 pm
Evolution and Memes: The human brain as a selective imitation device
Susan Blackmore
This article originally appeared in Cybernetics and Systems, Vol 32:1, 225-255, 2001, Taylor and Francis, Philadelphia, PA. Reproduced with permission.
Italian translation I memi e lo sviluppo del cervello, in KOS 211, aprile 2003, pp. 56-64.
German translation Evolution und Meme: Das menschliche Gehirn als selektiver Imitationsapparat , in: Alexander Becker et al. (Hg.): Gene, Meme und Gehirne. Geist und Gesellschaft als Natur, Frankfurt: Suhrkamp 2003 pp 49-89.
Abstract
The meme is an evolutionary replicator, defined as information copied from person to person by imitation. I suggest that taking memes into account may provide a better understanding of human evolution in the following way. Memes appeared in human evolution when our ancestors became capable of imitation. From this time on two replicators, memes and genes, coevolved. Successful memes changed the selective environment, favouring genes for the ability to copy them. I have called this process memetic drive. Meme-gene coevolution produced a big brain that is especially good at copying certain kinds of memes. This is an example of the more general process in which a replicator and its replication machinery evolve together. The human brain has been designed not just for the benefit of human genes, but for the replication of memes. It is a selective imitation device.
Some problems of definition are discussed and suggestions made for future research.
The concept of the meme was first proposed by Dawkins (1976) and since that time has been used in discussions of (among other things) evolutionary theory, human consciousness, religions, myths and mind viruses (e.g. Dennett 1991, 1995, Dawkins 1993, Brodie 1996, Lynch 1996). I believe, however, that the theory of memes has a more fundamental role to play in our understanding of human nature. I suggest that it can give us a new understanding of how and why the human brain evolved, and why humans differ in important ways from all other species. In outline my hypothesis is as follows.
Everything changed in human evolution when imitation first appeared because imitation let loose a new replicator, the meme. Since that time, two replicators have been driving human evolution, not one. This is why humans have such big brains, and why they alone produce and understand grammatical language, sing, dance, wear clothes and have complex cumulative cultures. Unlike other brains, human brains had to solve the problem of choosing which memes to imitate. In other words they have been designed for selective imitation.
This is a strong claim and the purpose of this paper is first to explain and defend it, second to explore the implications of evolution operating on two replicators, and third to suggest how some of the proposals might be tested. One implication is that we have underestimated the importance of imitation.
The new replicator
The essence of all evolutionary processes is that they involve some kind of information that is copied with variation and selection. As Darwin (1859) first pointed out, if you have creatures that vary, and if there is selection so that only some of those creatures survive, and if the survivors pass on to their offspring whatever it was that helped them survive, then those offspring must, on average, be better adapted to the environment in which that selection took place than their parents were. It is the inevitability of this process that makes it such a powerful explanatory tool. If you have the three requisites variation, selection and heredity, then you must get evolution. This is why Dennett calls the process the evolutionary algorithm. It is a mindless procedure which produces Design out of Chaos without the aid of Mind (Dennett 1995, p 50).
This algorithm depends on something being copied, and Dawkins calls this the replicator. A replicator can therefore be defined as any unit of information which is copied with variations or errors, and whose nature influences its own probability of replication (Dawkins 1976). Alternatively we can think of it as information that undergoes the evolutionary algorithm (Dennett 1995) or that is subject to blind variation with selective retention (Campbell 1960), or as an entity that passes on its structure largely intact in successive replications (Hull, 1988).
The most familiar replicator is the gene. In biological systems genes are packaged in complex ways inside larger structures, such as organisms. Dawkins therefore contrasted the genes as replicators with the vehicles that carry them around and influence their survival. Hull prefers the term interactors for those entities that interact as cohesive wholes with their environments and cause replication to be differential (Hull 1988). In either case selection may take place at the level of the organism (and arguably at other levels) but the replicator is the information that is copied reasonably intact through successive replications and is the ultimate beneficiary of the evolutionary process.
Note that the concept of a replicator is not restricted to biology. Whenever there is an evolutionary process (as defined above) then there is a replicator. This is the basic principle of what has come to be known as Universal Darwinism (Dawkins 1976, Plotkin 1993) in which Darwinian principles are applied to all evolving systems. Other candidates for evolving systems with their own replicators include the immune system, neural development, and trial and error learning (e.g. Calvin 1996, Edelman 1989, Plotkin 1993, Skinner 1953).
The new replicator I refer to here is the meme; a term coined in 1976 by Dawkins. His intention was to illustrate the principles of Universal Darwinism by providing a new example of a replicator other than the gene. He argued that whenever people copy skills, habits or behaviours from one person to another by imitation, a new replicator is at work.
We need a name for the new replicator, a noun that conveys the idea of a unit of cultural transmission, or a unit of imitation. Mimeme comes from a suitable Greek root, but I want a monosyllable that sounds a bit like gene. I hope my classicist friends will forgive me if I abbreviate mimeme to meme. Examples of memes are tunes, ideas, catch-phrases, clothes fashions, ways of making pots or of building arches. Just as genes propagate themselves in the gene pool by leaping from body to body via sperms or eggs, so memes propagate themselves in the meme pool by leaping from brain to brain via a process which, in the broad sense, can be called imitation. (Dawkins, 1976, p 192).
Dawkins now explains that he had modest, and entirely negative, intentions for his new term. He wanted to prevent his readers from thinking that the gene was necessarily the be-all and end-all of evolution which all adaptations could be said to benefit (Dawkins, 1999, p xvi) and make it clear that the fundamental unit of natural selection is the replicator any kind of replicator. Nevertheless, he laid the groundwork for memetics. He likened some memes to parasites infecting a host, especially religions which he termed viruses of the mind (Dawkins, 1993), and he showed how mutually assisting memes will group together into co-adapted meme complexes (or memeplexes) often propagating themselves at the expense of their hosts.
Dennett subsequently used the concept of memes to illustrate the evolutionary algorithm and to discuss personhood and consciousness in terms of memes. He stressed the importance of asking Cui bono? or who benefits? The ultimate beneficiary of an evolutionary process, he stressed, is whatever it is that is copied; i.e. the replicator. Everything else that happens, and all the adaptations that come about, are ultimately for the sake of the replicators.
This idea is central to what has come to be known as selfish gene theory, but it is important to carry across this insight into dealing with any new replicator. If memes are truly replicators in their own right then we should expect things to happen in human evolution which are not for the benefit of the genes, nor for the benefit of the people who carry those genes, but for the benefit of the memes which those people have copied. This point is absolutely central to understanding memetics. It is this which divides memetics from closely related theories in sociobiology (Wilson 1975) and evolutionary psychology (e.g. Barkow, Cosmides & Tooby 1992, Pinker 1997). Dawkins complained of his colleagues that In the last analysis they wish always to go back to biological advantage (Dawkins 1976 p 193). This is true of theories in evolutionary psychology but also of most of the major theories of gene-culture coevolution. For example, Wilson famously claimed that the genes hold culture on a leash (Lumsden & Wilson 1981). More recently he has conceded that the term meme has won against its various competitors but he still argues that memes (such as myths and social contracts) evolved over the millennia because they conferred a survival advantage on the genes, not simply because of advantages to themselves (Wilson 1998). Other theories such as the mathematical models of Cavalli-Sforza and Feldman (1981) and Lumsden and Wilson (1981) take inclusive fitness (advantage to genes) as the final arbiter, as does Durham (1991) who argues that organic and cultural selection work on the same criterion and are complementary. Among the few exceptions are Boyd and Richersons Dual Inheritance model (1985) which includes the concept of cultural fitness, and Deacons (1997) coevolutionary theory in which language is likened to a parasitic organism with adaptations that evolved for its own replication, not for that of its host.
