Enjoy this CGI 3D Animated Short Film and winner of over 50 film festival jury and audience awards including Best Short Film, Best Sci-Fi Film, Best Animated Film, Best Production Design, Best Visual Effects, and Best Sound Design. During the construction of the universe, a young member of the Cosmos Corps of Engineers decides to break some fundamental laws in the name of self-expression.
NASA researchers have found large quantities (2.8 parts per billion) of acrylonitrile* (vinyl cyanide, C2H3CN) in Titan’s atmosphere that could self-assemble as a sheet of material similar to a cell membrane.
Acrylonitrile (credit: NASA Goddard)
Consider these findings, presented July 28, 2017 in the open-access journal Science Advances, based on data from the ALMA telescope in Chile (and confirming earlier observations by NASA’s Cassini spacecraft):
Azotozome illustration (credit: James Stevenson/Cornell)
1. Researchers have proposed that acrylonitrile molecules could come together as a sheet of material similar to a cell membrane. The sheet could form a hollow, microscopic sphere that they dubbed an “azotosome.”
A bilayer, made of two layers of lipid molecules (credit: Mariana Ruiz Villarreal/CC)
2. The azotosome sphere could serve as a tiny storage and transport container, much like the spheres that biological lipid bilayers can form. The thin, flexible lipid bilayer is the main component of the cell membrane, which separates the inside of a cell from the outside world.
“The ability to form a stable membrane to separate the internal environment from the external one is important because it provides a means to contain chemicals long enough to allow them to interact,” said Michael Mumma, director of the Goddard Center for Astrobiology, which is funded by the NASA Astrobiology Institute.
Organic rain falling on a methane sea on Titan (artist’s impression) (credit: NASA Goddard)
3. Acrylonitrile condenses in the cold lower atmosphere and rains onto its solid icy surface, ending up in seas of methane liquids on its surface.
Illustration showing organic compounds in Titan’s seas and lakes (ESA)
4. A lake on Titan named Ligeia Mare that could have accumulated enough acrylonitrile to form about 10 million azotosomes in every milliliter (quarter-teaspoon) of liquid. Compare that to roughly a million bacteria per milliliter of coastal ocean water on Earth.
Chemistry in Titan’s atmosphere. Nearly as large as Mars, Titan has a hazy atmosphere made up mostly of nitrogen with a smattering of organic, carbon-based molecules, including methane (CH4) and ethane (C2H6). Planetary scientists theorize that this chemical make-up is similar to Earth’s primordial atmosphere. The conditions on Titan, however, are not conducive to the formation of life as we know it; it’s simply too cold (95 kelvins or -290 degrees Fahrenheit). (credit: ESA)
6. A related open-access study published July 26, 2017 in The Astrophysical Journal Letters notes that Cassini has also made the surprising detection of negatively charged molecules known as “carbon chain anions” in Titan’s upper atmosphere. These molecules are understood to be building blocks towards more complex molecules, and may have acted as the basis for the earliest forms of life on Earth.
“This is a known process in the interstellar medium, but now we’ve seen it in a completely different environment, meaning it could represent a universal process for producing complex organic molecules,” says Ravi Desai of University College London and lead author of the study.
* On Earth, acrylonitrile is used in manufacturing of plastics.
NASA Goddard | A Titan Discovery
Abstract of ALMA detection and astrobiological potential of vinyl cyanide on Titan
Recent simulations have indicated that vinyl cyanide is the best candidate molecule for the formation of cell membranes/vesicle structures in Titan’s hydrocarbon-rich lakes and seas. Although the existence of vinyl cyanide (C2H3CN) on Titan was previously inferred using Cassini mass spectrometry, a definitive detection has been lacking until now. We report the first spectroscopic detection of vinyl cyanide in Titan’s atmosphere, obtained using archival data from the Atacama Large Millimeter/submillimeter Array (ALMA), collected from February to May 2014. We detect the three strongest rotational lines of C2H3CN in the frequency range of 230 to 232 GHz, each with >4σ confidence. Radiative transfer modeling suggests that most of the C2H3CN emission originates at altitudes of ≳200 km, in agreement with recent photochemical models. The vertical column densities implied by our best-fitting models lie in the range of 3.7 × 1013 to 1.4 × 1014 cm−2. The corresponding production rate of vinyl cyanide and its saturation mole fraction imply the availability of sufficient dissolved material to form ~107 cell membranes/cm3 in Titan’s sea Ligeia Mare.
