A persistent barrage of information is not the best method for getting through to someone with a different point of view.
- When you want someone to see things differently and to abandon their previous stance, sometimes persistence is not key.
- "Too often we think change is about pushing," says Jonah Berger, author of the book The Catalyst: How to Change Anyone's Mind, and a marketing professor at the Wharton School at the University of Pennsylvania. "We think if we just come up with one more way people will eventually come around."
- Through speaking with people who have successfully changed minds of others, Berger identified five common barriers and created the REDUCE framework for finding the catalysts needed to break through: reactants, endowment, distance, uncertainty, and corroborating evidence.
Northwell Health CEO Michael Dowling has an important favor to ask of the American people.
- Michael Dowling is president and CEO of Northwell Health, the largest health care system in New York state. In this PSA, speaking as someone whose company has seen more COVID-19 patients than any other in the country, Dowling implores Americans to wear masks—not only for their own health, but for the health of those around them.
- The CDC reports that there have been close to 7.9 million cases of coronavirus reported in the United States since January. Around 216,000 people have died from the virus so far with hundreds more added to the tally every day. Several labs around the world are working on solutions, but there is currently no vaccine for COVID-19.
- The most basic thing that everyone can do to help slow the spread is to practice social distancing, wash your hands, and to wear a mask. The CDC recommends that everyone ages two and up wear a mask that is two or more layers of material and that covers the nose, mouth, and chin. Gaiters and face shields have been shown to be less effective at blocking droplets. Homemade face coverings are acceptable, but wearers should make sure they are constructed out of the proper materials and that they are washed between uses. Wearing a mask is the most important thing you can do to save lives in your community.
Researchers found the common element in the destruction of even the most powerful empires.
- Researchers found a commonality between the collapse of ancient empires.
- Even the best-run nations fell apart because of leaders who undermined social contracts.
- The scientists found that societies that had good governments broke up even worse than those with dictators.
The ruins of the Roman Forum, which served as representational government.
Credit: Linda Nicholas / Field Museum
Perhaps downhill and cross-country skiers don't face the fate of potters, typesetters and saddlers, but their situation is certainly unclear.
Global warming is making it difficult to organize sporting tournaments, but it's an even greater threat to small local clubs. Meanwhile, Big Sport is taking the lead in climate hypocrisy.
Can we end world hunger by 2030? Thanks to a new program, the data for it is all there.
- An international team of researchers has released a series of studies geared towards ending world hunger.
- They are thought to be some of the first people to use Evidence Synthesis for agricultural data.
- Their ideas could increase food production and lower poverty for a low cost, regardless if they meet their lofty goal.
Who are these people?<p> Headquartered at Cornell University, <a href="https://ceres2030.org/" target="_blank" rel="noopener noreferrer">Ceres2030 </a>is a collective project involving people from around the world. It is financed in part by the Bill and Melinda Gates Foundation and the German Federal Ministry of Economic Cooperation and Development. <br> <br> The enterprise includes more than 70 researchers from 23 different countries with the best information available on what works to reduce <a href="https://news.cornell.edu/stories/2020/10/cornell-unites-science-and-policy-end-hunger" target="_blank" rel="noopener noreferrer">hunger</a>. These researchers are divided into eight teams, each covering a separate subject area. Each group reviews the literature and combines it into a general review which can be used to inform policy <a href="https://news.cornell.edu/stories/2020/10/cornell-unites-science-and-policy-end-hunger" target="_blank" rel="noopener noreferrer">decisions</a>. </p>
