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Thermodynamic Limits Around M-dwarf Stars: Acknowledgements

The notes on Sculpture

THE GREAT EMPIRE OF JENGIS KHAN AND HIS SUCCESSORS

POLLOCK AND THE PORROH MAN

The Diseases of Association

In Tolkien's legendarium, the kings communicated and acquired information about the past and the present with palantirs, a local variety of crystal ball. Palantirs were placed in the world's 7 largest fortresses, and only the rulers had access to them. The artifacts only showed things that existed but could lead the viewer to misinterpret what they saw. To minimize bad judgment, the kings appointed wardens to protect the stones and constantly look into them, gathering news and intelligence. Now, Google and Facebook have become present-day palantirs, and competitive intelligence companies turned into wardens. The kings can thus avoid misinterpreting what they see and get answers to their questions. The range of applications is limited only to the client's imagination: from background checks on one's partners to reuniting with long-lost relatives. Molfar agency gathers and cross-references information from publicly available sources (OSINT, open-source intelligence) that will help the client company make a decision regarding deals, hires, and partnerships, prepare for court proceedings, and much more. We're going to show you how it works, and although we take fictional characters as examples, the search techniques in the story are genuine. The client, an owner of the Middle-earth zoo chain, makes a request to do a thorough background check on his former fellow student, Saruman. Sadly, he forgot the last name. The only thing he does recollect is that the man owned a mine flooded in 2002, and there was something about iron and mountains in its name. So, we have four entry points to analyze: The first name The fact that the client and the person of interest studied together The date of the mine's flooding The mine's name Getting a confirmation on at least 3 of the 4 would let us advance in our search. Knowing that the client graduated from the Isengard National University in 1985 majoring in ornithology, we start looking for his fellow students on Facebook. We find three, but no Saruman among them. Looking at Twitter and Instagram yield the same results. Then we turn to ornithology forums and Craigslist postings. There, we find two ornithologists by the name of Saruman. Those don't match the university, though. Now we switch to the next entry point - the name of the mine. There are lots of mines in the Isengard region. We narrow down our search to 5–6 similar names, albeit to no avail. Then we decide to look up similar names among companies. Now we go to the public register and search by ‘mountain’ and ‘iron’. There it is: . Seemingly, it's what we need. However, the company director is Alex Treebeard, which doesn't add up. a record for Iron Mountain Mining LLC We open the company profile and see that it was registered in 1995. And its director indeed had been Saruman White up until 2004, when Alex Treebeard took over. Now we have another part of the mosaic - Saruman's last name. Now let's continue our background check. On to 2002, the date of flooding. Suppose the mine was flooded between the foundation of the company and Saruman White's exit. We launch Google Earth and zoom in to the level of detail between 15 m and 15 cm. Apart from higher fidelity, it will show us the digital terrain model, too. Now we go to the mine location and start browsing historical satellite images. Judging by the image on the top that dates back to 2000, the water started accumulating in the coal pit already at that time. Sadly, the next image is as recent as 2014, and there are no interim ones. We see that the mine was indeed flooded as of 2014. Thus, we narrow down the time frame from 1995–2004 to 2000–2004. Now let's look at the company register's license module. There, we see that the license for Iron Mountain Mining LLC was revoked in 2003. In the Unified State Register of Judicial Decisions, we sift through the endless tide of cases on illegal logging in the Isengard region. An idea strikes us. Who is in charge of the country's mines? The State Geology and Mineral Resources Service of Middle-earth. Going through administrative orders, we find out that the mine was renamed Anthracite in 2002, and its ownership was transferred to an eponymous legal entity registered by Alex Treebeard. At this point, we also note that one order mentions not only coal but also the accumulation of drainage water within the mine. So, with the revoked license and the new owner, we bring the upper end of the time frame down to 2002. It's enough to verify what the client said about the flooding date. Verifying 3 out of 4 facts would be enough. However, our inner perfectionists won't rest until we are 100% sure. Let's return to verifying the ornithology major. We go to the diploma database, but Saruman White's full name is not enough, we need a diploma number, too. We go to Google Scholar, a scholarly literature search engine, hoping to find Saruman's articles, but all we discover is ‘why owls go “woo” in the night’. Knowing the full name, we can surmise that our person of interest has children, who we can google by ‘last name + * + patronymic’, where * is a wildcard character. The query yields a Craigslist posting of Saruman White Jr. wanting to sell a cape complete with his telephone number. We input the number into Nuga.app, and it returns his Instagram handle: @saruman_the_white. We scroll through a year worth of posts and find the confirmation of Saruman’s Sr. alma mater. The photo shows a page from the Birds of America book and a text about the father's education. As a result, we know the name of our target and the names of several persons related to him, and now we have an opportunity to dig further. My lord, in the murky glare of the palantir we found the person you want, what more do you want to know about him? This article is written by Artem Starosiek, the CEO of the Molfar, an OSINT research and consulting company.

