The Future of Facebook

30

September

2019

4.63/5 (8)

As a platform, it is key to understand how to attract and maintain your users. For a long time, Facebook managed to do this very effectively: it became a huge network very fast. Meanwhile, the company acquired WhatsApp and Instagram and built Facebook Messenger.

However, since the start of 2018, Facebook has received a lot of bad press, because multiple events showed that the company is struggling to protect the privacy of its users (TechTarget, 2019). Since people seem to become increasingly aware of the importance of their privacy, Facebook has to make a big move in order to maintain its userbase.

A few months ago, Facebook founder Mark Zuckerberg published a blogpost (Facebook, 2019) in which he describes the future the company. The blogpost included the following statement:

“I believe the future of communication will increasingly shift to private, encrypted services where people can be confident what they say to each other stays secure and their messages and content won’t stick around forever. This is the future I hope we will help bring about.”

In an interview with WIRED (2019), Zuckerberg adds to this that he sees a demand for two types of platforms in the future: town squares and living rooms. Town squares are platforms like Facebook and Instagram where people interact publicly and living rooms are platforms like WhatsApp and Facebook Messenger where people interact privately. Zuckerberg goes on by stating that his company has been very busy in the past 15 years with building services and products around the town square, while there is an increasing demand for the development of living room facilities. Therefore, the latter will be the main focus of Facebook in the coming years.

Interesting thoughts, right? It is clear that Zuckerberg wants to restore the reputation of Facebook and turn it into a brand that takes privacy seriously. At the same time, the new road has its own challenges:

First, Facebook earns money by selling targeted ads. How can they continue doing this when a significantly increasing part of their user data will become end-to-end encrypted? Zuckerberg himself admits that it will take some time before they exactly know what the impact on their business model will be.

Second, end-to-end encryption also means that it will be harder to prevent the spread of fake news. Furthermore, it facilitates private conversations between criminals. How can we then make sure Facebook has a positive impact on society?

Do you think Facebook can handle a transformation like this? And is Zuckerberg just working on the company’s reputation or does he really want to build a product that everyone loves?

SOURCES:

  1. https://www.facebook.com/notes/mark-zuckerberg/a-privacy-focused-vision-for-social-networking/10156700570096634/
  1. https://www.wired.com/story/mark-zuckerberg-facebook-interview-privacy-pivot/
  1. https://www.vox.com/2019/3/6/18253461/mark-zuckerberg-facebook-private-messaging-future-whatsapp-messenger
  1. https://www.inc.com/larry-kim/mark-zuckerberg-makes-it-facebook-official-future-of-facebook-is-messaging.html
  1. https://www.theverge.com/2019/3/6/18253472/mark-zuckerberg-facebook-letter-privacy-encrypted-messaging
  1. https://searchsecurity.techtarget.com/news/252462588/A-recent-history-of-Facebook-security-and-privacy-issues
  1. https://www.vox.com/2019/3/7/18254298/facebook-private-messaging-zuckerberg-questions-social-network-dying

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The End of an Era of Discussion: Messi vs. Ronaldo

30

September

2019

5/5 (3)

Every day, enormous amounts of data are produced by businesses and consumers, forming vast data sets, both structured and unstructured. Such sets are increasingly used to guide strategic decision making, by means of big data analytical processes.

Big data analytics helps businesses to become more effective, hereby generating more revenue and/or profit. Depending on the desired outcome of the analysis, analytical processes can be descriptive, prescriptive, diagnostic or predictive (Banerjee, Bandyopadhyay & Acharya, 2013).
However, besides organizational decision-making focussed on costs, revenues and ultimately shareholder value, big data analytics can be used to guide decision making regarding one of the more trivial questions buzzing around society the last couple of decades, such as: ‘Which footballer is actually the best: Christiano Ronaldo or Lionel Messi?’.

For over 20 years, fans, experts, players and coaches have not reached consensus about which player may be called the greatest of all time. Both sides of the argument have embayed numerous arguments and statistics to solve this seemingly unsolvable question. However, by using big data analytics, Decroos, Bransen, Van Haaren & Davis (2019) have finally produced an undebatable and unambiguous answer to the question.

