AI Astronaut Assistant CIMON 1 & 2 designed by DLR, Airbus, and IBM to help ISS

There was a time where astronauts on the International Space Station were all alone. But with the advent of  Artificial Intelligence, now they feel less lonely. All thanks to CIMON, the first-ever free-flying AI Astronaut Assistant in space. Isn’t that amazing? Let’s know more about CIMON.

CIMON 1: The first free-flying AI Astronaut Assistant

Crew Interactive MObileCompanioN (CIMON) is an AI Astronaut Assistant that is developed by German space agency DLR, Airbus, and IBM. The project lead for this first free-flying AI astronaut, Matthias Biniok, was approached for this big project in 2016. Their main aim, was to build a robot and send it into space for providing assistance. Later, when DLR agreed to design it, Airbus went ahead to IBM to help this project by handling the AI aspect. The AI technology used by CIMON is IBM Watson Assistant, which is used worldwide by many IBM clients. The first AI astronaut, CIMON 1 was launched into space via a SpaceX Falcon 9 rocket on June 29, 2018.

The outcome of the project was a roughly spherical, 11-pound robot that could talk with astronauts living in the international space station. CIMON has a unique facial recognition software through which it knows who is talking to it. This AI astronaut has simple visual designs which allow it to show basic facial expressions too. CIMON can talk, understand and see things.

The ears of the AI astronaut consist of eight microphones to identify the directions of the sound. This little AI astronaut can travel independently over the European Columbus Research Module, which is part of the ISS. This AI robot was designed keeping in mind the situations in space. CIMON cannot harm nor fly into anything; it was approved by the researcher responsible for the mental health of the astronauts. CIMON proves to be a handy assistant with its simple and unique structure and design.

The main idea behind designing this AI robot, was to help astronauts in doing their work effectively with the use of Artificial Intelligence. And the other one is for giving the astronauts a companion who can talk to them.  CIMON can help the astronauts by giving them information immediately and can assist them in documenting the experiments as and when they are done. CIMON can also record and click pictures for the astronauts.  This is a scientific project awarded by DSL, designed by Airbus and IBM. CIMON-1 returned to Earth on August 29, 2019.

CIMON 2: The updated version of CIMON 1

CIMON 2, was also developed by Airbus for the German Aerospace Center Space Administration (DLR).  This new updated version of CIMON was launched into the ISS on December 05, 2019, with the help of the CRS-19 supply mission from Kennedy Space Center, Florida. CIMON 2 is yet to stay in ISS for 3years.

This new versioned AI astronaut has added features of autonomous flight capabilities, voice-controlled navigation, and also can understand and carry out various tasks. The microphones of the updated version are more sensitive and advanced to find the direction of the sound. The complex AI software has also been improved along with the battery extending more up to 30% than the CIMON 1. The special feature of CIMON 2 is that it can also analyse the emotion in the language and can show empathy while interacting with the astronauts.  This is another achievement in the use of Artificial Intelligence in human space flight.

CIMON and the future

CIMON is just a first step in designing AI robots that can be useful in space centers. CIMON can help in two ways- one is by offering an objective viewpoint without any stress of space travel and the second is it can be the best companion in space. Biniok, the project lead, hopes to see CIMON’s value as a companion and as an assistant which grows in the near future by supporting ISS and journeys to Mars and Moon. CIMON 2 will also be able to help researchers with scientific experiments carried on the ISS in the future.

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Source – https://www.institutionalinvestor.com/

Forecasting 2020 was near impossible. Here’s how the buyside’s favorite equity analysts did it.

In a year unlike any that came before, how does one predict what will happen next?

In 2020, that was the major challenge facing equity research analysts, whose job it is to forecast stock prices and make judgement calls about which companies will succeed or fail. Usually, analysts can rely on past data and analysis to tell them about how a company or sector might perform in the near future. But as the coronavirus pandemic unfolded, it became clear that the answers weren’t going to be found in historic data, according to Juan Luis Perez, group head of group research and analytics at UBS.

“A year ago it was incredibly difficult to forecast how the year was going to play out,” Perez said by phone. “We took the view that it was probably dangerous to try to extrapolate past trends” — for example, how markets recovered from the global financial crisis of 2008 and how the economy was impacted by the last major pandemic in 1918. “The year required a lot of adaptation, and it was very important not to come with very clear beliefs about how this was going to go,” he added.