OSINT: What You Can Find About a Person from Public Sources

Intelligence is a political, management, jurisprudence and due-diligence tool. There are fewer investors than startups, so an investor often finds itself choosing between multiple applications and putting their money into one or two companies. Venture capital investments are risky, taking into account how disingenuous startup founders can be. Steve Tan raised $1.5 million on Indiegogo, a crowdfunding platform, promising voice- and gesture-controlled smartwatch to his contributors. Then he was of buying a Ferrari and shopping in luxury boutiques for that money, with Facebook photos presented as evidence. According to Steve, it was his friend's Ferrari, and the picture was taken 4 years ago in Italy. However, he made no comments regarding the boutique photos. The smartwatch was launched, albeit in an unfinished state, and the company eventually closed. accused Josh Tetrick, the founder of vegan mayonnaise producer Hampton Creek, made his employees buy their own product in 2014 to inflate demand and attract investments. By the end of that year, the startup a $90 million deal. closed The founders of Ukrainian startup Easy Ads, stopped communicating with the investor just three months after receiving $25,000 in investments and ultimately . disappeared The story of Elizabeth Holmes and her startup Theranos began with a $700 million investment and a peak valuation of $10 billion in 2014 and ended with a titled Bad Blood: Secrets and Lies in a Silicon Valley Startup and people as Elizabeth on Halloween. book dressing In 2019, competitive intelligence company Molfar performed 237 background checks on partners, startup founders asking investors for money, and candidates for top positions. Let's consider one notable case. Mr. Frankel, a famous businessman, had a lot of friends among prosecutors, senators, industrialists, and bandits. One day, one of them tipped him off on a certain Baker Estate property. Mr. Frankel wanted to evaluate the risks of the new investment project to see if it wasn't a complete flop. Therefore, Molfar was tasked with checking if the owner - a certain Mr. Carter - was a reliable person. A certain Mr. Parker G. Carter. A search through various business registers yielded 30 something legal entities belonging to Mr Carter and connected to IT in one way or another. Among them were a marketplace, an outsourcing company, an advertising platform, and an AI start-up. Green Acres, a company based in a rural region, which developed an inventory management solution for farmers, caught our attention. The thing is, it had two owners in its history: George C. Parker and Parker G. Carter. Weird, we thought, it looks much like the other name was just the first one shuffled around, but we lacked evidence or understanding of Mr. Carter's motivations. First, we decided to find out, which companies George C. Parker also held a stake in. And it wasn't long until we stumbled upon Brooklyn Bridge Inc. The company developed a VR solution for bungee-jumping aficionados who wanted to try their favourite thing in historical locations. Having checked the company's court history, we found three records related to its failure to repay loans worth $8 million in total. In 2016, our person of interest took the money of unsuspecting investors and disappeared with it. To cover up his involvement in the fraud, Mr. Carter just shuffled up the initials, turning into an inconspicuous Parker G. Carter. The picture was complete, but we still lacked evidence. And we didn't have to dig deep to find it. Parker G. Carter's phone number on the register of individual entrepreneurs was the same as indicated on the VR development company's website. We also dived into Mr. Carter's Instagram for additional proof, looking through all 300 pictures he had there. And there it was, a photo of him in the Green Acres' office. Ultimately, by enlisting Molfar's services for a fraction of the money, the client managed to avoid blowing $50,000 by buying the Baker Estate. Written by Artem Starosiek, CEO of Molfar