Using data mining and machine learning techniques, these researchers from the University of Leuven, Belgium, have been able to examine the statistics of both players more extensively than researcher had done before. By creating the VAEP (Valuing Actions by Estimating Probabilities) framework, the researchers were able to accurately determine the value of football player’s actions. Besides focussing on assists and goals, the researchers included three more elements in their analysis:
1) The value of the player’s action types (passes, crosses, dribbles etc)
2) The value of the player’s action type with regards to the game context
3) The possible effect of a player’s action on subsequent actions

Using these elements, the researchers were able to calculate the value of football players not only based on quantifiable statistics such as goals, assists and successful passes and dribbles, but also included the value these actions deliver. The analysis shows that Messi is superior in terms of action quantity and quality, which is shown in the table below.Messi

Without the use of big data analytics techniques, an area that is imminently growing both in size and in capabilities, the debate between supporters of Messi and Ronaldo would possibly never been solved, and none of the two players could have been called greatest of all time with certainty. However, by means of the study by Decroos et al. (2019), the end of an era of discussion between who is the best is now marked.

Sources

Banerjee, A., Bandyopadhyay, T., & Acharya, P. (2013). Data analytics: Hyped up aspirations or true potential?. Vikalpa, 38(4), 1-12.

Decroos, T., Bransen, L., Van Haaren, J., & Davis, J. (2019, July). Actions speak louder than goals: Valuing player actions in soccer. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1851-1861). ACM.

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Skiing and AI?

30

September

2019

5/5 (2)

I doubt that many of us will like the current weather situation in Rotterdam, nevertheless I do have good news. Winter is around the corner – and with it comes the ski season. Since I am a big fan of skiing, I will dedicate this post on how AI and tech are being used on the slopes. I will present 3 interesting innovations that have been commercialized: 

  • Carv – Digital Ski Coach 

Yes, it might sound strange, but Carv might revolutionize ski instructions. Carv provides personalized skiing tips in real-time – just like a ski coach, but over your smartphone speaker or headset.  An article of the Financial Times is well describing how Carv works: “Hidden in my boots are two sensor-packed footbeds, communicating via Bluetooth to the smartphone in my jacket pocket, which in turn is reporting back to a server in Frankfurt. With each turn I throw up a spray of snow and a cloud of data. […] An average turn lasts about 1.5 seconds, during which time it will have collected and analyzed more than 5.000 pieces of information.”  

  • Emma – Epic Mountain Assistant

The way we can think of Emma is Siri for the slopes. A smart assistant for all snow-related queries with real-time information. According to the Telegraph, the queries include “snow conditions, queue times for lifts to your favorite runs and the status of the backside gullies, to where to head to for a family friendly meal, the best deal in resort on a new jacket, or which bars will have a happy hour later”. Moreover, it can be integrated with the apps of the ski resort which can track your speed, distance travelled or altitude changes. 

  • Skadi – AR ski app 

Similar to Emma, Skadi is an app that is used during skiing. The difference is that Skadi works like a mountain guide who can choose new routes for you and makes sure that you do not miss your last lift. The goal is “to encourage skiers and snowboarders to explore more. Without Skadi, most visitors to a resort only tackle about 15 per cent of the ski area. Skadi opens that up and encourages visitors to try new runs” (Telegraph). Skadi has other futuristic functions that even rely on augmented reality. This means that the app combines real-time environment from the camera of the phone with visual content created like a computer game. For example, you can ski on the slope while hunting for marmots or bears, similar to Pokémon Go. 

 

What do you think of these developments? Are they distracting you from the beauty of nature or do they help making winter sports an even greater hobby?  

 

Bibliography 

https://www.ft.com/content/049f15ce-d798-11e8-a854-33d6f82e62f8 

https://getcarv.com/ 

https://www.telegraph.co.uk/travel/ski/news/siri-for-the-slopes-vail-resorts-launch-emma/ 

https://www.telegraph.co.uk/travel/ski/articles/new-ski-app-skadi-launches-sat-nav-and-augmented-reality-on-the-slopes/ 

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Does Uber have a future?

30

September

2019

5/5 (1)

Uber has updated its app in a way that CEO Khosrowshahi has branded as the beginning of Uber’s step to becoming the ‘Amazon of transportation’ (Hawkins, 2019). The app includes new elements such as Bike lane alert, Improved Real-Time ID check and Verify your ride (Vasile, 2019). Besides new safety features, the app will now integrate UberEATS so riding and ordering can be done in one app, under the same Uber umbrella. More about the safety features later, first let’s take a look at why the company decided to also integrate public transit information into its app and why it believes that this will be useful since we already have an app for this (Google Maps, anyone?).