What do they want us to do?<iframe width="730" height="430" src="https://www.youtube.com/embed/D1eFcqZE3xU" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe><p> The analysis shows that many studies agree on the benefits of a few, straightforward initiatives. Among these findings are game-changing ideas <a href="https://www.nature.com/articles/s41477-020-00795-9" target="_blank" rel="noopener noreferrer">like</a>:<br> <br> Farmer's organizations help their members increase both their incomes and crop yields. Membership was linked with higher incomes in nearly 60 percent of studies, and benefits to crop yields were demonstrated in a quarter. These organizations play a part in helping farmers adopt modern techniques, tools, and crop types to help implement other policy suggestions. Assisting people in joining them can have a tremendous impact on their lives.</p><p>In the middle and lower-income countries, nearly three-quarters of small farmers live and work in areas where water is scarce. The vast majority of these farms do not have an irrigation system to speak of. Output and income could both be increased by addressing this infrastructure issue. Helping farmers switch to more climate change and drought-resistant crops and introduce new and improved livestock sources, both as sources of labor and food, can improve productivity and keep people resilient in the face of climate change. </p><p>These are just a handful of the ideas Ceres2030 endorse in their press releases. In each case, they point to piles of data showing the effectiveness of these ideas in increasing incomes, crop yields, and small producers' resiliency in the face of threats such as climate change. It could cost roughly 14 billion dollars more a year in aid to do it, about twice as much as we are spending on the problem now, alongside new investments by the governments of nations most plagued by <a href="https://ceres2030.org/shorthand_story/donors-must-double-aid-to-end-hunger-and-spend-it-wisely/" target="_blank" rel="noopener noreferrer">hunger</a>. </p><p>All of these ideas can be implemented tomorrow; many places have already done these things. It is only a matter of deciding to do it. Some of the findings and ideas are even simpler than these, including discovering that we <a href="https://osf.io/6zc92/" target="_blank">waste a lot of food</a> and that simple solutions can prevent much of it. <br><br>More information on their ideas and how they came to their conclusions can be found on the Ceres2030 <a href="https://ceres2030.org/" target="_blank">website</a>. <br></p>
Will this work?<iframe width="730" height="430" src="https://www.youtube.com/embed/9CdZSakEqBU" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe><p> It might. <br> <br> The findings and recommendations are based on extensive research, histories of successful implementation elsewhere, and a sincere desire to use evidence to help people. Following them would lead to better-informed farmers making more money while sustainably growing more food. The recommendations are neither one-size-fits-all, nor are they overly specific to the point where they cannot be <a href="https://news.cornell.edu/stories/2020/10/cornell-unites-science-and-policy-end-hunger" target="_blank" rel="noopener noreferrer">generalized</a>. <br> <br> There are also plenty of reasons to be pessimistic. A <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015855/" target="_blank" rel="noopener noreferrer">study</a> published this year in Nature argues that we will not be able to end world hunger by 2030. It takes the stance that some countries with endemic malnourishment are unlikely to reach their development targets for 2025, let alone the more ambitious goals for 2030. <br> <br> The costs of not at least making progress on this front are very high. Without progress, an additional 100 million people could end up both hungry and mired in extreme poverty by the end of the decade, according to an <a href="https://www.weforum.org/agenda/2020/07/global-hunger-rising-food-agriculture-organization-report/" target="_blank" rel="noopener noreferrer">estimate</a> by the United Nations' Food and Agriculture Organization. The COVID-19 pandemic has caused some regression already, as economic difficulty leads to empty bellies. <br> <br> The entirety of human history has been marked by attempts to produce enough food for everybody, and it is only recently (relatively speaking) that we've managed to do that. Today, we grow enough food for 10 billion people but seem to have difficulty getting it to the people who need it most. The suggestions of the Ceres2030 team, if followed, offer the chance to finally rid the world of hunger and famine for less than $50 per currently malnourished person per year. <br> <br> It's only a question of doing it. Let's see if we want to. </p>
Machine learning is a powerful and imperfect tool that should not go unmonitored.
- When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage.
- Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque.
- In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism.