Flying warehouses, robot receptionists, smart toilets… do such innovations sound like science fiction or part of a possible reality? Technology has been evolving at such a rapid pace that, in the near future, our world may well resemble that portrayed in futuristic movies, such as Blade Runner, with intelligent robots and technologies all around us.
But what technologies will actually make a difference? Based on recent advancements and current trends, here are five innovations that really could shape the future
1. Smart homes
Many typical household items can already connect to the internet and provide data. But much smart home technology isn’t currently that smart. A smart meter just lets people see how energy is being used, while a smart TV simply combines television with internet access. Similarly, smart lighting, remote door locks or smart heating controls allow for programming via a mobile device, simply moving the point of control from a wall panel to the palm of your hand.
But technology is rapidly moving towards a point where it can use the data and connectivity to act on the user’s behalf. To really make a difference, technology needs to fade more into the background – imagine a washing machine that recognises what clothes you have put into it, for example, and automatically selects the right programme, or even warns you that you have put in items that you don’t want to wash together. Here it is important to better understand people’s everyday activities, motivations and interactions with smart objects to avoid them becoming uninvited guests at home.
Such technologies could even work for the benefit of all. The BBC reports, for example, that energy providers will “reduce costs for someone who allows their washing machine to be turned on by the internet to maximise use of cheap solar power on a sunny afternoon” or “to have their freezers switched off for a few minutes to smooth demand at peak times”.
A major concern in this area is security. Internet-connected devices can and are being hacked – just recall the recent ransomware attack. Our home is, after all, the place where we should feel most secure. For them to become widespread, these technologies will have to keep it that way.
2. Virtual secretaries
While secretaries play a very crucial role in businesses, they often spend large parts of their working day with time-consuming but relatively trivial tasks that could be automated. Consider the organisation of a “simple” meeting – you have to find the right people to take part (likely across business boundaries) and then identify when they are all available. It’s no mean feat.
Tools such as doodle.com, which compare people’s availability to find the best meeting time, can help. But they ultimately rely on those involved actively participating. They also only become useful once the right people have already been identified.
By using context information (charts of organisations, location awareness from mobile devices and calendars), identifying the right people and the right time for a given event became a technical optimisation problem that was explored by the EU-funded inContext project a decade ago. At that stage, technology for gathering context information was far less advanced – smart phones were still an oddity and data mining and processing was not where it is today. Over the coming years, however, we could see machines doing far more of the day-to-day planning in businesses.
On the downside, much of the required context information is relatively privacy-invasive – but then the younger generation is already happily sharing their every minute on Twitter and Snapchat and such concerns may become less significant over time. And where should we draw the line? Do we fully embrace the “rise of the machines” and automate as much as possible, or retain real people in their daily roles and only use robots to perform the really trivial tasks that no one wants to do? This question will need to be answered – and soon.
But how would you feel about receiving a diagnosis from an artificial intelligence? A private company called Babylon Health is already running a trial with five London boroughs which encourages consultations with a chatbot for non-emergency calls. The artificial intelligence was trained using massive amounts of patient data in order to advise users to go to the emergency department of a hospital, visit a pharmacy or stay at home.
The company claims that it will soon be able to develop a system that could potentially outperform doctors and nurses in making diagnoses. In countries where there is a shortage of medical staff, this could significantly improve health provision, enabling doctors to concentrate on providing treatment rather than spending too much time on making a diagnosis. This could significantly redefine their clinical role and work practices.
An increasing number of mobile apps and self-tracking technologies, such as Fitbit, Jawbone Up and Withings, can now facilitate the collection of patients’ behaviours, treatment status and activities. It is not hard to imagine that even our toilets will soon become smarter and be used to examine people’s urine and faeces, providing real-time risk assessment for certain diseases.