Uber is trying to expand from simply being an app to becoming a true platform business, targeting city life and transportation. As Khosrowshahi put it: “We want Uber to be the operating system for your everyday life” (Nuttall, 2019), meaning that it is attempting to build a complete experience for their consumers and aims to become a city life partner on all fronts: food, ride-hauling and even public transit. In a recent interview with The Verge’s reporter Andrew Hawkins, Uber’s CEO revealed the company’s ambitions in providing a platform that not only provides information but allows you to take action, advancing previous CEO Kalanick’s goal to brokerage all human movement in cities (Hawkins, 2019). However, what makes the Uber app more special than for example Google Maps or Citymapper? Khosrowshahi demonstrates the app and explains how it will be a comparable experience but provide all services in one place and allows customers to take action in the application, increasing app engagement which will provide more business (read: data) down the road (Hawkins, 2019). The choice to integrate public transit information, and eventually ticketing, into the app was not motivated by money: Khosrowshahi simply explains that it hopes to complement transit, offer Uber users all options and therefore cater to the individual user’s needs, whether that be timeliness or budget. Khosrowshahi says the company aims for profitability in the long run, achieved by creating “the right solutions for consumers, even if it’s not making them money” (Hawkins, 2019).

The decision to incorporate transit information is not entirely random, as Uber, Lyft and other ride-hailing apps have been proven responsible for declining rates of public transit usage; both rail and bus ridership falling by 1-2% after the entrance of a ride-hailing app into the market (Graehler et al., 2019). As clarified by the CEO, Uber wants to complement transit, beside the fact that it has competition anyways, it merely wants to provide its users with all options, not compete or draw customers away from public transit. Uber released its beta version where users can see transit schedules, directions and some ticketing options in a few cities like San Francisco, Mexico City and Paris on September 27th (Hawkins, 2019).

Some other features in the app were included to improve the privacy and safety of both riders and drivers. The most important feature being Verify Your Ride, which uses a four-digit pin code that needs to be verbally communicated to drivers, to ensure riders meet their paired drivers and do not take the wrong car (Vasile, 2019). Other features encompass a 911-alert function through the app, as well as Bike lane alert that notifies riders when they get dropped off near bike lanes to prevent ‘dooring’ bicyclers. Lastly, the company incorporated a better Real-Time ID Check to guarantee Uber drivers match the account in the company’ systems. All these features are implemented to increase safety surrounding Uber after significant security-related issues in the past.

This update sounds good, but these new features also sound like they should have been incorporated all along and are targeted at relieving the pressure the company has faced around privacy and safety issues in the past, think Grey Ball and God View (Hawkins, 2019). The company has improved its firewalls and introduced a Report Safety Incident function that allows riders to report concerns during their trip (instead of only after), to regain riders’ trust and prevent future reports of kidnappings, sexual assaults and sometimes even deaths that have occurred in the past (Silicon Canals, 2019).

Uber has not only struggled with safeguarding its users but also has reported billions in losses over the past years and is of yet unable to turn its business profitable. With a $3 billion operating loss and an accumulated deficit of almost $8 billion in 2018, the company could be in serious trouble now that its earnings are being monitored as it has issued its IPO earlier this year (Poletti, 2019). Since the innovative self-driving cars will most likely not arrive soon enough to save Uber business model, their unprofitable business model will probably result in price hikes for rides to cover costs and improve profitability, but will riders accept these higher prices or simply revert to one of the many alternatives (public transit, Lyft, grab etc.). Further, major investors’ lockup periods are about to end in early November, which might have disastrous consequences for the company’s stock. The financial and security matters are enough to get investors worried, yet Uber also faces legislative and environmental challenges. A few examples are the AB5 California bill undermining its current business model by enforcing drivers to be recognised as employees that receive benefits, democratic candidates placing blame on Uber and Lyft for increased congestion problems, and prolonged efforts to retain its operating licenses in European cities like London (Hawkins, 2019).