Here are six ways machine learning threatens social justice<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDUyMDgxNC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY0MzM0NjgxOH0.zHvEEsYGbNA-lnkq4nss7vwVkZlrKkuKf0XASf7A7Jg/img.jpg?width=980" id="05f07" class="rm-shortcode" data-rm-shortcode-id="a7089b6621166f5a2df77d975f8b9f74" data-rm-shortcode-name="rebelmouse-image" />
Credit: metamorworks via Shutterstock<p><strong></strong><strong>1) </strong><strong>Blatantly discriminatory models</strong> are predictive models that base decisions partly or entirely on a protected class. Protected classes include race, religion, national origin, gender, gender identity, sexual orientation, pregnancy, and disability status. By taking one of these characteristics as an input, the model's outputs – and the decisions driven by the model – are based at least in part on membership in a protected class. Although models rarely do so directly, there is <a href="https://www.youtube.com/watch?v=eSlzy1x6Fy0" target="_blank">precedent</a> and <a href="https://www.youtube.com/watch?v=wfpNN8ASIq4" target="_blank">support</a> for doing so.</p><p>This would mean that a model could explicitly hinder, for example, black defendants for being black. So, imagine sitting across from a person being evaluated for a job, a loan, or even parole. When they ask you how the decision process works, you inform them, "For one thing, our algorithm penalized your score by seven points because you're black." This may sound shocking and sensationalistic, but I'm only literally describing what the model would do, mechanically, if race were permitted as a model input. </p><p><strong>2) Machine bias</strong>. Even when protected classes are not provided as a direct model input, we find, in some cases, that model predictions are still inequitable. This is because other variables end up serving as proxies to protected classes. This is <a href="https://coursera.org/share/51350b8fb12a5937bbddc0e53a4f207d" target="_blank" rel="noopener noreferrer">a bit complicated</a>, since it turns out that models that are fair in one sense are unfair in another. </p><p>For example, some crime risk models succeed in flagging both black and white defendants with equal precision – each flag tells the same probabilistic story, regardless of race – and yet the models falsely flag black defendants more often than white ones. A crime-risk model called COMPAS, which is sold to law enforcement across the US, falsely flags white defendants at a rate of 23.5%, and Black defendants at 44.9%. In other words, black defendants who don't deserve it are <a href="https://coursera.org/share/df6e6ba7108980bb7eeae0ba22123ac1" target="_blank" rel="noopener noreferrer">erroneously flagged almost twice as much</a> as white defendants who don't deserve it.</p><p><strong>3) Inferring sensitive attributes</strong>—predicting pregnancy and beyond. Machine learning predicts sensitive information about individuals, such as sexual orientation, whether they're pregnant, whether they'll quit their job, and whether they're going to die. Researchers have shown that it is possible to <a href="https://youtu.be/aNwvXhcq9hk" target="_blank" rel="noopener noreferrer">predict race based on Facebook likes</a>. These predictive models deliver dynamite.</p><p>In a particularly extraordinary case, officials in China use facial recognition to <a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html" target="_blank" rel="noopener noreferrer">identify and track the Uighurs, a minority ethnic group</a> systematically oppressed by the government. This is the first known case of a government using machine learning to profile by ethnicity. One Chinese start-up valued at more than $1 billion said its software could recognize "sensitive groups of people." It's website said, "If originally one Uighur lives in a neighborhood, and within 20 days six Uighurs appear, it immediately sends alarms" to law enforcement.</p>
Recourse: Establish machine learning standards as a form of social activism<p>To address these problems, take on machine learning standardization as a form of social activism. We must establish standards that go beyond nice-sounding yet vague platitudes such as "be fair", "avoid bias", and "ensure accountability". Without being precisely defined, these catch phrases are subjective and do little to guide concrete action. Unfortunately, such broad language is fairly common among the principles released by many companies. In so doing, companies protect their public image more than they protect the public.<br></p><p>People involved in initiatives to deploy machine learning have a powerful, influential voice. These relatively small numbers of people mold and set the trajectory for systems that automatically dictate the rights and resources that great numbers of consumers and citizens gain access to.</p><p>Famed machine learning leader and educator Andrew Ng drove it home: "AI is a superpower that enables a small team to affect a huge number of people's lives... Make sure the work you do leaves society better off."</p><p>And Allan Sammy, Director, Data Science and Audit Analytics at Canada Post, clarified the level of responsibility: "A decision made by an organization's analytic model is a decision made by that entity's senior management team."</p><p>Implementing ethical data science is as important as ensuring a self-driving car knows when to put on the breaks.</p><p>Establishing well-formed ethical standards for machine learning will be an intensive, ongoing process. For more, <a href="https://youtu.be/ToSj0ZkJHBQ" target="_blank">watch this short video</a>, in which I provide some specifics meant to kick-start the process.</p>
An overfished planet needs a better solution. Fortunately, it's coming.
- Cell-based fish companies are getting funding and making progress in offering a new wave of seafood.
- Overfishing and rising ocean temperatures are destroying entire ecosystems.
- The reality of cell-based fish is likely five to 10 years away.
