AI Meets the Kitchen

10

October

2024

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When I was younger, I used to dislike cooking. It always seemed overwhelming to me since there were so many different techniques, recipes and ingredients. I was often confused, especially when trying to come up with meals based on whatever I had at home. However, when I began exploring different generative AI tools, my perspective shifted completely. These AI tools transformed cooking into something enjoyable and creative rather than a chore I needed to finish as soon as possible.

One of the most valuable tools in my cooking journey has been ChatGPT. Instead of spending hours browsing cooking websites or watching YouTube videos, I can simply ask ChatGPT what to cook using the ingredients I have at home. It generates personalized recipe ideas along with step-by-step instructions, which saves me time and reduces food waste by helping me make the most of what’s on hand. Additionally, I use it to create customized meal plans tailored to my dietary goals, such as increasing protein intake or following a gluten-free diet. Below is an example of a weekly meal plan I’ve created with ChatGPT’s help.


I also believe that generative AI can be a useful tool for menu and recipe creation in restaurants. By generating novel recipes based on specific criteria like ingredient availability or dietary preferences, AI helps restaurants stay competitive and offer unique menu options. It can assist chefs by suggesting adjustments, reducing the time needed for recipe development. AI simplifies parts of the creative process, allowing restaurants to consistently introduce new dishes while reducing the pressure on the culinary teams.

As generative AI continues to evolve, its potential to revolutionize at-home cooking and the restaurant industry continues to grow. Whether you are a beginner like myself or a professional chef, I believe that generative AI can enhance everyone’s cooking experience.

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Can artificial intelligence be creative?

21

September

2021

5/5 (1)

 Over the ages the human race has been presented as a superior to all other living creatures. One of the arguments for such bold assumption was that there are attributes unique to humans, such as the ability to use tools, language and form complex social structures. This belief has been long dismissed with scientific evidence, as numerous creatures in the animal kingdom have the same attributes, just expressed in a different way (Bok, 2019). In the era of rapid technological innovations a new philosophical question about the uniqueness of humans has been baffling the minds of people – Are we superior to artificial intelligence?

 History tends to repeat itself. So, naturally, in order to answer the question of whether we are superior to AI, humans started to look for ‘unique’ human attributes that will differentiate us from the AI. One trait seems to dominate the discussions as inherently unique – our creativity. The definition of creativity is ‘the ability to produce or use original and unusual ideas’ (Cambridge, n.d.). It has many forms –  form painting to writing novels and creating music.

The argument against the ability of AI to be creative is the fact that an algorithm can only preform what it’s programmed to do, thus it cannot achieve anything novel. Consequently, any achievement of the AI is a demonstration of the programmer’s ability, not the machine’s. However, this argument can also be used against human creativity (Vandegrift, 2016). Maybe you believe in God, Allah, Brahma or just Darwin, and if that’s the case, then our creativity can also be considered as a byproduct of someone or something. If we are the divine creators of AI, why shouldn’t we gift it the ability to be creative?

If we go back to the definition of creativity, it states: .. to produce OR use original and unusual ideas. Then if we think about it, AI is indeed using ideas ( given by the programmer) to create original and unusual products. In fact, various algorithms have already been showing creativity in many fields. The painting below is created by an AI that generates it based on a text input (Purtill, 2021).

Currently, there are so many examples of machine creativity that it even has it’s own field! If you are interested, check computational creativity. Below you can also find a very cool example of how humans and machines can work together and create some amazing art!

What do you think can AI be creative? Is it possible or is it indeed a trait unique to humans?  Let me know below 😊

References

Bok, V. (2019, September 7). What Distinguishes Us from AI? Retrieved from TowardsDataScience: https://towardsdatascience.com/what-distinguishes-us-from-ai-dc98f71de9a3

Cambridge. (n.d.). Creativity . Retrieved from Cambridge Dictionary : https://dictionary.cambridge.org/dictionary/english/creativity

Purtill, J. (2021, Jul 14). We asked a new kind of AI art tool to make ‘paintings’ of Australia. Retrieved from ABC: https://www.abc.net.au/news/science/2021-07-15/ai-art-tool-makes-paintings-of-australia/100288386

Vandegrift, D. (2016, 10 Jun). Can Artificial Intelligence Be Creative? Retrieved from Medium: https://medium.com/@DavidVandegrift/can-artificial-intelligence-be-creative-40e7eac56e71

Featured Image source: https://artist.com/art-recognition-and-education/art-and-the-singularity-how-will-artificial-intelligence-creative-robots-change-art/

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BIM, Meet Gertrude!