This leaves the question if Uber will survive the existential crises it is currently strung up in. Despite Uber’s positive claims that it expects to be around in the future, it will first need to survive the present. With many global and local challengers like autonomous driving, Grab, Lyft or Bolt (Silicon Canals, 2019), competition has arrived and a simple app update will not solve the bigger existential threats that are attacking Uber from all fronts: legislative, financial and environmental. Do you think Uber will crawl its way to the top and become the urban city life-app it desires to be, or will it fall from grace and be forced out of business by financial and legal difficulties?

Leave your thoughts and comments below!

References

Graehler, M., Mucci, A., & Erhardt, G. D. (2019). Understanding the Recent Transit Ridership Decline in Major US Cities: Service Cuts or Emerging Modes?. In Transportation Research Board 98th Annual Meeting, Washington, DC, January.

Hawkins, A. J. (2019). Exclusive: INSIDE UBER’S PLAN TO TAKE OVER CITY LIFE WITH CEO DARA KHOSROWSHAHI. [online] The Verge.  Available at: https://www.theverge.com/2019/9/26/20885185/uber-ceo-dara-khosrowshahi-interview-exclusive [Accessed 30 September 2019).

Nuttall, C. (2019). All hail Uber’s everything app. [online] Financial Times. Available at: https://www.ft.com/content/85e5b38e-e149-11e9-9743-db5a370481bc [Accessed 30 September 2019].

Poletti, T. (2019). Opinion: Uber and Lyft IPOs mean the cheap rides are coming to an end. [online] MarketWatch. Available at: https://www.marketwatch.com/story/uber-and-lyft-ipos-mean-the-cheap-rides-are-coming-to-an-end-2019-05-09 [Accessed 30 September 2019].

Silicon Canals (2019). Uber to focus on rider’s safety with new features, but here are 7 alternatives if you’re in London. [online] Silicon Canals. Available at: https://siliconcanals.com/news/startups/uber-focus-on-riders-safety-new-features/ [Accessed 30 September 2019].

Vasile, C. (2019). Uber launches new mobile app, adds important new features. [online] phoneArena.com. Available at: https://www.phonearena.com/news/Uber-new-mobile-app-new-features_id119278 [Accessed 30 September 2019].

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Are Sidewalks safe?

30

September

2019

5/5 (1)

In today’s world data is power. Therefore, the technological giants; including Amazon, Google and Facebook; are going to great lengths to collect, analyze and utilize information about their customers (Haber, 2019). By extending their product and service offerings they strive to gain more and more insights into the demands and purchasing patterns of clients with a view to design and propose even more new revenue-bringing gadgets and solutions (Rijmenan, 2016).
Next to the already wieldy known approaches, including collaborative filtering and click-following techniques, those companies are starting to introduce more tangible devices, such as Alexa or Google Smart Home. Up till now, it was up to the customer to decide whether or not they want to equip themselves in those expensive watchdogs.

However, recently at the hardware event, Amazon presented two new additions to the data-collecting family: Sidewalk and eero. The former is a wireless protocol that aims to link smart devices. The latter, is a Wi-Fi router. With an aid of those, Amazon will be able to significantly increase the coverage of areas that it controls, in terms of continuous customer data collection, without customers even realizing it. The biggest threat that these new inventions impose on the privacy is the fact that users no longer need to be logged into the network provided by Amazon or poses Amazon’s devices – eero and Sidewalk can track smartphones that are in the close enough proximity to them. And as far this information is as least to say concerning, Amazon claims that by placing 700 of their newly-developed devices in a city of a size of Los Angeles (1,302 km2, population 4 million) they would be able to fully control the entire city. Now this is actually becoming scary. (Haber, 2019).

In this case, what is a common user’s chance to protect their privacy? Is it still save to walks on the sidewalks? Or shall we start imaging a life without smartphones as an only alternative to constantly being tracked? Will regulative bodies or another companies come to rescue with stricter privacy-protecting legislations or track-blocking devices?