With these exceptions, the genes remain the bottom line in most such theories, even though maladaptive traits (that is, maladaptive to the genes) can arise, and may even thrive under some circumstances (Durham 1991, Feldman and Laland 1996). By contrast, if you accept that memes are a true replicator then you must consider the fitness consequences for memes themselves. This could make a big difference, and this is why I say that everything changed in evolution when memes appeared.
When was that? If we define memes as information copied by imitation, then this change happened when imitation appeared. I shall argue that should we do just that, but this will require some justification.
Problems of definition
If we had a universally agreed definition of imitation, we could define memes as that which is imitated (as Dawkins originally did). In that case we could say that, by definition, memes are transmitted whenever imitation occurs and, in terms of evolution, we could say that memes appeared whenever imitation did. Unfortunately there is no such agreement either over the definition of memes or of imitation. Indeed there are serious arguments over both definitions. I suggest that we may find a way out of these problems of definition by thinking about imitation in terms of evolutionary processes, and by linking the definitions of memes and imitation together.
In outline my argument is as follows. The whole point of the concept of memes is that the meme is a replicator. Therefore the process by which it is copied must be one that supports the evolutionary algorithm of variation, selection and heredity in other words, producing copies of itself that persist through successive replications and which vary and undergo selection. If imitation is such a process, and if other kinds of learning and social learning are not, then we can usefully tie the two definitions together. We can define imitation as a process of copying that supports an evolutionary process, and define memes as the replicator which is transmitted when this copying occurs.
Note that this is not a circular definition. It depends crucially on an empirical question is imitation in fact the kind of process that can support a new evolutionary system? If it is then there must be a replicator involved and we can call that replicator the meme. If not, then this proposal does not make sense. This is therefore the major empirical issue involved, and I shall return to it when I have considered some of the problems with our current definitions.
Defining the meme
The Oxford English Dictionary defines memes as follows meme (mi:m), n. Biol.(shortened from mimeme that which is imitated, after GENE n.) An element of a culture that may be considered to be passed on by non-genetic means, esp. imitation. This is clearly built on Dawkinss original conception and is clear as far as it goes. However, there are many other definitions of the meme, both formal and informal, and much argument about which is best. These definitions differ mainly on two key questions: (1) Whether memes exist only inside brains or outside of them as well, and (2) the methods by which memes may be transmitted.
The way we define memes is critical, not only for the future development of memetics as a science, but for our understanding of evolutionary processes in both natural and artificial systems. Therefore we need to get the definitions right. What counts as right, in my view, is a definition that fits the concept of the meme as a replicator taking part in a new evolutionary process. Any definition which strays from this concept loses the whole purpose and power of the idea of the meme indeed its whole reason for being. It is against this standard that I judge the various competing definitions, and my conclusion is that memes are both inside and outside of brains, and they are passed on by imitation. The rest of this section expands on that argument and can be skipped for the purposes of understanding the wider picture.
First there is the question of whether memes should be restricted to information stored inside peoples heads (such as ideas, neural patterns, memories or knowledge) or should include information available in behaviours or artefacts (such as speech, gestures, inventions and art, or information in books and computers).
In 1975, Cloak distinguished between the cultural instructions in peoples heads (which he called i-culture) and the behaviour, technology or social organisation they produce (which he called m-culture). Dawkins (1976) initially ignored this distinction, using the term meme to apply to behaviours and physical structures in a brain, as well as to memetic information stored in other ways (as in his examples of tunes, ideas and fashions). This is sometimes referred to as Dawkins A (Gatherer 1998). Later (Dawkins B) he decided that A meme should be regarded as a unit of information residing in a brain (Cloaks i-culture) (Dawkins 1982, p 109). This implies that the information in the clothes or the tunes does not count as a meme. But later still he says that memes can propagate themselves from brain to brain, from brain to book, from book to brain, from brain to computer, from computer to computer (Dawkins, 1986, p 158). Presumably they still count as memes in all these forms of storage not just when they are in a brain. So this is back to Dawkins A.
Dennett (1991, 1995) treats memes as information undergoing the evolutionary algorithm, whether they are in a brain, a book or some other physical object. He points out that copying any behaviour must entail neural change and that the structure of a meme is likely to be different in any two brains, but he does not confine memes to these neural structures. Durham (1991) also treats memes as information, again regardless of how they are stored. Wilkins defines a meme as the least unit of sociocultural information relative to a selection process that has favourable or unfavourable selection bias that exceeds its endogenous tendency to change. (Wilkins 1998). This is based on Williamss now classic definition of the gene as any hereditary information for which there is a favorable or unfavorable selection bias equal to several or many times its rate of endogenous change. (Williams 1966, p 25). What is important here is that the memetic information survives intact long enough to be subject to selection pressures. It does not matter where and how the information resides.
In contrast, Delius (1989) describes memes as constellations of activated and non-activated synapses within neural memory networks (p 45) or arrays of modified synapses (p 54). Lynch (1991) defines them as memory abstractions or memory items, Grant (1990) as information patterns infecting human minds, and Plotkin as ideas or representations the internal end of the knowledge relationship (Plotkin 1993, p 215), while Wilson defines the natural elements of culture as the hierarchically arranged components of semantic memory, encoded by discrete neural circuits awaiting identification. (Wilson 1998, p 148). Closer to evolutionary principles, Brodie defines a meme as a unit of information in a mind whose existence influences events such that more copies of itself get created in other minds. (Brodie 1996, p 32), but this restricts memes to being in minds. Presumably, on all these latter definitions, memes cannot exist in books or buildings, so the books and buildings must be given a different role. This has been done, by using further distinctions, usually based on a more or less explicit analogy with genes.
Cloak (1975) explicitly likened his i-culture to the genotype and m-culture to the phenotype. Dennett (1995) also talks about memes and their phenotypic effects, though in a different way. The meme is internal (though not confined to brains) while the way it affects things in its environment (p 349), is its phenotype. In an almost complete reversal, Benzon (1996) likens pots, knives, and written words (Cloaks m-culture) to the gene; and ideas, desires and emotions (i-culture) to the phenotype. Gabora (1997) likens the genotype to the mental representation of a meme, and the phenotype to its implementation. Delius (1989), having defined memes as being in the brain, refers to behaviour as the memes phenotypic expression, while remaining ambiguous about the role of the clothes fashions he discusses. Grant (1990) defines the memotype as the actual information content of a meme, and distinguishes this from its sociotype or social expression. He explicitly bases his memotype/sociotype distinction on the phenotype/genotype distinction. All these distinctions are slightly different and it is not at all clear which, if any, is better.
The problem is this. If memes worked like genes then we should expect to find close analogies between the two evolutionary systems. But, although both are replicators, they work quite differently and for this reason we should be very cautious of meme-gene analogies. I suggest there is no clean equivalent of the genotype/phenotype distinction in memetics because memes are a relatively new replicator and have not yet created for themselves this highly efficient kind of system. Instead there is a messy system in which information is copied all over the place by many different means.