Future of Food: This genetically engineered salmon may hit U.S. markets as early as 2020<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="466fe20063a2292a48789f370c04ea13"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/bco7rPyKwec?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>While cell-based beef is getting all the press, companies like BlueNalu recently raised $24.5 million in funding. The San Diego-based start-up <a href="https://www.sandiegouniontribune.com/business/story/2019-12-25/lab-grown-fish-just-got-real-san-diego-startup-shows-off-first-slaughter-free-yellowtail#:~:text=A%20San%20Diego%20foodtech%20startup,many%20researchers%20only%20dream%20of" target="_blank">extracts</a> muscle cells from an anesthetized fish, treats the cells with enzymes in a culture, places the mixture in a nutrient solution in a bioreactor, spins it all around in a centrifuge, and finally 3D-prints the new concoction into the desired shape.</p><p>The goal isn't to perfectly replicate a fish that you'd find on ice in your local market. No brain, skin, organs, or even possibility of consciousness are in this creature. In a strange twist, this makes cell-based seafood a potential food source for vegetarians and vegans, since the Adam fish can be returned to the waters unharmed. </p><p>One current solution to overfishing—fish farms—comes with it a host of problems, including the proliferation of sea lice, which have a tendency to escape the porous boundaries to infect wild fish. Bonus: with cell-based fish, you won't run into any issues with mercury or <a href="https://bigthink.com/surprising-science/microplastics-soil" target="_self">microplastics</a>. </p><p>What you'll (hopefully) purchase is a good-tasting product, which has thus far been elusive. BlueNalu CEO, <a href="https://apnews.com/5327a2c3a8e74adab0cc63d5994ffc72" target="_blank">Lou Cooperhouse</a>, is confident his company's product will eventually meet standards set by your taste buds. </p><p style="margin-left: 20px;">"Our medallions of yellowtail can be cooked via direct heat, steamed or even fried in oil; can be marinated in an acidified solution for applications like poke, ceviche, and kimchi, or can be prepared in the raw state."</p>
Photo: aleksandr / Shutterstock<p>There are barriers, of course. As with pluripotent meats, cell-based fish are expensive. A spicy salmon roll produced by the start-up, Wildtype, <a href="https://singularityhub.com/2020/09/16/this-startup-is-growing-sushi-grade-salmon-from-cells-in-a-lab/" target="_blank">cost $200</a> to make. It's going to take a while for the price to drop and consumer demand to rise; estimates are five to ten years.</p><p>Another issue is indicative of solar power and wind energy trying to cut in on Big Oil: the seafood industry doesn't want to lose its profit margin. Of course, like oil companies, Big Seafood is betting on a finite resource. The sooner they realize that, the better. </p><p>Then there's production, which is where education comes into play. Former BlueNalu Chairman Chris Somogyi tries to <a href="https://www.npr.org/sections/thesalt/2019/05/05/720041152/seafood-without-the-sea-will-lab-grown-fish-hook-consumers" target="_blank">demystify</a> the laboratory process. </p><p style="margin-left: 20px;">"We aren't using CRISPR technology. We aren't introducing new molecules into the diet. We're not introducing a new entity that doesn't exist in nature. The approval will be about whether this is safe, clean and are the manufacturing processes reliable and accountable."</p><p>If there's an ick factor to cell-based fish, remember that most processed foods are already created in laboratories. There are no Oreo trees or ketchup plants to harvest. </p><p>For now, these start-ups and others like them will have to figure out how to create non-energy-intensive and cost-prohibitive solutions for spinning up seafood inside of a petri dish. Novelty alone will create enough demand to get them going, as <a href="https://www.cnbc.com/2020/08/04/beyond-meat-bynd-q2-2020-earnings.html#:~:text=Net%20sales%20rose%2069%25%20to,since%20many%20are%20temporarily%20shuttered." target="_blank" rel="noopener noreferrer">precedent</a> in the lab-grown meat industry shows. </p><p>The reality is that we need to go down this path. There are too many humans and not enough resources. While we can hope (as David Attenborough does in his <a href="https://www.netflix.com/title/80216393" target="_blank" rel="noopener noreferrer">new Netflix documentary</a>) that national governments will create more no-fish zones, there's no guarantee that will happen. We need science to win this one. </p><p>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank" rel="noopener noreferrer">Twitter</a> and <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank" rel="noopener noreferrer">Facebook</a>. His new book is</em> "<em><a href="https://www.amazon.com/gp/product/B08KRVMP2M?pf_rd_r=MDJW43337675SZ0X00FH&pf_rd_p=edaba0ee-c2fe-4124-9f5d-b31d6b1bfbee" target="_blank" rel="noopener noreferrer">Hero's Dose: The Case For Psychedelics in Ritual and Therapy</a>."</em></p>