Sources:
https://en.wikipedia.org/wiki/Los_Angeles

https://www.nytimes.com/interactive/2019/technology/tech-investigations.html

https://www.inc.com/matt-haber/why-google-facebook-amazon-really-want-you-to-have-a-screen-based-smart-device-in-your-house.html

https://www.businessinsider.com/amazon-may-soon-be-able-to-track-your-phone-location-2019-9

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Hide and seek, not only a game for children but also for AI

30

September

2019

5/5 (2)

AI (Artificial intelligence) surprises us every day. The basic concept of a technology which can be taught is something people still find difficult to comprehend. However, AI is not planning on slowing down and allowing these people to get used to the idea, it is quite the opposite. Last week AI has made a new development which once again indicates how fascinating and human-like AI can be. Researchers created a program that facilitated multi-agent reinforcement learning. This is the concept of placing two algorithms in a competitive environment which ensures emergent behavior and reinforcement learning. This was done by a game of hide and seek. This seems like a very ordinary game but it facilitates all the aspects needed to show this behavior.

 

Interestingly AI showed 6 strategies over time which were getting more and more developed and none were initially programmed. All these strategies are similar to human development in the way they were organized and their learning sequence.

In the first phase, the hiders and seekers learn strategies and counter-strategies.

In the second phase, the hiders learn to use tools and alter their environment. They build shelters to create better hiding spots.

In the third phase, the seekers learn this alteration as well to enter the hiding spots of the hiders by using ramps.

The fourth phase happens when hiders learn to lock ramps so that the seekers can not use them anymore to enter their shelters.

The most fascinating phase is probably the fifth one. In this phase, seekers use the blocks also present in the game. The ramps can not be moved but the seekers can ‘surf’ on the boxes, therefore, increasing height and ‘surf’ over the walls the ramps has created.

The sixth phase happens when the hiders learn to lock the boxes as well.

AI

All these phases happened over 380 million rounds of training. What makes this case so interesting is that the researchers decided to end this trial after around 500 million rounds. They explained that they initially wanted to end the training around phase 4 as they believed this was the end phase, but then phase 5 and 6 occurred. Therefore, who knows what would have happened if they would have let it run for another 500 million rounds? This once again shows how unexpected AI can be, and how it is still difficult for us to grasp the skills AI is capable of.

 

Bibliography

Technologyreview (2019) available at: https://www.technologyreview.com/s/614325/open-ai-algorithms-learned-tool-use-and-cooperation-after-hide-and-seek-games/

Towardsdatascience (2019) available at: https://towardsdatascience.com/openai-tried-to-train-ai-agents-to-play-hide-and-seek-but-instead-they-were-shocked-by-what-they-3ea32bf7fc95

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Data Process Mining: “Less sexy, but more valuable than Big Data”

30

September

2019

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Data Process Mining: “Less sexy, but more valuable than Big Data”

Organizations are increasingly registering data that can be used to analyze processes. Unfortunately, traditional data mining and BI (Business Intelligence) techniques fall short due to the absence of a clear process orientation. Process mining, on the other hand, bridges the gap between data mining and model-based process analysis. This makes it possible to show what is actually happening. By playing historical data, it is also possible to show where the bottlenecks are and where processes deviate from the ideal path. These insights are extremely relevant for both the IT professional and the business consultant (Process Mining: Wat gebeurt er nu echt? … en hoe kan het beter?, n.d.). This paper will elaborate more on the fundaments of process mining and the opportunities for the market.

Process mining essentially works as follows: people who use computer systems within an organization, leave traces. The large amounts of stored data are analyzed to show how processes are proceeding, with the aim of ultimately making them more efficient. Process mining exploits the information recorded in event logs to perform an analysis of the real process afterwards. There is a distinction between three types of process mining:

1. Discovery: With the help of clever analysis techniques that search for frequently occurring patterns, it is possible to derive process models automatically. These models provide insight into what really happens within a process or organization.
2. Conformance: The found process models often deviate greatly from the normative process descriptions that often assume an ideal situation that has little to do with reality. In order to map deviations between such an idealized process description and the actual state of affairs, so-called “conformity checking” techniques are used. These can show the degree of compliance (for example, “80% of the events are going according to plan”) and where the largest deviations can be found in the process (for example, “this monitoring activity is frequently skipped”)
3. Enhancement (extension): where the process models are adapted and improved according to the data of the real process.
Process mining consists of two main steps:
• Step 1: Process selection and prioritization, which clearly establishes the improvement objectives and identifies where the business value is created in different parts of the organization and how high-level processes affect the creation of value (Robledo, 2018).
• Step 2: capture of the process information to be improved to represent it as a process model (Robledo, 2018).
Professor Wil Van der Aalst is considered an international pioneer in data analysis of business processes (Kleyngeld, 2018). Van der Aalst started working on process mining in the late ‘90s, as he saw that “IT-projects fail often because the difference between what people said what they did and what they really did was large.” (Kleyngeld, 2018). Professor Wil Van der Aalst: “At the time we started doing this, the data about human actions was limited and we still collected data by letting people keep logs themselves. It has since evolved into a mature field. There are 25 suppliers of process mining software and around 200 organizations that work with it in the Netherlands.” (Kleyngeld, 2018).