I previously gave the example of someone inventing a new recipe for pumpkin soup and passing it on to various relatives and friends (Blackmore 1999). The recipe can be passed on by demonstration, by writing the recipe on a piece of paper, by explaining over the phone, by sending a fax or e-mail, or (with difficulty) by tasting the soup and working out how it might have been cooked. It is easy to think up examples of this kind which make a mockery of drawing analogies with genotypes and phenotypes because there are so many different copying methods. Most important for the present argument, we must ask ourselves this question. Does information about the new soup only count as a meme when it is inside someones head or also when it is on a piece of paper, in the behaviour of cooking, or passing down the phone lines? If we answer that memes are only in the head then we must give some other role to these many other forms and, as we have seen, this leads to confusion.
My conclusion is this. The whole point of memes is to see them as information being copied in an evolutionary process (i.e. with variation and selection). Given the complexities of human life, information can be copied in myriad ways. We do a disservice to the basic concept of the meme if we try to restrict it to information residing only inside peoples heads as well as landing ourselves in all sorts of further confusions. For this reason I agree with Dennett, Wilkins, Durham and Dawkins A, who do not restrict memes to being inside brains. The information in this article counts as memes when it is inside my head or yours, when it is in my computer or on the journal pages, or when it is speeding across the world in wires or bouncing off satellites, because in any of these forms it is potentially available for copying and can therefore take part in an evolutionary process.
We may now turn to the other vexed definitional question the method by which memes are replicated. The dictionary definition gives a central place to imitation, both in explaining the derivation of the word meme and as the main way in which memes are propagated. This clearly follows Dawkinss original definition, but Dawkins was canny in saying imitation in the broad sense. Presumably he meant to include many processes which we may not think of as imitation but which depend on it, like direct teaching, verbal instruction, learning by reading and so on. All these require an ability to imitate. At least, learning language requires the ability to imitate sounds, and instructed learning and collaborative learning emerge later in human development than does imitation and arguably build on it (Tomasello, Kruger & Ratner 1993). We may be reluctant to call some of these complex human skills imitation. However, they clearly fit the evolutionary algorithm. Information is copied from person to person. Variation is introduced both by degradation due to failures of human memory and communication, and by the creative recombination of different memes. And selection is imposed by limitations on time, transmission rates, memory and other kinds of storage space. In this paper I am not going to deal with these more complex kinds of replication. Although they raise many interesting questions, they can undoubtedly sustain an evolutionary process and can therefore replicate memes. Instead I want to concentrate on skills at the simpler end of the scale, where it is not so obvious which kinds of learning can and cannot count as replicating memes.
Theories of gene-culture coevolution all differ in the ways their cultural units are supposed to be passed on. Cavalli-Sforza and Feldmans (1981) cultural traits are passed on by imprinting, conditioning, observation, imitation or direct teaching. Durhams (1991) coevolutionary model refers to both imitation and learning. Runciman (1998) refers to memes as instructions affecting phenotype passed on by both imitation and learning. Laland and Odling Smee (in press) argue that all forms of social learning are potentially capable of propagating memes. Among meme-theorists both Brodie (1996) and Ball (1984) include all conditioning, and Gabora (1997) counts all mental representations as memes regardless of how they are acquired.
This should not, I suggest, be just a matter of preference. Rather, we must ask which kinds of learning can and cannot copy information from one individual to another in such a way as to sustain an evolutionary process. For if information is not copied through successive replications, with variation and selection, then there is no new evolutionary process and no need for the concept of the meme as replicator. This is not a familiar way of comparing different types of learning so I will need to review some of the literature and try to extract an answer.
Communication and contagion
Confusion is sometimes caused over the term communication, so I just want to point out that most forms of animal communication (even the most subtle and complex) do not involve the copying of skills or behaviours from one individual to another with variation and selection. For example, when bees dance information about the location of food is accurately conveyed and the observing bees go off to find it, but the dance itself is not copied or passed on. So this is not copying a meme. Similarly when vervet monkeys use several different signals to warn conspecifics of different kinds of predator (Cheney and Seyfarth 1990), there is no copying of the behaviour. The behaviour acts as a signal on which the other monkeys act, but they do not copy the signals with variation and selection.
Yawning, coughing or laughter can spread contagiously from one individual to the next and this may appear to be memetic, but these are behaviours that were already known or in the animals repertoire, and are triggered by another animal performing them (Provine 1996). In this type of contagion there is no copying of new behaviours (but note that there are many other kinds of contagion (Levy & Nail, 1993; Whiten & Ham, 1992)). Communication of these kinds is therefore not even potentially memetic. Various forms of animal learning may be.
Learning
Learning is commonly divided into individual and social learning. In individual learning (including classical conditioning, operant conditioning, acquisition of motor skills and spatial learning) there is no copying of information from one animal to another. When a rat learns to press a lever for reward, a cat learns where the food is kept, or a child learns how to ride a skateboard, that learning is done for the individual only and cannot be passed on. Arguably such learning involves a replicator being copied and selected within the individual brain (Calvin 1996, Edelman 1989), but it does not involve copying between individuals. These types of learning therefore do not count as memetic transmission.
In social learning a second individual is involved, but in various different roles. Types of social learning include goal emulation, stimulus enhancement, local enhancement, and true imitation. The question I want to ask is which of these can and cannot sustain a new evolutionary process.
In emulation, or goal emulation, the learner observes another individual gaining some reward and therefore tries to obtain it too, using individual learning in the process, and possibly attaining the goal in quite a different way from the first individual (Tomasello 1993). An example is when monkeys, apes or birds observe each other getting food from novel containers but then get it themselves by using a different technique (e.g. Whiten & Custance 1996). This is social learning because two individuals are involved, but the second has only learned a new place to look for food. Nothing is copied from one animal to the other in such a way as to allow for the copying of variations and selective survival of some variants over others. So there is no new evolutionary process and no new replicator.
In stimulus enhancement the attention of the learner is drawn to a particular object or feature of the environment by the behaviour of another individual. This process is thought to account for the spread among British tits of the habit of pecking milk bottle tops to get at the cream underneath, which was first observed in 1921 and spread from village to village (Fisher and Hinde 1949). Although this looks like imitation, it is possible that once one bird had learned the trick others were attracted to the jagged silver tops and they too discovered (by individual learning) that there was cream underneath (Sherry & Galef 1984). If so, the birds had not learned a new skill from each other (they already knew how to peck), but only a new stimulus at which to peck. Similarly the spread of termite fishing among chimpanzees might be accounted for by stimulus enhancement as youngsters follow their elders around and are exposed to the right kind of sticks in proximity to termite nests. They then learn by trial and error how to use the sticks.
In local enhancement the learner is drawn to a place or situation by the behaviour of another, as when rabbits learn from each other not to fear the edges of railway lines in spite of the noise of the trains. The spread of sweet-potato washing in Japanese macaques may have been through stimulus or local enhancement as the monkeys followed each other into the water and then discovered that washed food was preferable (Galef 1992).