If AI systems can address these challenges and focus on understanding and enhancing existing care practices and the doctor-patient relationship, we can expect to see more and more successful stories of data-driven healthcare initiatives.
4. Care robots
Will we have robots answering the door in homes? Possibly. At most people’s homes? Even if they are reasonably priced, probably not. What distinguishes successful smart technologies from unsuccessful ones is how useful they are. And how useful they are depends on the context. For most, it’s probably not that useful to have a robot answering the door. But imagine how helpful a robot receptionist could be in places where there is shortage of staff – in care homes for the elderly, for example.
Robots equipped with AI such as voice and face recognition could interact with visitors to check who they wish to visit and whether they are allowed access to the care home. After verifying that, robots with routing algorithms could guide the visitor towards the person they wish to visit. This could potentially enable staff to spend more quality time with the elderly, improving their standard of living.
The AI required still needs further advancement in order to operate in completely uncontrolled environments. But recent results are positive. Facebook‘s DeepFace software was able to match faces with 97.25% accuracy when tested on a standard database used by researchers to study the problem of unconstrained face recognition. The software is based on Deep Learning, an artificial neural network composed of millions of neuronal connections able to automatically acquire knowledge from data.
5. Flying warehouses and self-driving cars
Self-driving vehicles are arguably one of the most astonishing technologies currently being investigated. Despite the fact that they can make mistakes, they may actually be safer than human drivers. That is partly because they can use a multitude of sensors to gather data about the world, including 360-degree views around the car.
Moreover, they could potentially communicate with each other to avoid accidents and traffic jams. More than being an asset to the general public, self-driving cars are likely to become particularly useful for delivery companies, enabling them to save costs and make faster, more efficient deliveries.
Advances are still needed in order to enable the widespread use of such vehicles, not only to improve their ability to drive completely autonomously on busy roads, but also to ensure a proper legal framework is in place. Nevertheless, car manufacturers are engaging in a race against time to see who will be the first to provide a self-driving car to the masses. It is believed that the first fully autonomous car could become available as early as the next decade.
The advances in this area are unlikely to stop at self-driving cars or trucks. Amazon has recently filed a patent for flying warehouses which could visit places where the demand for certain products is expected to boom. The flying warehouses would then send out autonomous drones to make deliveries. It is unknown whether Amazon will really go ahead with developing such projects, but tests with autonomous drones are already successfully being carried out.
Thanks to technology, the future is here – we just need to think hard about how best to shape it.
Amazon’s Alexa just got a new job. In addition to her other 15,000 skills like playing music and telling knock-knock jokes, she can now also answer economic questions for clients of the Swiss global financial services company, UBS Group AG.
According to the Wall Street Journal (WSJ), a new partnership between UBS Wealth Management and Amazon allows some of UBS’s European wealth-management clients to ask Alexa certain financial and economic questions. Alexa will then answer their queries with the information provided by UBS’s chief investment office without even having to pick up the phone or visit a website. And this is likely just Alexa’s first step into offering business services. Soon she will probably be booking appointments, analyzing markets, maybe even buying and selling stocks. While the financial services industry has already begun the shift from active management to passive management, artificial intelligence will move the market even further, to management by smart machines, as in the case of Blackrock, which is rolling computer-driven algorithms and models into more traditional actively-managed funds.
But the financial services industry is just the beginning. Over the next few years, artificial intelligence may exponentially change the way we all gather information, make decisions, and connect with stakeholders. Hopefully this will be for the better and we will all benefit from timely, comprehensive, and bias-free insights (given research that human beings are prone to a variety of cognitive biases). It will be particularly interesting to see how artificial intelligence affects the decisions of corporate leaders — men and women who make the many decisions that affect our everyday lives as customers, employees, partners, and investors.
Already, leaders are starting to use artificial intelligence to automate mundane tasks such as calendar maintenance and making phone calls. But AI can also help support more complex decisions in key areas such as human resources, budgeting, marketing, capital allocation and even corporate strategy — long the bastion of bespoke consulting firms such as McKinsey, Bain, and BCG, and the major marketing agencies.