Moreover, ResearchandMarkets explains that the global process analytics market size is expected to grow from $185.3 million (2018) to $1,421.7 (2023), which is almost 8 times within 5 years. One of the main factors for this grow is that the implementation of Digital Transformation is driving awareness of users to understand business processes and analyze them (Robledo, 2018). Furthermore, Europe represents the largest market size, where Germany and The Netherlands are the top countries that contribute to the process analytics market. The U.S. shows the highest opportunity for growth in the following years. Will process mining thrive and become the mainstream way of analyzing your processes?

Bibilography
TUE (n.d). Process Mining: Wat gebeurt er nu echt? … en hoe kan het beter? Retrieved from TUE: http://wwwis.win.tue.nl/~wvdaalst/publications/p635.pdf
Kleyngeld, J. (2018, January 9). Process mining: “Minder sexy, maar waardevoller dan big data”. Retrieved from CFO: https://cfo.nl/artikel/process-mining-het-einde-van-politieke-discussies-in-bedrijven-
Robledo, P. (2018, September 23). Process Mining Plays an essential role in Digital Transformation. Retrieved from Medium: https://medium.com/@pedrorobledobpm/process-mining-plays-an-essential-role-in-digital-transformation-384839236bbe

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The rise of Dataism

30

September

2019

5/5 (1)

In his book, Homo Deus: A Brief History of Tomorrow, author Yuval Noah Harari describes amongst many things the rise of “Dataism”. The current homo-centric worldview, according to which human life and values are placed above all else, is predicted to be replaced by a dystopia where data is at the center of the human experience (Harari, 2017).

Dataism is a term that first appeared in a New York Times article in 2013 (Brooks, 2013), where it is referred to as “the rising philosophy of the day”. The term has been broadened and further described as a worldview which celebrates humans’ experiences as data processing, categorizes individuals and organizations as algorithms who are programmed to function in a specific way and measures the value of human life in terms of its ability to transform experience into data.

New as the term may sound, the concept of processing and passing on information lies at the foundation of human societies. Today food, products, people and information can travel all around the globe quickly, safely and efficiently. According to (Brinson, 2019) all of human history can be seen as making this processing and transportation system more efficient.

So, what lies ahead?

The pursuit of the Dataist is to create an efficient system known as Internet-of-All-Things, a system that includes every literal thing, from sentient beings to animals, plants, machines, and manufactured goods. If the goal is the processing of information, the next logical step would be to extend the network of data extraction to include everything within our reach.

“The refrigerator will monitor the number of eggs in the drawer, and inform the chicken coop when a new shipment is needed. The cars will talk with one another, and the trees in the jungle will report on the weather and on carbon dioxide levels” (Brinson, 2019).

In this new interconnected world, the individual becomes a tiny chip within a giant system, unable to process or even comprehend the amount of data that surrounds him. In the data-driven world, power lies with organizations who have the will, infrastructure, access, and know-how to harness the vast amount of data available. Everyday life will be changing drastically within the following decades, from the way we consume goods, work, interact and collaborate with one another. It remains to be seen which tech giants will emerge to serve as leaders of the Dataist era, but current industry leaders such as Google and Amazon easily come to mind.

To end with a quote from Y.Harari himself, “What will happen to society, politics and daily life when non-conscious but highly intelligent algorithms know us better than we know ourselves?”

___________________________________________________________________________________________________

References

Brinson, S. (2019). Dataism: God is in the Algorithm. [online] Medium. Available at: https://medium.com/understanding-us/dataism-god-is-in-the-algorithm-84af800205cd [Accessed 29 Sep. 2019].

Brooks, D., 2013. The philosophy of data. New York Times, 4, p.2013.

Harari, Y. (2017). Homo Deus. New York, NY: Harper, an imprint of HarperCollins Publishers.