Business intelligence as a tool for risk mitigation

Photo by Jens Johnsson on Unsplash So, you’d like to spice things up in your current business strategy and add an extra layer of high technology with a sophisticated AI solution. Let me just ask you one simple question first: Are you sure it is the best move? If your eyes sparkle with excitement as you think “Yes!”, you can skip this article and go find your perfect AI vendor. If, however, you have even a shade of doubt — stick around. I promise that you won’t regret it! What I have for you is answered by Miquido’s Head of Innovation that should or financial suicide for your business. Ok, no more waiting — here are the questions: 10 questions help anyone understand whether adopting AI technology is the right move Can you clearly? state your problem Can you ? solve your challenge without AI What do you understand by “ ”? doing your task right Do you accept that ? your solution will never be perfect Do to get started? you possess enough data Is your ? data relevant Will you have ? continuous access to new data Can you l ? abel your data correctly or is accuracy enough? Do you need an interpretable model Do you have to keep the project going? enough resources Sound simple enough? Well, it gets a bit more complicated. So we’ll look into each of them in more detail in just a second. Get comfy, grab something to take notes with, and go along with this article, or simply save it for later. Let’s dive in! 1. Can you state the problem clearly? A clear vision of what you want to achieve is a must when it comes to applying AI in business. You need to have a specific problem that requires high-tech solutions that can be solved with Artificial Intelligence and/or Machine Learning. Introducing AI for the sake of bragging about having it won’t fly here. To help you answer this question, try thinking about specific challenges you want to address and always have your target audience in mind. Ask yourself: Which repetitive tasks are you trying to automate? Who will benefit from this automation? Will it be worth the investment? Pro tip Its ultimate goal is to help you get inspiration and once your idea is stated, you can come back to Machine Learning. Focus on what you can have right there and right now — the cornerstone of Artificial Intelligence, data. If you’re not sure where to start — start with data mining. And then ask yourself the next question: 2. Can you solve your challenge without AI? Artificial Intelligence is great because it offers seemingly simple solutions to complex problems. The only issue is that, in fact, it’s far more demanding than it appears. So the ugly truth about the development of AI technology is that if you can get on without it — just do it. Consider these questions: Is your problem complex enough to engage machine learning? Could it be represented by a mathematical equation? Can you think of a step-by-step recipe for an output? If you answered “yes” to the last two questions — congratulations, you need artificial intelligence! don’t If, however, you couldn’t quite wrap your mind around the correct patterns, advanced machine learning algorithms might be your only chance to succeed. Pro tip It’s quite simple, really: . if you can recognise the pattern yourself, you don’t need Machine Learning to do that for you By now, you should have a pretty good understanding of whether you actually AI in your strategy or if you simply want to follow the trends. Let’s see if Artificial Intelligence actually help you out. need can 3. What does it mean to do your task right? First of all, you’ll have to define what “right” and “wrong” mean to you and your business. The world of technology is still pretty binary, and if something isn’t “true”, it false by definition. is So, before investing in any solution, . make sure you understand what it is you hope to gain from it Artificial intelligence is, first and foremost, a complicated algorithm. And in order for it to learn, you need to be able to evaluate its performance. Think along the lines of: How will you evaluate that a task is done correctly? Which mistakes will be more harmful than others? How many mistakes per 1000 results can you afford to have? That’s another big revelation about working with AI: . mistakes are unavoidable Pro tip Be careful not to aim too high with accuracy. . Placing an excessively high bar may result in you missing some profitable opportunities That brings us straight to question #4: 4. Do you accept that your solution will never be perfect? If your reply to this question is “no”, I’ve got some bad news for you: you’re not ready to work with machine learning. Sometimes more, sometimes less, but there’s no chance in the world for your solution to run error-free. Even if you have Elon Musk on your team. There will be mistakes. At this point, think about yourself, your mental health, and the daily struggle you’ll have to accept from now on in your business: Can you live with occasional mistakes in your model? What are the actual consequences of such mistakes? What does it all mean from an ethical standpoint? Once again, there are two potential outcomes here: You’ve evaluated the risks correctly, talked yourself into accepting them, and are on your merry way to hiring an AI software development company. You’ve reached the conclusion that the stakes are too high, so you can either give up on the idea of working with machine learning altogether. Pro tip To minimise the chance of costly and dangerous mistakes, simply . This is known as a “human-in-the-loop approach” and can save you some headaches. have someone who will double-check the results With that in mind, you must have a pretty good idea of whether or not the AI onion is worth peeling. Now it’s time to see if you’ll be able to actually build your model. 5. Do you have enough data? The most common and broadly discussed limitation of Artificial Intelligence is its heavy dependency on datasets. There is simply no machine learning without data. To put it bluntly: if you don’t have the data to keep your project running, your chances of launching it in the first place are slim. Some questions that might help you think are: Do potentially useful inputs even exist? Can you gain access to them (e.g. build them, buy them, etc.)