One might argue that corporate clients prefer speaking to their strategy consultants to get high priced, custom-tailored advice that is based on small teams doing expensive and time-consuming work. And we agree that consultants provide insightful advice and guidance. However, a great deal of what is paid for with consulting services is data analysis and presentation. Consultants gather, clean, process, and interpret data from disparate parts of organizations. They are very good at this, but AI is even better. For example, the processing power of four smart consultants with excel spreadsheets is miniscule in comparison to a single smart computer using AI running for an hour, based on continuous, non-stop machine learning.
In today’s big data world, AI and machine learning applications already analyze massive amounts of structured and unstructured data and produce insights in a fraction of the time and at a fraction of the cost of consultants in the financial markets. Moreover, machine learning algorithms are capable of building computer models that make sense of complex phenomena by detecting patterns and inferring rules from data — a process that is very difficult for even the largest and smartest consulting teams. Perhaps sooner than we think, CEOs could be asking, “Alexa, what is my product line profitability?” or “Which customers should I target, and how?” rather than calling on elite consultants.
Another area in which leaders will soon be relying on AI is in managing their human capital. Despite the best efforts of many, mentorship, promotion, and compensation decisions are undeniably political. Study after study has shown that deep biases affect how groups like women and minorities are managed. For example, women in business are described in less positive terms than men and receive less helpful feedback. Minorities are less likely to be hired and are more likely to face bias from their managers. These inaccuracies and imbalances in the system only hurt organizations as leaders are less able to nurture the talent of their entire workforce and to appropriately recognize and reward performance. Artificial intelligence can help bring impartiality to these difficult decisions. For example, AI could determine if one group of employees is assessed, managed, or compensated differently. Just imagine: “Alexa, does my organization have a gender pay gap?” (Of course, AI can only be as unbiased as the data provided to the system.)
In addition, AI is already helping in the customer engagement and marketing arena. It’s clear and well documented by the AI patent activities of the big five platforms — Apple, Alphabet, Amazon, Facebook and Microsoft — that they are using it to market and sell goods and services to us. But they are not alone. Recently, HBR documented how Harley-Davidson was using AI to determine what was working and what wasn’t working across various marketing channels. They used this new skill to make resource allocation decisions to different marketing choices, thereby “eliminating guesswork.” It is only a matter of time until they and others ask, “Alexa, where should I spend my marketing budget?’’ to avoid the age-old adage, “I know that half my marketing budget is effective, my only question is — which half?”
AI can also bring value to the budgeting and yearly capital allocation process. Even though markets change dramatically every year, products become obsolete and technology advances, and most businesses allocate their capital the same way year after year. Whether that’s due to inertia, unconscious bias, or error, some business units rake in investments while others starve. Even when the management team has committed to a new digital initiative, it usually ends up with the scraps after the declining cash cows are “fed.” Artificial intelligence can help break through this budgeting black hole by tracking the return on investments by business unit, or by measuring how much is allocated to growing versus declining product lines. Business leaders may soon be asking, “Alexa, what percentage of my budget is allocated differently from last year?” and more complex questions.
Although many strategic leaders tout their keen intuition, hard work, and years of industry experience, much of this intuition is simply a deeper understanding of data that was historically difficult to gather and expensive to process. Not any longer. Artificial intelligence is rapidly closing this gap, and will soon be able to help human beings push past our processing capabilities and biases. These developments will change many jobs, for example, those of consultants, lawyers, and accountants, whose roles will evolve from analysis to judgement. Arguably, tomorrow’s elite consultants already sit on your wrist (Siri), on your kitchen counter (Alexa), or in your living room (Google Home).
The bottom line: corporate leaders, knowingly or not, are on the cusp of a major disruption in their sources of advice and information. “Quant Consultants” and “Robo Advisers” will offer faster, better, and more profound insights at a fraction of the cost and time of today’s consulting firms and other specialized workers. It is likely only a matter of time until all leaders and management teams can ask Alexa things like, “Who is the biggest risk to me in our key market?”, “How should we allocate our capital to compete with Amazon?” or “How should I restructure my board?”