If this is the right explanation for the spread of these behaviours we can see that there is no new evolutionary process and no new replicator, for there is nothing that is copied from individual to individual with variation and selection. This means there can be no cumulative selection of more effective variants. Similarly, Boyd and Richerson (in press) argue that this kind of social learning does not allow for cumulative cultural change.
Most of the population-specific behavioural traditions studied appear to be of this kind, including nesting sites, migration routes, songs and tool use, in species such as wolves, elephants, monkeys, monarch butterflies, and many kinds of birds (Bonner 1980). For example, oyster catchers use two different methods for opening mussels according to local tradition but the two methods do not compete in the same population in other words there is no differential selection of variants within a given population. Tomasello, Kruger and Ratner (1993) argue that many chimpanzee traditions are also of this type. Although the behaviours are learned population-specific traditions they are not cultural in the human sense of that term because they are not learned by all or even most of the members of the group, they are learned very slowly and with wide individual variation, and most telling they do not show an accumulation of modifications over generations. That is, they do not show the cultural ratchet effect precluding the possibility of humanlike cultural traditions that have histories.
There may be exceptions to this. Whiten et al. (1999) have studied a wide variety of chimpanzee behaviours and have found limited evidence that such competition between variants does occur within the same group. For example, individuals in the same group use two different methods for catching ants on sticks, and several ways of dealing with ectoparasites while grooming. However, they suggest that these require true imitation for their perpetuation.
Imitation
True imitation is more restrictively defined, although there is still no firm agreement about the definition (see Zentall 1996, Whiten 1999). Thorndike (1898), originally defined imitation as learning to do an act from seeing it done. This means that one animal must acquire a novel behaviour from another so ruling out the kinds of contagion noted above. Whiten and Ham (1992), whose definition is widely used, define imitation as learning some part of the form of a behaviour from another individual. Similarly Heyes (1993) distinguishes between true imitation learning something about the form of behaviour through observing others, from social learning learning about the environment through observing others (thus ruling out stimulus and local enhancement).
True imitation is much rarer than individual learning and other forms of social learning. Humans are extremely good at imitation; starting almost from birth, and taking pleasure in doing it. Meltzoff, who has studied imitation in infants for more than twenty years, calls humans the consummate imitative generalist (Meltzoff, 1996) (although some of the earliest behaviours he studies, such as tongue protrusion, might arguably be called contagion rather than true imitation). Just how rare imitation is has not been answered. There is no doubt that some song birds learn their songs by imitation, and that dolphins are capable of imitating sounds as well as actions (Bauer & Johnson, 1994; Reiss & McCowan, 1993). There is evidence of imitation in the grey parrot and harbour seals. However, there is much dispute over the abilities of non-human primates and other mammals such as rats and elephants (see Byrne & Russon 1998; Heyes & Galef 1996, Tomasello, Kruger & Ratner 1993, Whiten 1999).
Many experiments have been done on imitation and although they have not been directly addressed at the question of whether a new replicator is involved, they may help towards an answer. For example, some studies have tried to find out how much of the form of a behaviour is copied by different animals and by children. In the two-action method a demonstrator uses one of two possible methods for achieving a goal (such as opening a specially designed container), while the learner is observed to see which method is used (Whiten et al. 1996; Zentall 1996). If a different method is used the animal may be using goal emulation, but if the same method is copied then true imitation is involved. Evidence of true imitation has been claimed using this method in budgerigars, pigeons and rats, as well as enculturated chimpanzees and children (Heyes and Galef 1996). Capuchin monkeys have recently been found to show limited ability to copy the demonstrated method (Custance, Whiten & Fredman 1999).
Other studies explore whether learners can copy a sequence of actions and their hierarchical structure (Whiten 1999). Byrne and Russon (1998) distinguish action level imitation (in which a sequence of actions is copied in detail) from program level imitation (in which the subroutine structure and hierarchical layout of a behavioural program is copied). They argue that other great apes may be capable of program level imitation although humans have a much greater hierarchical depth. Such studies are important for understanding imitation, but they do not directly address the questions at issue here that is, does the imitation entail an evolutionary process? Is there a new replicator involved?
To answer this we need new kinds of research directed at finding out whether a new evolutionary process is involved when imitation, or other kinds of social learning, take place. This might take two forms. First there is the question of copying fidelity. As we have seen, a replicator is defined as an entity that passes on its structure largely intact in successive replications. So we need to ask whether the behaviour or information is passed on largely intact through several replications. For example, in the wild, is there evidence of tool use, grooming techniques or other socially learned behaviours being passed on through a series of individuals, rather than several animals learning from one individual but never passing the skill on again? In experimental situations one animal could observe another, and then act as model for a third and so on (as in the game of Chinese whispers or telephone). We might not expect copying fidelity to be very high, but unless the skill is recognisably passed on through more than one replication then we do not have a new replicator i.e. there is no need for the concept of the meme.
Second, is there variation and selection? The examples given by Whiten et al. (1999) suggest that there can be. We might look for other examples where skills are passed to several individuals, these individuals differ in the precise way they carry out the skill, and some variants are more frequently or reliably passed on again. For this is the basis of cumulative culture. Experiments could be designed to detect the same process occurring in artificial situations. Such studies would enable us to say just which processes, in which species, are capable of sustaining an evolutionary process with a new replicator. Only when this is found can we usefully apply the concept of the meme.
If such studies were done and it turned out that, by and large, what we have chosen to call imitation can sustain cumulative evolution while other kinds of social learning cannot, then we could easily tie the definitions of memes and imitation together so that what counts as a meme is anything passed on by imitation, and wherever you have imitation you have a meme.
In the absence of such research we may not be justified in taking this step, and some people may feel that it would not do justice to our present understanding of imitation. Nevertheless, for the purposes of this paper at least, that is what I propose. The advantage is that it allows me to use one word imitation to describe a process by which memes are transmitted. If you prefer, for imitation read a kind of social learning which is capable of sustaining an evolutionary process with a new replicator.
This allows me to draw the following conclusion. Imitation is restricted to very few species and humans appear to be alone in being able to imitate a very wide range of sounds and behaviours. This capacity for widespread generalised imitation must have arisen at some time in our evolutionary history. When it did so, a new replicator was created and the process of memetic evolution began. This, I suggest, was a crucial turning point in human evolution. I now want to explore the consequences of this transition and some of the coevolutionary processes that may have occurred once human evolution was driven by two replicators rather than one. One consequence, I suggest, was a rapid increase in brain size.
The big human brain
Humans have abilities that seem out of line with our supposed evolutionary past as hunter-gatherers, such as music and art, science and mathematics, playing chess and arguing about our evolutionary origins. As Cronin puts it, we have a brain surplus to requirements, surplus to adaptive needs (Cronin, 1991, p 355). This problem led Wallace to argue, against Darwin, that humans alone have a God-given intellectual and spiritual nature (see Cronin 1991). Williams (1966) also struggled with the problem of mans cerebral hypertrophy, unwilling to accept that advanced mental capacities have ever been directly favoured by selection or that geniuses leave more children.