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Google Translate Determines Refugee Status

30

September

2019

5/5 (1)

We are connected to people from all around the world through social media. The translate button allows us to understand text from our foreign friends. As I am writing this article, I need help from Google Translate to translate some words. You might be doing that as well. It is a simple tool to interpret from and to convert to another language. We know that it is not reliable enough to use it on its own. Google has warned that translation tools should not be relied on for complex tasks. However, in the US it is being used in the qualification of refugee application.

The US government allows the use of machine translation tools for the vetting procedure of refugees. A manual has been set up for the US immigration officials on how to use Google Translate step-by-step. Screening the social media of applicants is part of the procedure and helps to decide whether a refugee is allowed into the country. The manual only applies to refugees whose parents or spouses have been admitted to the US or also defined as follow-to-join cases. The officials are instructed to utilize free automated translation services and can decide whether to use an expert in translation. However, officers might lack fluency in the foreign language and therefore would not request a language expert for a second revision (Torbati, 2019). Nonetheless, the automated translation services have difficulties in translating culture-based texts or idioms and, therefore, are prone to make mistakes which can cause misinterpretation.

The machine translation is improving but is not meant as a replacement for human translators. Improvements have been made with neural machine translation, algorithms that uses artificial intelligence to predict the sequence of sentences using phrases (Siu, 2018). However, we should not rely heavily on this kind of technology while making critical decisions.

To what extend do you think we should use automated translation in the decision-making process?

Sources:
Siu, D.N. (2018) Google Translate is getting a big upgrade with improved offline mode. Available at: https://mashable.com/2018/06/12/google-translate-offline-mode-ai/?europe=true
Torbati, Y. (2019) Google says Google Translate can’t replace human translators. US immigration officials have used it to vet refugees. Available at: https://www.businessinsider.com/google-translate-us-immigration-officials-use-app-vet-refugees-2019-9

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Is AI ready for healthcare?

29

September

2019

5/5 (3)

Artificial intelligence algorithms can improve the performance of radiologists, by improving the speed and accuracy of diagnosing their patients. The algorithms can interpret and give diagnoses on X-rays, CT scans and other images (Kim & Holzberger, 2019). However, the drawback is that AI only focuses on one question and hence, one answer. Each purpose would need its own algorithm, forcing developers to create thousands of algorithms (Kim & Holzberger, 2019). However, AI marketplaces gives access to a variety of AI models and hence, tries to solve this problem. Also, it asks for feedback to refine its algorithms. Moreover, Forbes (2019) claims that AI could help to solve a big problem in healthcare, namely the ‘iron triangle’. The triangle exist of the three factors access, affordability and effectiveness. AI can decrease costs, but also make treatment improvements and create a great accessibility. Forbes (2019) also sees a great future in the use of AI on robots that assist operations.

Even though prospects look great, we also need to discuss the other side. Of course, there is a large difference in using AI in for example buying stock and in using it to diagnose or operate a real human being. Is it ethical to use AI in healthcare? Whose fault will it be when someone passes away due to a fault in the algorithms? Also, AI gives an output it cannot further explain, and it can even rely on bias due to the data it has been provided with (Sanofi, 2019). AI algorthms can of course contain errors that lead to serious consequences (Keshinbora, 2019). So, how can one explain an outcome to a patient if the outcome is based on a very complex system that cannot explain itself? In order to do that, we need AI systems that can explain other AI systems, called XAI (Sanofi, 2019). I believe that AI is not ready for large healthcare decisions yet until it reaches full reliability and until its choices can be explained. What do you think?

Sources:

Forbes. (2019) AI and Healthcare: A Giant Opportunity. [Online] Available from: https://www.forbes.com/sites/insights-intelai/2019/02/11/ai-and-healthcare-a-giant-opportunity/#2fa19d5b4c68 .

Keskinbora, K. (2019) Medical ethics considerations on artificial intelligence. Journal of Clinical Neuroscience. 64(6), 277-282.

Kim, W. & Holzberger, K. (2019) What AI ”App Stores” Will Mean for Riadiology. Harvard Business Review. [Online] Available from: https://hbr.org/2019/06/what-ai-app-stores-will-mean-for-radiology .

Sanofi (2019). The Ethics of AI in Healthcare. [Online] Available from: https://www.sanofi.com/en/about-us/our-stories/the-ethics-of-ai-in-healthcare .

 

 

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