6

October

2020

Gertrude enjoying a well deserved drink during her performance. 

In August 2020, famous tech entrepreneur Elon Musk revealed his latest technological project: a pig called Gertrude. On first sight, Gertrude looks like an ordinary Pig. She seems healthy, curious, and eager to taste some delicious snacks. When looking at her, it is hard to imagine how she managed to get one of the world’s most radical and well known tech entrepreneurs so excited. Gertrude just seems normal.

This is exactly the point!

ElonMuskGotcha

Elon Musk “Gotcha”

Gertrude is no ordinary pig. She has been surgically implanted with a brain-monitoring chip, Link V0.9, created by one of Elon Musk’s latest start-ups named Neuralink.

Neuralink was founded in 2016, by Elon Musk and several neuroscientists. The short term goal of the company is to create devices to treat serious brain diseases and overcome damaged nervous systems. Our brain is made up of 86 billion neurons: nerve cells which send and receive information through electrical signals. According to Neuralink, your brain is like electric wiring. Rather than having neurons send electrical signals, these signals could be send and received by a wireless Neuralink chip.

To simplify: Link is a Fitbit in your skull with tiny wires

The presentation in August was intended to display that the current version of the Link chip works and has no visible side-effects for its user. The user, in this case Gertrude, behaves and acts like she would without it. The chip is designed to be planted directly into the brain by a surgical robot. Getting a Link would be a same day surgery which could take less than an hour. This creates opportunities for Neuralink to go to the next stage: the first human implantation. Elon Musk expressed that the company is preparing for this step, which will take place after further safety testing and receiving the required approvals.

The long term goal of the Neuralink is even more ambitious: human enhancement through merging the human brain with AI. The system could help people store memories, or download their mind into robotic bodies. An almost science-fictional idea, fuelled by Elon Musk’s fear of Artificial Intelligence (AI). Already in 2014, Musk called AI “the biggest existential threat to humanity”. He fears, that with the current development rate, AI will soon reach the singularity: the point where AI has reached intelligence levels substantially greater than that of the human brain and technological growth has become uncontrollable and irreversible, causing unforeseeable effects to human civilization. Hollywood has given us examples of this with The Matrix and Terminator. With the strategy of “if you cannot beat them, join them”, Elon Musk sees the innovation done by Neuralink as an answer to this (hypothetical) catastrophical point in time. By allowing human brains to merge with AI, Elon Musk wants to vastly increase the capabilities of humankind and prevent human extinction.

Singularity
Man versus Machine

So, will we all soon have Link like chips in our brains while we await the AI-apocalypse?

Probably not. Currently, the Link V0.9 only covers data collected from a small number of neurons in a coin size part of the cortex. With regards to Gertrude, Neuralink’s pig whom we met earlier in this article, this means being able to wirelessly monitor her brain activity in a part of the brain linked to the nerves in her snout. When Gertrude’s snout is touched, the Neuralink system can registers the neural spikes produced by the neurons firing electronical signals. However, in contrast: major human functions typically involve millions of neurons from different parts of the brain. To make the device capable of helping patients with brain diseases or damaged nervous system, it will need to become capable of collecting larger quantities of data from multiple different areas in the brain.

On top of that, brain research has not yet achieved a complete understanding of the human brain. There are many functions and connections that are not yet understood. It appears that the ambitions of both Elon Musk and Neuralink are ahead of current scientific understanding.

So, what next?