Humans have an encephalisation quotient of about 3 relative to other primates. That is, our brains are roughly three times as large when adjusted for body weight (Jerison 1973). The increase probably began about 2.5 million years ago in the australopithecines, and was completed about 100,000 years ago by which time all living hominids had brains about the same size as ours (Leakey, 1994; Wills, 1993). Not only is the brain much bigger than it was, but it appears to have been drastically reorganised during what is, in evolutionary terms, a relatively short time (Deacon 1997). The correlates of brain size and structure have been studied in many species and are complex and not well understood (Harvey & Krebs 1990). Nevertheless, the human brain stands out. The problem is serious because of the very high cost (in energy terms) of both producing a large brain during development, and of running it in the adult, as well as the dangers entailed in giving birth. Pinker asks Why would evolution ever have selected for sheer bigness of brain, that bulbous, metabolically greedy organ? Any selection on brain size itself would surely have favored the pinhead. (1994, p 363).
Early theories to explain the big brain focused on hunting and foraging skills, but predictions have not generally held up and more recent theories have emphasised the complexity and demands of the social environment (Barton & Dunbar 1997). Chimpanzees live in complex social groups and it seems likely that our common ancestors did too. Making and breaking alliances, remembering who is who to maintain reciprocal altruism, and outwitting others, all require complex and fast decision making and good memory. The Machiavellian Hypothesis emphasises the importance of deception and scheming in social life and suggests that much of human intelligence has social origins (Byrne & Whiten 1988; Whiten & Byrne 1997). Other theories emphasise the role of language (Deacon 1997, Dunbar 1996).
There are three main differences between this theory and previous ones. First, this theory entails a definite turning point the advent of true imitation which created a new replicator. On the one hand this distinguishes it from theories of continuous change such as those based on improving hunting or gathering skills, or on the importance of social skills and Machiavellian intelligence. On the other hand it is distinct from those which propose a different turning point, such as Donalds (1991) three stage coevolutionary model or Deacons (1997) suggestion that the turning point was when our ancestors crossed the Symbolic Threshold.
Second, both Donald and Deacon emphasise the importance of symbolism or mental representations in human evolution. Other theories also assume that what makes human culture so special is its symbolic nature. This emphasis on symbolism and representation is unnecessary in the theory proposed here. Whether behaviours acquired by imitation (i.e. memes) can be said to represent or symbolise anything is entirely irrelevant to their role as replicators. All that matters is whether they are replicated or not.
Third, the theory has no place for the leash metaphor of sociobiology, or for the assumption, common to almost all versions of gene-culture coevolution, that the ultimate arbiter is inclusive fitness (i.e. benefit to genes). In this theory there are two replicators, and the relationships between them can be cooperative, competitive, or anything in between. Most important is that memes compete with other memes and produce memetic evolution, the results of which then affect the selection of genes. On this theory we can only understand the factors affecting gene selection when we understand their interaction with memetic selection.
In outline the theory is this. The turning point in hominid evolution was when our ancestors began to imitate each other, releasing a new replicator, the meme. Memes then changed the environment in which genes were selected, and the direction of change was determined by the outcome of memetic selection. Among the many consequences of this change was that the human brain and vocal tract were restructured to make them better at replicating the successful memes.
The origins of imitation
We do not know when and how imitation originated. In one way it is easy to see why natural selection would have favoured social learning. It is a way of stealing the products of someone elses learning i.e. avoiding the costs and risks associated with individual learning though at the risk of acquiring outdated or inappropriate skills. Mathematical modelling has shown that this is worthwhile if the environment is variable but does not change too fast (Richerson and Boyd 1992). Similar analyses have been used in economics to compare the value of costly individual decision making against cheap imitation (Conlisk 1980).
As we have seen, other forms of social learning are fairly widespread, but true imitation occurs in only a few species. Moore (1996) compares imitation in parrots, great apes and dolphins and concludes that they are not homologous and that imitation must have evolved independently at least three times. In birds imitation probably evolved out of song mimicry, but in humans it did not. We can only speculate about what the precursors to human imitation may have been, but likely candidates include general intelligence and problem solving ability, the beginnings of a theory of mind or perspective taking, reciprocal altruism (which often involves strategies like tit-for-tat that entail copying what the other person does), and the ability to map observed actions onto ones own.
The latter sounds very difficult to achieve involving transforming the visual input of a seen action from one perspective into the motor instructions for performing a similar action oneself. However, mirror neurons in monkey premotor cortex appear to belong to a system that does just this. The same neurons fire when the monkey performs a goal-directed action itself as when it sees another monkey perform the same action, though Gallese and Goldman (1998) believe this system evolved for predicting the goals and future actions of others, rather than for imitation. Given that mirror neurons occur in monkeys, it seems likely that our ancestors would have had them, making the transition to true imitation more likely.
We also do not know when that transition occurred. The first obvious signs of imitation are the stone tools made by Homo habilis about 2.5 million years ago, although their form did not change very much for a further million years. It seems likely that less durable tools were made before then; possibly carrying baskets, slings, wooden tools and so on. Even before that our ancestors may have imitated ways of carrying food, catching game or other behaviours. By the time these copied behaviours were widespread the stage was set for memes to start driving genes. I shall take a simple example and try to explain how the process might work.
Memetic drive
Let us imagine that a new skill begins to spread by imitation. This might be, for example, a new way of making a basket to carry food. The innovation arose from a previous basket type, and because the new basket holds slightly more fruit it is preferable. Other people start copying it and the behaviour and the artefact both spread. Note that I have deliberately chosen a simple meme (or small memeplex) to illustrate the principle; that is the baskets and the skills entailed in making them. In practice there would be complex interactions with other memes but I want to begin simply.
Now anyone who does not have access to the new type of basket is at a survival disadvantage. A way to get the baskets is to imitate other people who can make them, and therefore good imitators are at an advantage (genetically). This means that the ability to imitate will spread. If we assume that imitation is a difficult skill (as indeed it seems to be) and requires a slightly larger brain, then this process alone can already produce an increase in brain size. This first step really amounts to no more than saying that imitation was selected for because it provides a survival advantage, and once the products of imitation spread, then imitation itself becomes ever more necessary for survival. This argument is a version of the Baldwin effect (1896) which applies to any kind of learning: once some individuals become able to learn something, those who cannot are disadvantaged and genes for the ability to learn therefore spread. So this is not specifically a memetic argument.
However, the presence of memes changes the pressures on genes in new ways. The reason is that memes are also replicators undergoing selection and as soon as there are sufficient memes around to set up memetic competition, then meme-gene coevolution begins. Let us suppose that there are a dozen different basket types around that compete with each other. Now it is important for any individual to choose the right basket to copy, but which is that? Since both genes and memes are involved we need to look at the question from both points of view.
From the genes point of view the right decision is the basket that increases inclusive fitness i.e. the decision that improves the survival chances of all the genes of the person making the choice. This will probably be the biggest, strongest, or easiest basket to make. People who copy this basket will gather more food, and ultimately be more likely to pass on the genes that were involved in helping them imitate that particular basket. In this way the genes, at least to some extent, track changes in the memes.
From the memes point of view the right decision is the one that benefits the basket memes themselves. These memes spread whenever they get the chance, and their chances are affected by the imitation skills, the perceptual systems and the memory capacities (among other things) of the people who do the copying. Now, let us suppose that the genetic tracking has produced people who tend to imitate the biggest baskets because over a sufficiently long period of time larger artefacts were associated with higher biological success. This now allows for the memetic evolution of all sorts of new baskets that exploit that tendency; especially baskets that look big. They need not actually be big, or well made, or very good at doing their job but as long as they trigger the genetically acquired tendency to copy big baskets then they will do well, regardless of their consequence for inclusive fitness. The same argument would apply if the tendency was to copy flashy-looking baskets, solid baskets, or whatever. So baskets that exploit the current copying tendencies spread at the expense of those that do not.