?Do you have enough examples? Pro tip It usually takes at least 10 thousand samples when training the model from scratch. Yet, , and don’t we all thrive for perfection? the more examples you have the more reliable your AI model will be In data science, however, it’s both quality quantity that matter, so if you do have enough materials to proceed with, let’s check if they hold any value. and 6. Is your data relevant? As you could’ve guessed from the previous point, without having a well-thought-through plan for data collection, you’ll stumble across multiple serious issues with your AI solution pretty quickly. The best way to ensure it doesn’t happen is to double-check the actual relevance of your data. There’s no point in having 1000+ features with no practical use, they’ll only eat up your precious storage space. The important questions to keep in mind to ensure that you’ll only work with the highest quality data points are: Are your data points significant? Is all the data clean enough? Is the data you have relevant to your target audience? Is it free of bias? Pro tip . In order for everything to work in an orderly fashion, you need to ensure your sample data is well-balanced, clean and free of inconsistencies. To learn from examples, AI needs good examples to learn from And if you’ve got that covered, it’s time to think about the further evolution of your AI solution. 7. Will you be able to access new data continuously? We are almost done with data questions, I promise! But while we’re on the subject, let’s think about the scalability of your project. Having an accurate initial model might suffice for obtaining reliable predictions for a relatively short period of time. Yet, you’ll soon notice there are numerous factors that may negatively affect its performance. These threats include social events, seasonal changes, shifts in demographics, the geographical location of your users, etc. When there are factors that can influence your target audience over time, . your AI model needs to be constantly retrained Here, you will have to face new challenges and try to answer questions like: How sensitive is your dataset to changes? Do or will you have access to new data continuously in order to update it? A great example of such unexpected threats is Covid-19. It has pretty much rendered a massive amount of data obsolete, as people’s behaviour has changed drastically. Pro tip Or, in case of a disastrous setback, like a worldwide pandemic, at least can be easily updated. Make sure that your AI solution will be weather-proof. That brings us to the final question about data. 8. Can you label your data properly? We’ve covered some paramount issues like obtaining, updating, and navigating Big Data, so now it’s time to talk about data management. . Some datasets, such as image recognition systems, inherently contain labels based on the logged user actions. However, if your plan is to build a classification from the ground up, you’ll need to come up with a system for correct data labelling. In order for your AI solution to correctly understand the data, it requires proper labelling The important questions to think about here are: Do you to label your dataset? need Can you get a human to do it? How much time and money will that process require? Pro tip When working with advanced data, such as ECG signals or medical images, before your model could learn from examples. hire experts to correctly classify each case With that, we’re ready to move on to the next question! 9. Does your model have to be interpretable? is one of the primary issues with machine learning. But what does it even mean? Interpretability In the simplest terms, the higher the , the easier it makes it for a human to understand the processes behind the algorithm’s decision making. interpretability of an AI training model It is crucial for some businesses to fully understand the flow due to external policies and regulations. However, more often than not, . having a model that is accurate yet not entirely interpretable, is enough to get your AI project up and running So, the two major questions that arise here are: Are there any regulations governing your model’s interpretability? What is your trade-off between accuracy and interpretability? Pro tip Testing helps to launch many projects regardless of their complexity. If you can’t explain how something works, run as many tests as needed to prove that it does work. Looks like it’s time for the final and the most important question you should ask yourself before committing to an AI project: 10. Do you have enough resources to implement & maintain your AI solution? As you may already know, machine learning projects aren’t particularly cheap, are not that easy to implement, and require an experienced team behind the wheel. So before you jump into your next idea headfirst, try to evaluate with great care whether you have enough resources to really pull this off. Some of the questions that might help you at this stage are: How much does building and updating the database cost? Do you have access to enough processing power? Are you aware of the costs of training and retraining your model? Are the expected benefits higher than the estimated costs? Depending on the complexity of your model, all these numbers can vary drastically. Pro tip G , as these solutions generally tend to be much more cost-effective than self-hosted solutions. o with Cloud Final remarks With all that in mind, you should be perfectly ready to make a final decision regarding your AI project. If you’re still up for the challenge — that’s great news! All there’s left to do is to find the right vendor to deliver your AI solution. And if you’ve discovered it may not be the best time for one just yet — don’t worry! The world of technology is evolving rapidly, so while something might have been too complicated yesterday, it may as well become the new normal tomorrow. Just remember — as long as you can get on Machine Learning, there’s really no pressing need to spend resources on it. without About The Author This article was written by Anna Kvasnevska, and has been posted on her behalf, with her permission. Anna is a Millennial, an ex-pat, a writer, a marketer — in that particular order. On a path to understanding the world through personal & spiritual growth.