Neuralink has received a Breakthrough Device Designation from the US Food and Drug Administration (FDA), the organisation that regulates the quality of medical products. This means Neuralink has the opportunity to interact with FDA’s experts during the premarket development phase and opens the opportunity towards human testing. The first clinical trials will be done on a small group of patients with severe spinal cord injuries, to see if they can regain motor functions through thoughts alone. For now a medical goal with potentially life changing outcomes, while we wait for science to catch up with Elon Musk’s ambitions.

 Neuralink-Logo

Thank you for reading. Did this article spark your interest?
For more information, I recommend you to check out Neuralink’s website https://neuralink.com/

Curious how Gertrude is doing?
Neuralink often posts updates on their Instagram page https://www.instagram.com/neura.link/?hl=en

Want to read more BIM-articles like this?
Check out relating articles created by other BIM-students in 2020:

Sources used for this article:

4.88/5 (8)

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Using AI to Build Smart Cities

6

October

2019

No ratings yet. According to data presented by the UN it is estimated that the world population will grow to approx. 9.7 billion people by 2050. We are also seeing an increasing movement towards cities and it is estimated that almost 70% of the population will be living in urban areas (Medium, 2019). The cities must, therefore, be able to host a large number of inhabitants and additional amounts of commuters. The cities need to be able to provide energy and resources to all these people, whilst also removing waste and wastewater. Traffic is another issue. Furthermore, it is anticipated that these cities, many of which will house 10 million people, will consist of mixed nationalities, cultures, and backgrounds (Medium, 2019). Administration and management are therefore also focus-areas to create peaceful, prospering cities.

Many of these problems can be tackled using AI. This blog post will present some ideas discussed by Medium (2019) that might help battle the challenges presented by the large crowds of future cities.

Smart Traffic Management: Smart traffic solutions can be used to control the traffic flow and, consequently, avoid congestion. This can consist of road-surface sensors and cameras that will collect data in real-time, and a data system that is analyzing this data and offering recommendations to commuters to limit congestion issues.

Smart Parking: Again, road sensors will collect data and further notify the users of available parking spots nearby. Imagine finding a parking spot on your app and reserving it before you leave for your destination instead of aimlessly searching around the city for a parking spot for hours – wasting time and releasing emissions for every minute.

Smart Waste Management: Waste collection and disposal is an increasingly difficult challenge for the cities. Not only are they faced with more trash, but there is also an increasing public concern about proper disposal and recycling as the majority of people get more aware of climate issues. An example of a city in the foreground of smart waste management is Barcelona, where sensors are fitted on the trash bins which notifies the collection trucks when they are being filled. AI can also be used to design smarter routes for trash collection, or even automate the process with the use of robots.

Smart Policing: This is a rather controversial topic, where cities could use data-driven strategies to predict and catch criminal actions. This has already been implemented in Singapore, where a network of cameras and sensors monitors and notifies the authorities if criminal actions are happening. This might be difficult to implement in certain cities, as many populations are more skeptical towards surveillance and has a larger focus on privacy. The idea is still interesting, though.

As most people will find themselves living in cities in the future, the authorities of the cities will be extremely important in the development of our future world. The politics in the cities might in many cases be more significant than the politics countrywide. Cities should cooperate and share their smart solutions with other cities and create a positive loop which will contribute to creating a better world for humans and the planet.

Could you think of other smart initiatives that can help cities be more sustainable and liveable?

 

 

 

References:

Medium. (2019). Artificial Intelligence for Smart Cities. [online] Available at: https://becominghuman.ai/artificial-intelligence-for-smart-cities-64e6774808f8 [Accessed 6 Oct. 2019].

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Autonomous revolution: What else is going Driverless?

17

September

2018

In the autonomous revolution that is underway, virtually every means of transportation will be self-driving.

5/5 (3) In the autonomous revolution that is underway, virtually every means of transportation will be self-driving. The automotive industry might still need a few decades before the first driverless car is commercially available. But what about other industries? Be prepared to say good bye to your favourite cab driver, machinist,..and maybe even your human pilot.