This memetic evolution now changes the situation for the genes which have, as it were, been cheated and are no longer effectively tracking the memetic change. Now the biological survivors will be the people who copy whatever it is about the current baskets that actually predicts biological success. This might be some other feature, such as the materials used, the strength, the kind of handle, or whatever and so the process goes on. This process is not quite the same as traditional gene-culture evolution or the Baldwin effect. The baskets are not just aspects of culture that have appeared by accident and may or may not be maladaptive for the genes of their carriers. They are evolving systems in their own right, with replicators whose selfish interests play a role in the outcome.
I have deliberately chosen a rather trivial example to make the process clear; the effects are far more contentious, as we shall see, when they concern the copying of language, or of seriously detrimental activities.
Whom to imitate
Another strategy for genes might be to constrain whom, rather than what, is copied. For example, a good strategy would be to copy the biologically successful. People who tended, other things being equal, to copy those of their acquaintances who had the most food, the best dwelling space, or the most children would, by and large, copy the memes that contributed to that success and so be more likely to succeed themselves. If there was genetic variation such that some people more often copied their biologically successful neighbours, then their genes would spread and the strategy copy the most successful would, genetically, spread through the population. In this situation (as I have suggested above) success is largely a matter of being able to acquire the currently important memes. So this strategy amounts to copying the best imitators. I shall call these people meme fountains, a term suggested by Dennett (1998) to refer to those who are especially good at imitation and who therefore provide a plentiful source of memes both old memes they have copied and new memes they have invented by building on, or combining, the old.
Now we can look again from the memes point of view. Any memes that got into the repertoire of a meme fountain would thrive regardless of their biological effect. The meme fountain acquires all the most useful tools, hunting skills, fire-making abilities and his genes do well. However, his outstanding imitation ability means that he copies and adapts all sorts of other memes as well. These might include rain dances, fancy clothes, body decoration, burial rites or any number of other habits that may not contribute to his genetic fitness. Since many of his neighbours have the genetically in-built tendency to copy him these memes will spread just as well as the ones that actually aid survival.
Whole memetic lineages of body decoration or dancing might evolve from such a starting point. Taking dancing as an example, people will copy various competing dances and some dances will be copied more often than others. This memetic success may depend on whom is copied, but also on features of the dances, such as memorability, visibility, interest and so on features that in turn depend on the visual systems and memories of the people doing the imitation. As new dances spread to many people, they open up new niches for further variations on dancing to evolve. Any of these memes that get their hosts to spend lots of time dancing will do better, and so, if there is no check on the process, people will find themselves dancing more and more.
Switching back to the genes point of view, the problem is that dancing is costly in terms of time and energy. Dancing cannot now be un-evolved but its further evolution will necessarily be constrained. Someone who could better discriminate between the useful memes and the energy-wasting memes would leave more descendants than someone who could not. So the pressure is on to make more and more refined discriminations about what and whom to imitate. And crucially the discriminations that have to be made depend upon the past history of memetic as well as genetic evolution. If dancing had never evolved there would be no need for genes that selectively screened out too much dance-imitation. Since it did there is. This is the crux of the process I have called memetic driving. The past history of memetic evolution affects the direction that genes must take to maximise their own survival.
We now have a coevolutionary process between two quite different replicators that are closely bound together. To maximise their success the genes need to build brains that are capable of selectively copying the most useful memes, while not copying the useless, costly or harmful ones. To maximise their success the memes must exploit the brains copying machinery in any way they can, regardless of the effects on the genes. The result is a mass of evolving memes, some of which have thrived because they are useful to the genes, and some of which have thrived in spite of the fact that they are not and a brain that is designed to do the job of selecting which memes are copied and which are not. This is the big human brain. Its function is selective imitation and its design is the product of a long history of meme-gene coevolution.
Whom to mate with
There is another twist to this argument; sexual selection for the ability to imitate. In general it will benefit females to mate with successful males and, in this imagined human past, successful males are those who are best at imitating the currently important memes. Sexual selection might therefore amplify the effects of memetic drive. A runaway process of sexual selection could then take off.
For example, let us suppose that at some particular time the most successful males were the meme fountains. Their biological success depended on their ability to copy the best tools or firemaking skills, but their general imitation ability also meant they wore the most flamboyant clothes, painted the most detailed paintings, or hummed the favourite tunes. In this situation mating with a good painter would be advantageous. Females who chose good painters would begin to increase in the population and this in turn would give the good painters another advantage, quite separate from their original biological advantage. That is, with female choice now favouring good painters, the offspring of good painters would be more likely to be chosen by females and so have offspring themselves. This is the crux of runaway sexual selection and we can see how it might have built on prior memetic evolution.
Miller (1998, 1999) has proposed that artistic ability and creativity have been sexually selected as courtship displays to attract women, and has provided many examples, citing evidence that musicians and artists are predominantly male and at their most productive during young adulthood. However, there are differences between his theory and the one proposed here. He does not explain how or why the process might have begun whereas on this theory the conditions were created by the advent of imitation and hence of memetic evolution. Also on his theory the songs, dances or books act as display in sexual selection, but the competition between them is not an important part of the process. On the theory proposed here, memes compete with each other to be copied by both males and females, and the outcome of that competition determines the direction taken both by the evolution of the memes and of the brains that copy them.
Whether this process has occurred or not is an empirical question. But note that I have sometimes been misunderstood as basing my entire argument on sexual selection of good imitators (Aunger, in press). In fact the more fundamental process of memetic drive might operate with or without the additional effects of sexual selection.
The coevolution of replicators with their replication machinery
Memetic driving of brain design can be seen as an example of a more general evolutionary process. That is, the coevolution of a replicator along with the machinery for its replication. The mechanism is straightforward. As an example, imagine a chemical soup in which different replicators occur, some together with coenzymes or other replicating machinery, and some without. Those which produce the most numerous and long lived copies of themselves will swamp out the rest, and if this depends on being associated with better copying machinery then both the replicator and the machinery will thrive.
Something like this presumably happened on earth long before RNA and DNA all but eliminated any competitors (Maynard Smith & Szathmry 1995). DNAs cellular copying machinery is now so accurate and reliable that we tend to forget it must have evolved from something simpler. Memes have not had this long history behind them. The new replicator is, as Dawkins (1976 p 192) puts it, still drifting clumsily about in its primeval soup the soup of human culture. Nevertheless we see the same general process happening as we may assume once happened with genes. That is, memes and the machinery for copying them are improving together.
The big brain is just the first step. There have been many others. In each case, high quality memes outperform lower quality memes and their predominance favours the survival of the machinery that copies them. This focuses our attention on the question of what constitutes high quality memes. Dawkins (1976) suggested fidelity, fecundity and longevity.