For UBS — which has made a name for itself in quantitative, data-driven insights — this meant taking in as much new information as possible, as quickly as possible. To provide the best real-time research in a constantly changing environment, the firm’s teams of analysts and data scientists had to deliver “speed and quality,” according to Dan Dowd, the firm’s global head of research. This included responding quickly to client questions and updating views on thousands of stocks in a very short period time, he said.

“We tried from the very beginning to listen as much as we could to what companies were telling us,” Perez added. “We had to be very agile, we had to listen a lot, and from the very beginning we understood that we had to update projections very quickly.”

Their efforts paid off: For the fourth year in a row, UBS has ranked as the world’s top equity research provider in Institutional Investor’s 2020 ranking of the Global Research Leaders. The Swiss bank held onto its crown after racking up 158 team positions for research coverage across the U.S., developed Europe, Latin America, Asia, China, Japan, and the emerging markets of Europe, the Middle East, and Africa.

This time, UBS was closely followed by JPMorgan Chase & Co., which added 24 equity research team positions to its total over the course of 2020, for a final tally of 150 positions globally. The second-place finish is a big step up for JPMorgan, which placed fourth in 2019’s ranking of the world’s top equity research firms.

Like UBS, JPMorgan relied on real-time data and quantitative insights to make sense of the unfolding pandemic and its impact on markets and the economy. “We followed statistics on new cases and hospitalizations to better understand the nature of the virus,” Marko Kolanovic, the bank’s global head of quantitative and derivatives strategy, said by email. “Perhaps even more important was gauging the size and the timing of extraordinary monetary and fiscal measures and their impact on corporate earnings and equity valuation multiples.”

In the face of “extraordinary” market developments, including all-time-high volatility and the fastest market sell-off in history, Kolanovic said it was also important for analysts to keep track of the positioning and flows coming from different groups of investors including hedge funds, institutions, quant funds, retail investors, and others. “Research skills that had to be employed to deliver value to clients ranged from epidemiology to macro-economics, data science, and market microstructure,” he said.

All of this was gobbled up buy-side clients, who looked to sell-side research firms to help them navigate a period of extreme uncertainty.

“Investors’ appetite for information and insights was voracious,” said Noelle Grainger, global head of equity research at JPMorgan. “We saw an increased focus on fundamental bottom-up research, including written research, multi-media content, and time spent talking to analysts, experts and companies.”

To meet this need, Grainger said JPMorgan increased research report publication by 10 percent as readership accelerated. Analysts also spent more time on phone and video calls chatting with clients — an increase from of about 15 to 20 percent from 2019, she said.

Likewise, Dowd and Perez reported a dramatic increase in readership and client engagement at UBS. According to Dowd, some of the best-received publications were a new series of research reports called “Future Reimagined,” which examined what the world would look like after Covid-19.

But while the pandemic was the biggest story of 2020, it wasn’t the only topic that investors were interested in last year. Demand for environmental, social, and governance research also increased in 2020, as the pandemic “heightened everyone’s sense of what were the most important, underestimated risks,” Dowd said.

Interest in alternative data — like that produced by the team of data scientists at UBS Evidence Lab — also continued to grow, Dowd said.

“The use of extra-financial data is becoming not a nice thing to have but a core component of research,” Perez added. He said that UBS has deployed a team of social scientists, data analysts, and machine learning experts — led by Barry Hurewitz, global head of UBS Evidence Lab innovations — to help the firm’s equity analysts better think about and use data.

Dowd “thought it was essential that we built the capabilities to make sure analysts would incorporate data,” Perez said. In addition, the firm is focusing on helping buy-side clients incorporate data-driven research into their investment processes through the UBS Research Academy, Dowd said.

As the Covid-19 pandemic continues to shape markets and policy, Perez warned that economic and stock estimates will remain “very noisy,” with the potential for a lot of dispersion between analysts’ forecasts.

“The responsibility of our firm and every firm is to try to shorten this gap as much as possible,” he said. “You need a framework that belongs mostly to research on what makes you think you’re right and what makes you think you’re wrong. And you need high-resolution data to help you understand what’s going on.”

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