10+ Questions you Should ask Yourself Before Developing an AI Solution

The Moon has long fascinated humankind, inspiring countless myths, stories, and scientific inquiries. Our understanding of the Moon has shifted gears in recent years owing to several outer space missions that explored geology, surface, and, mineral resources. The idea of building a lunar economy has existed for some time now. To make it a reality, scientists will have to set up a communication and navigation infrastructure with a standardized way to determine time. NASA is currently in the early stages of developing , a vital initiative to create an internet network for the Moon. This project goes beyond enabling astronauts to take pictures; it is a crucial component of the program. The agency intends to build various infrastructures on and around the Moon, such as a , a new , and a lunar web service that can maintain connectivity between all elements. LunaNet Artemis human habitat space station “LunaNet is a framework of mutually agreed-upon standards, protocols, and interface requirements allowing future lunar missions to work together, conceptually similar to what we did on Earth for joint use of GPS and Galileo,” Javier Ventura-Traveset, ESA's Navigation Manager. explains LunaNet: Enhancing Connectivity and Empowering Missions at the Moon “As NASA establishes a sustained lunar presence on the Moon, creating a robust infrastructure becomes increasingly important. The LunaNet communications and navigation architecture is extensible and flexible,” D.J. Israel, Exploration and Space Communications Projects Division Architect in a conference paper titled “LunaNet: a Flexible and Extensible Lunar Exploration Communications and Navigation Infrastructure.” states He further explains how LunaNet will provide three types of services: networking, position, timing and navigation, and science utilization. Users, both human and robotic, will experience network functionality similar to that experienced on Earth. The ESA(European Space Agency) opened an last week,  inviting private companies to help with its program, aimed at launching  three or four satellites to be “carried into lunar orbit by a space tug and deployed one by one, to form a constellation of lunar satellites.” The satellite constellation's orbits will be strategically optimized to encompass the lunar south pole, which has been designated as the primary landing site for  NASA’s future Artemis missions. application portal “Moonlight” What is ESA’s Moonlight initiative? “Many of the proposed lunar missions will require reliable navigation and telecommunication capabilities. Building these capabilities independently would be inefficient and costly due to complex, recurrent developments,” ESA’s announcement blog post titled “Lunar Economy Applications” . read Not only ESA, but NASA has also adopted public-private models for providing space services, such as and to the International Space Station. These partnerships have also been established for future endeavors, including the and to the lunar surface. delivering cargo transporting crew delivery of scientific experiments astronauts NASA has invested $1.2 billion in to develop a human landing variant of to land American astronauts on the Moon under Artemis III,” marking humanity’s first return to the lunar surface in more than 50 years.” SpaceX Starship Of the total investment in space over the last decade, which , 99% was directed toward the development of satellites and their launch vehicles for Earth's orbit. The emerging markets of space habitats, satellite servicing, and the fledgling lunar market accounted for $3.3 billion of this investment. amounted to $272 billion The US-based space agency anticipates that the lunar network will expand along with the increase in activity on and around the Moon. It projects that with the establishment of the Lunar Gateway, its newly planned lunar space station, the Moon will require approximately 210 megabits per second and 4.6 terabytes per day, as stated in a from the agency in the previous year. As NASA prepares for a human mission to Mars around 2035, it foresees the Moon requiring as much as 950 megabits per second and 12 terabytes per day. presentation "In 2021 there were 13 landers, orbiters, and rovers on and around the Moon," Kelly Larson, CEO of Aquarian Space, in the statement. "By 2030, we will have around 200, creating a multibillion-dollar lunar economy.” said NASA is devising a plan to enhance its services by launching lunar satellites that will interconnect with each other and eventually link back to the communication infrastructure on Earth. will be especially beneficial for the challenging-to-reach parts of the Moon, such as the far side and the lunar south pole, which the agency aims to explore with the aid of astronauts in the future. Furthermore, the agency intends to establish a range of ground stations that will function like cell towers on the Moon's