This is the basis for my argument about the origins of language (Blackmore 1999, in press). In outline it is this. Language is a good way of creating memes with high fecundity and fidelity. Sound carries better than visual stimuli to several people at once. Sounds digitised into words can be copied with higher fidelity than continuously varying sounds. Sounds using word order open up more niches for memes to occupy and so on. In a community of people copying sounds from each other memetic evolution will ensure that the higher quality sounds survive. Memetic driving then favours brains and voices that are best at copying those memes. This is why our brains and bodies became adapted for producing language. On this theory the function of language ability is not primarily biological but memetic. The copying machinery evolved along with the memes it copies.
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Nutrients in Food Supplements: the European Court of Justice Rules on Boundaries of National Legislation Imposing … – Lexology (registration)
Posted: at 5:13 pm
On 27 April 2017, the Court of Justice of the European Union issued a judgement upon request of the Tribunal de Grande Instance of Perpignan (France) for a preliminary ruling under Article 267 TFEU, made by decision of 5 August 2015.
The request concerned the interpretation of Directive (EC) 2002/46 on the approximation of laws of the Member States relating to food supplements and Articles 28 and 30 of the Treaty on the Functioning of the European Union (TFEU) on the free movement of goods.
The question arose in the context of a criminal proceeding brought against Noria Distribution SARL (Noria Distribution) for putting on sale or sold food supplements not authorised in France because they exceeded the maximum daily doses of vitamins and minerals which may be used for the manufacture of such food supplements, as set forth in the inter-ministerial order of 9 May 2006 on nutrients (Order).
Background of the case
Noria Distribution, a French company that markets food supplements in the European Union, is prosecuted in France for having sold food supplements containing vitamins and minerals in quantities exceeding the maximum daily doses provided in the Order. The Company does not substantially contest the violation of French Law, but it claims that the Order on which the criminal proceeding is based is not compatible with the European Law.
According to Article 5 of Decree No 2006/352 transposing Directive (EC) 2002/46 under French Law ("Decree"), vitamins and minerals can be used in the manufacture of food supplements only under the conditions set forth in an implementing inter-ministerial Order. The Order provides a positive list of vitamins and minerals that can be used in the manufacture of food supplements and establishes the maximum daily doses that must not be exceeded in the context of that use. It follows that food supplements with content of nutrients exceeding the limit set forth in the Decree, cannot be legally placed in the French market even though they are legally sold in other European Member States. Although the Decree provides a simplified "mutual recognition" procedure, this shall not apply to food supplements containing vitamins and minerals.
Question referred to the Court
The Tribunal de Grande Instance of Perpignan, unsure on the conformity of national legislation with Directive (EC) 2002/46, decided to stay the proceeding and refer to the Court a request for preliminary ruling. In particular, the referring Court inquires:
Findings of the Court
As to the first question, the Court of Justice of the European Union ("CJEU") observes that until the adoption by the European Commission of an act setting forth the maximum amount of vitamins and minerals to be used in foodstuffs, Member States remain competent to adopt the legislation concerning these amounts. However, in the exercise of that competence, they shall comply with the rules concerning the free movement of goods, as well as with principles laid down in Article 5(1) and (2) of Directive (EU) 2002/46, including the requirement for a risk assessment based on generally accepted scientific data.
According to the CJEU, the French Decree constitutes a measure having an effect equivalent to a quantitative restriction, since it prohibits the marketing of food supplements exceeding the maximum limits of nutrients even if they are lawfully manufactured or marketed in another Member State. According to the CJEU's case law, measures having equivalent effect to a quantitative restriction are justified when two requirements are fulfilled. First, national rules provide a procedure enabling economic operators to obtain the authorisation to market food supplements non-compliant with these limits and the procedure is: easily accessible; can be completed within a reasonable time; and, in case of refusal, the decision can be challenged before the courts. Secondly, the application to obtain the authorisation to market those food supplements may be refused by the competent national authorities only if those supplements pose a genuine risk to public health.
Since the French legislation forbids the marketing of food supplements whose content in nutrients exceeds the upper limits set by the legislation without providing a procedure of mutual recognition, the restriction does not seem justified under the European Law.
With reference to the second question, concerning the method used to set maximum amounts of vitamins, the CJEU affirms that it shall be based on a scientific risk assessment based on generally accepted scientific data and it must be carried out on a case-by-case basis. It follows that a method which consists of setting those amounts without taking into account all of these elements, is not compatible with rules on free movement of goods.
Finally, addressing the third question, the CJEU points out that by requiring that the assessment is based on generally accepted scientific data, Article 5(1) of Directive (EC) 2002/46 intends that the assessment shall be based on reliable scientific data, regardless of whether they are national or international. It follows that if recent and reliable international scientific data are available on the date on which the scientific assessment of risks is carried out, that assessment cannot be made without having regard to those data.
Comment
In the case at issue, the Court of Justice provides a new ruling on boundaries of national legislation which provides measures having an effect equivalent to a quantitative restriction to free circulation of goods. According to the Court, these measures are not generally forbidden provided that they are based substantively on a full risk assessment based on up-to-date science and, procedurally, on a system that allows a Member State to verify whether a genuine risk to public health actually exists.
Addressing the first question, the Court confirms the findings of the Solgar decision (C-233/10), where it stated that maximum amounts of vitamins and minerals shall be based on generally accepted scientific data and on risk assessment, as generally required by Regulation (EC) 178/2002 for all measures concerning food safety.
Dealing with the procedural requirement, the Court clarifies the meaning of "mutual recognition procedure", that it shall not be intended as a procedure according to which Member States automatically recognize and authorize the import of any food supplements, but as a procedure that allows Member States to verify whether a genuine risk to public health exists. In the light of this, Member States are required to provide a procedure for repeating the assessment when importers are able to present new scientific evidence that could lead to a reconsideration of the original restriction.
This decision confirms that the lack of harmonization in sensitive matters such as food supplements still leads to the creation of barriers to free circulation of goods even though Regulation 764/2008/EC ("Mutual Recognition Regulation") has clarified the procedure which national authorities shall follow before they can restrict goods which are lawfully marketed in other Member States. This has been clearly pointed out by the EU Commission itself in the document "Upgrading the Single Market: more opportunities for people and business" where the Commission has highlighted that "National regulations and practices continue to create barriers ()While these problems occur in many industrial sectors, they are particularly present in the fields of construction, foodstuffs, food supplements and fertilisers. This translates into lost business opportunities, less competition and higher prices for consumers".
Even though the referring Court has not yet provided a decision on the case at issue, the ruling of the CJEU has led the Italian Ministry of Health to revise the maximum levels of vitamins (Vitamin D, Vitamin B12 and Vitamin K) allowed in food supplements, bringing legal levels in line with the European Food Safety Authority opinions and international safety data.
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All of those antioxidant supplements are a huge con – INSIDER
Posted: at 5:13 pm
facebook pinterest email copy link Antioxidants may not live up to all the hype.Flickr/Ano Lobb The INSIDER Summary:
Food and supplement companies make it seem like antioxidants are little warriors that start vanquishing diseases in your body as soon as you ingest them. It's easy to assume that consuming more of them must be better than consuming less.
But science shows loading up on antioxidants may not be as beneficial as you'd think some research suggests it can even cause harm. Here's what you need to know.
First, a quick primer on how antioxidants work:
Blueberries are a source of dietary antioxidants.Flickr/mystuart
Antioxidants have the power to stop free radicals, highly reactive chemicals that tear through the body, damaging cells and possibly playing a role in the development of diseases like cancer. Free radicals are an inescapable fact of life: The body makes them as a natural byproduct of digesting food, and it also makes them in response to pollution or radiation exposure.