Autonomous Ships – Riding the Wave of the Future
In 2016, freight shipping accounted for 90% of world’s trade, which amounts to around 10.3 billion tons of product. Besides freight shipping, industries such as wind farming, oil/gas, fishing and environmental research, have ships at sea. An over-crowded sea and the market size of freight shipping make and interesting case for startups and multinationals, such as Rolls Royce, to automate maritime vessels. The first vessels could become seaborne as soon as 2020. [1] [2] [3] [4] 

Fun fact: MIT is a frontrunner in the research on automated ships. The first major research project of autonomous boats in metropolitan areas is conducted in Amsterdam by MIT and the AMS  Institute [5]. Another interesting project in the Netherland is led by captainai.com, which is developing AI navigation software [6].

Autonomous Helicopters
The first successful heli projects already came to fruition. One of them is the AACUS using LiDAR, cameras, on-board computers and reinforcement learning algorithms. The AACUS has been developed by Aurora, acquired by Boeing. It already completed multiple flights and made first cargo deliveries in 2017. Through reinforcement learning, robotic helicopters teach themselves to fly difficult manoeuvres by observing other helicopters. After learning through observation, they can fly their own aerobatic stunt show. Autonomous helicopters are particularly interesting to the marine (think autonomous warfare), as well as for people and cargo transportation. Most research is currently conducted by the the marine, NASA and universities, such as Stanford. [7] [8]

And many more…
Other means of transportation expected to go autonomous include planes, trains, taxis, trucks and shuttles. In the past decades, major advances have been made, however, the autonomous revolution is still further ahead than some of us would like to think. Do you believe in the dawn of the autonomous revolution?

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Artificial intelligence and business model innovation: inseparable in the future?

13

September

2018

No ratings yet. Nowadays, Artificial Intelligence (AI) is a well-known concept among everyone. But, most of the people do not truly know what it means and what functions and purposes it has. Some people think that AI causes powerful robots that will take over our jobs for example. AI is already there, and we are using it, most of the time, without even knowing we are. At the McDonald’s you can place your order and pick up it up without even talking to one human being. Another example, all the personal advertisements we see on our smartphones are generated using AI.

AI is therefore really interesting for companies to implement in their business models. For example, the robots could be used to make the processes more efficient and, for example, to take over the standardized tasks of the employees, so they could really focus on being more creative and maintaining personal contact with customers. This could be extended by the fact that AI machines do not need any recurring trainings. Employees need to be trained and they can easily leave the company, causing another new employee that needs training.

Before business can implement AI into their business model, they should prepare their business for the arrival of AI. The process of gaining competitive advantage of AI consists of three phases. They need to generate data about the customers or processes they want to support with AI. After that, they should interpret this data, so that means transforming the data into information. Lastly, the company has to implement this information into the desired machines, in order to get the machine making decisions based on the imported information.

To conclude, AI offers very interesting opportunities to companies and their way of working. Companies can set up a team of people who are dedicated to developing the AI solutions within their company, but the most important part of become competitive is the data collection. Companies should see the importance of generating data as the first step in the process of implementing AI into their business model.

 

 

References

Ashwini, A. (2018, January 19). How To Create A Successful Artificial Intelligence Strategy. Retrieved from https://medium.com/swlh/how-to-create-a-successful-artificial-intelligence-strategy-44705c588e62

Barkman, A. (2018, January 23). How AI Impacts Business Model Innovation. Retrieved from https://www.techfunnel.com/information-technology/ai-impacts-business-model-innovation/

Sandehl, A. (2018, May 16). It’s Time To Adopt AI Into Your Business. Retrieved from Modelhttps://www.forbes.com/sites/forbesagencycouncil/2018/05/16/its-time-to-adopt-ai-into-your-business-model/#7fce247adcc3

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The blurring thin line between artificial and human creativity

9

September

2018

No ratings yet. Artificial Intelligence (AI) has already pervaded many industries. In recent years, a few AI use cases also caught the attention of the creative industry: Microsoft’s Chinese chatbot XiaoIce became capable of generating decent image-inspired poems, Sony started dishing out pop songs using its AI Lab’s FlowComposer, and 20th Century Fox asked IBM Watson to create a trailer for the horror movie “Morgan”. Now one has to wonder: Is the creative human mind still needed?