surface. The idea is to create a lunar communication system that operates similarly to an internet network rather than a phone line. The relay spacecraft Private companies, including Aquarian Space and Nokia, are contributing to the development of lunar internet infrastructure. Aquarian Space to launch its initial lunar communication satellites by Q2 2025, while Nokia is collaborating with NASA on a 4G cellular network for the Moon. Nokia a contract of over $14 million from NASA and is scheduled to deliver its first base station and radio equipment via a SpaceX rocket next year. plans has won Accurate navigation depends majorly on precise timekeeping as it is crucial in determining the exact location of a . The satnav receiver measures the time it takes for multiple satellite signals to reach it and converts this time measurement into an estimate of distance. satnav receiver Currently, every new expedition to the Moon is conducted on Earth's timeline, with the assistance of deep space antennas to ensure that the spacecraft's clocks are aligned with terrestrial time and to enable communication between the spacecraft and Earth. However, this approach will not be viable in the future lunar environment. There are over planned in the coming decade. These missions will not only occur simultaneously on or around the Moon, but they will also frequently interact with each other. This interaction may involve relaying communications, conducting joint observations, or carrying out rendezvous operations. 250 lunar missions Considering this, space agencies have initiated discussions on how to measure time on the Moon. These discussions began with a meeting at ESA's ESTEC technology center in the Netherlands in November of last year. "During this meeting at ESTEC, we agreed on the importance and urgency of defining a common lunar reference time, which is internationally accepted and towards which all lunar systems and users may refer to. A joint international effort is now being launched towards achieving this,” ESA navigation system engineer Pietro Giordano. said “Timekeeping and time distribution are essential for navigation performance and for maintaining synchronization across multiple assets. Time knowledge significantly impacts observation accuracy. Inaccuracies and differences among sources used in measurement time-tagging impart offsets relative to the true orbit location,”  states the conference paper. Observations commonly used in radio and optometric technologies include one-way and two-way range and Doppler measurements. These observations heavily rely on precise time-stamping, which must be referenced to a standardized time scale such as International Atomic Time (TAI). Establishing a universal lunar time presents a unique challenge as clocks on Earth and the Moon operate at different speeds due to the varying gravitational fields. As such, the form that an official lunar time would take is not clear. One possibility is that lunar time could be based on a clock system that is designed to synchronize with Coordinated Universal Time (UTC). Alternatively, lunar time could be entirely independent of Earth time, resulting in the creation of a unique time system specifically designed for the Moon. According to , a lunar clock would gain approximately 56 microseconds or millionths over 24 hours. The exact rate will be a function of their position on the moon, ticking differently on the lunar surface and orbit. Jeff Gramling's estimation Other than technical standpoints, geopolitics will play a major role in building the lunar economy. The entity that shapes the internet infrastructure on the Moon will wield significant influence, as it will have the authority to establish guidelines for how network providers collaborate and allocate lunar web addresses. Despite the U.S.'s efforts to bring other nations and space agencies into alignment with its , a set of international principles aimed at guiding lunar and outer space exploration and development, there is no assurance that all parties will agree to its terms. Notably, Russia and China are collaborating on their own . Artemis Accords lunar initiatives For effective collaboration, the international community must agree upon a shared "selenocentric reference frame," similar to the International Terrestrial Reference Frame used on Earth, which allows for accurate measurement of precise distances between points across the Moon. Such customized reference frames are crucial components of modern GNSS (Global Navigation Satellite Systems). Once the scientists are able to move past the challenges, the construction of stations will be open to astronauts’ stay and will be resupplied with periodic Artemis launches. Numerous uncrewed missions will also be in place – each Artemis mission alone will release numerous lunar – and ESA will be putting down its Gateway CubeSats . Argonaut European Large Logistics Lander

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