"Antioxidants" is the catchall name given to the hundreds probably thousands of chemicals that can quench destructive free radicals. The body makes a lot of its own antioxidants, but we can also get them from our diet. Some antioxidants are also vitamins vitamins A, C, and E, to be specific but most others aren't.
When it comes to antioxidants, more is n0t always better.
Vitamin E supplement pills.Flickr/John Liu
A few decades ago, scientists began to understand that free radical damage might play a role in conditions like heart disease, cancer, vision loss, and more, according to the Harvard School of Public Health. So they decided to study what would happen if they gave people large doses of antioxidants in supplement form.
The results have been largely disappointing.
In 1985, for instance, American researchers recruited 18,000 people at high risk for lung cancer and had some of them take vitamin A supplements. But the study was halted almost two years early because participants taking the supplements were lung cancer than participants taking a placebo.
Antioxidant supplements aren't always beneficial.Shutterstock
Newer research hasn't been much more promising. A 2007 review found that taking antioxidants beta carotene, vitamin A, or vitamin E could increase mortality yes, that's the fancy scientific term for death. And while some trials have found a benefit to antioxidant supplementation, most simply haven't.
"The supplement trials have really failed," Christopher Gardner, PhD, professor of Medicine at Stanford Prevention Research Center and member of the True Health Initiative (THI), told INSIDER.
The antioxidant "scores" on food packages don't mean much, either.
You've probably come across tons of foods with claims about antioxidants on the label.
The test that companies use to make such claims is called the Oxygen Radical Absorbance Capacity, or ORAC. The problem is that it's a done in a test tube, not in humans. And just because a food has lots of antioxidant power in a test tube, Gardner explained, doesn't mean it's going to translate to a tangible health benefit in your body.
Food companies like to boast about antioxidant content.Flickr/Ty Konzak
"Even though [there's an antioxidant] in a food, you would have to absorb it without breaking it down," Gardner said. "Then it would have to be delivered to some part of your body that needs it. Then it would have to be the case that you didn't have enough to begin with, so this [antioxidant] made up for your deficiency. And then the last thing is, how would you measure that it did something?"
It's really tough to prove that the antioxidants in your morning goji berries, for example, are the reason you do or don't get heart disease 50 years from now.
Antioxidant content isn't the only reason you should buy a food.Flickr/Mike Mozart
Because of all this, the USDA decided to shut down its online ORAC database back in 2012, writing that ORAC values were "routinely misused" by food and supplement companies.
This doesn't mean products that list ORAC scores are necessarily bad for you. On the contrary, foods with high ORAC scores are often very nutritious choices, cardiologist Joel Kahn, MD, another THI member, told INSIDER.
But you shouldn't let antioxidant-based marketing claims sway your food decisions. Don't spend more on a certain type of berry solely because it has a high ORAC score or the word "antioxidants" plastered all over the package. Just buy whatever berries you want to eat.
One thing is clear: Foods that contain lots of antioxidants are good for your health.
Fruits and vegetables are the way to go.Flickr/Jason Paris
Most health authorities agree: Antioxidant supplements aren't worth your money, but antioxidant-rich foods definitely are.
"Antioxidant-rich foods probably sound familiar because we've been telling you to eat those for a really long time," Gardner said.
Fruits, vegetables, whole grains these foods are all rich in antioxidants, but they also have healthful fiber and essential nutrients your body needs. Plus, a robust body of evidence says that they're beneficial for long-term health.
"People should get the majority of their antioxidants from brightly colored fresh fruits and vegetables," Kahn said. "There's no doubt eating fruits and vegetables is a dose-related way to improve your health."
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Nutrigenetics, Weight Management, and Dietary Supplements – Nutritional Outlook
Posted: at 5:13 pm
Could knowledge of our individual genetic variants guide us in making better, more personalized lifestyle choices, including the foods we eat and the dietary supplements we take? And could we leverage this information to mitigate our individual risk of obesity and other conditions? Experts in the fields of genetics, microbiology, the -omics (including metabolomics, proteomics, and more) and nutritional science generally say yesbut their opinions differ on what conclusions we can draw now with the information we currently have in this field, and how and when the science will be translatable into meaningful, scientifically sound commercial applications.
Much more than a trendy buzzword, nutrigenetics has as its main goal to understand the gene-based differences in response to dietary components and to develop recommendations that are the most compatible with the health status of individuals based on their genetic makeup, explains Jos M. Ordovs, PhD, director, Nutrition and Genomics, and professor, Nutrition and Genetics, at the Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University (Boston). Another way of putting it? Nutrigenetics is about how individual responses to food are driven by genetic differences.
Hooman Allayee, PhD, professor, Departments of Preventive Medicine & Biochemistry and Molecular Medicine, at the Keck School of Medicine, University of Southern California (Los Angeles), and president-elect, International Society of Nutrigenetics/Nutrigenomics, adds, Nutrigenetics asks, Do the DNA differences between any two people make them respond differently to nutrients? The concept is based on relating genetic differences at the DNA level to the response to nutritional components.
This science is poised to disrupt the nutrition field and its blanket public-health guidelines as we now know them, and the potential applications hold tantalizing appeal for industry and consumers alike. The ultimate personalized nutrition comes, of course, from nutrigenetics, Ordovs says.
Characterizing the field as extremely complex, Ordovs explains that while he and his colleagues have been studying nutrigenetics for decades, it is, in his opinion, still in its infancy from a practical perspective. Early on, progress was limited by more-primitive technology and poor knowledge of the human genome, Ordovs says. Since then, technology has vastly improved, and our knowledge of the human genome is betterthough rather incomplete.
Still, Ordovs says, we have to integrate nutrition and genetics using very solid scientific approaches if we want the field of nutrigenetics to mature and yield meaningful solutions and applications.
Nutrigenetics & Weight Management: What We Know, What We Have Still to Learn
The question, What do we currently know about nutrigenetics, particularly related to obesity and weight management, and what can we do with that knowledge? yields varying answers depending on whom you ask. A portion of an American Heart Association statement published in the journal Circulation: Cardiovascular Genetics in 2016 and to which both Ordovs and Allayee contributed reads, Nutrigenomics has the potential to identify genetic predictors of disease-relevant responses to diet, and this potential and its applicability in the context of personalized nutrition have popular appeal. However, nutrigenomics has also been the subject of much hyperbole and has been ascribed much promise, particularly in the arenas of personalized nutrition, functional foods, and nutraceuticals. Unfortunately, the science has not yet fully delivered on this unrealized potential. The statement does acknowledge enthusiasm about possible clinical applications but maintains that the evidence base remains limited.
While the tone of that particular statement is one of cautious optimism, Tufts Ordovs does, in comments shared with Nutritional Outlook, point to promising findings related to nutrigenetics and weight management, including research performed at Tufts University that concluded that limiting saturated-fat intake may help promote healthy body-mass index (BMI) especially in people whose genetic makeup increases their risk of obesity.1 (For this study, researchers identified 63 gene variants related to obesity and used them to calculate a genetic-risk score for obesity for more than 2800 white adults. Participants with a higher genetic-risk score who also consumed more of their calories as saturated fat were more likely to have a higher BMI, the researchers found.)
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