To answer this question, we need to look behind the scenes. For AIs to become “creative”, they have to be trained on relevant datasets first to understand what to read out of future inputs. In our examples, thousands of existing image-poem pairs were used for XiaoIce to learn how to find poetic clues in images; hundreds of songs of the same genre were fed into FlowComposer to make it adapt to different music styles; and Watson was forced to “watch” tons of horror movies to understand which scenes from “Morgan” may be useful for the trailer. Except for the poems, both the songs and the trailer actually also required extensive manual arrangement before release.

The results in all three categories are definitely remarkable for their technological achievement, but not as satisfying when compared to pure human creations. The poems, despite having passed the Turing test, tend to be more descriptive and bland rather than emotional or meaningful. The songs do show some typical characteristics of the respective genres, but are not very catchy due to the lack of recognizable motives. As for the trailer, it summarized the movie in an almost chaotic way, because the AI was trained to focus on salient emotions instead of the plot.

As we can see, AIs nowadays still miss a human touch when it comes to creating original content. And it is questionable if they will ever obtain real creativity since their outputs heavily depend on the datasets they were trained on. Yet, their ability to extract patterns from vast amounts of materials may help human creators see and break artistic boundaries to set new standards – something the creative industry urgently needs, and always should strive for.

 

Sources:

https://thenextweb.com/artificial-intelligence/2018/08/10/microsofts-ai-can-convert-images-into-chinese-poetry/

Click to access 1804.08473.pdf

https://www.scientificamerican.com/article/a-compendium-of-ai-composed-pop-songs/

https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/ai-creativity.html

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Can machines replace doctors anytime soon?

11

October

2017

No ratings yet. With each day some new developments in data science and machine learning are being published. The researchers constantly invent new ways to improve the performance of their predictive algorithms, thus improving the accuracy of the computers’ predictions on any given subject. One of such subjects, which should be of particular interest to us – humans – is medicine.

With the constant increase in the computation power and exponentially growing amounts of data, the researchers could potentially save a lot of lives with the use of advanced data science solutions. For example, research presented in the journal of the American Academy of Neurology states that the potential for artificial intelligence in precision medicine is significant. IBM Watson, a question answering computer system, provided a report of actionable insights within 10 minutes, in comparison to 160 hours of human analysis normally necessary to reach analogical conclusions (Monegain, 2017). This can often be of crucial difference when dealing with illnesses such as malign cancers, as some of them have a median survival of less than a few months following diagnosis.

Aside from genome analysis, there are countless areas in medicine where machine learning and artificial intelligence solutions can mean a difference between life and death (or very serious health complications). Currently, researchers around the world are trying to incorporate data science applications in detection of diseases such as autism, Parkinson’s, Alzheimer’s to name just a few! All of these sicknesses have one thing in common: early detection always improves the chances for a recovery or prevents further complications. And in this very sense, the AI can help people. Not only it is able to recognize the symptoms of a disease faster that a human can, but also it is able to do so at a larger scale, providing necessary diagnosis to the people in need. For instance, Google is launching an experiment to use machine learning to discover a diabetes-related eye disease in India, where the number of people with diabetes is around 70 million (with approximately 400 million people worldwide) and not a large percentage of them would normally receive a proper diagnosis in time (Simonite, 2017).

Another important factor, also connected to the accessibility, is the fact that a lot of people must postpone visits to the doctors because they cannot afford a private visit, while the wait time for a public one can even be around a year. And with the use of computers the time needed for an accurate diagnosis, as well as the number of specialists to be seen before obtaining it, is drastically reduced (Molteni, 2017).

Of course there are disadvantages of using AI in medicine. Firstly, there is a rising fear among the doctors that they will lose their jobs and be replaced by machines. According to an article in the New England Journal of Medicine, radiology and pathology are primarily susceptible to the power of AI, due to the fact that these jobs are based on pattern matching and machines can perform such tasks with surprising accuracy and speed (Asay, 2017). However, AI should mostly complement the work of doctors, enable them to perform their job more efficiently, thus help more people. Secondly, there are many issues regarding the data. Some people are not always aware that by signing a particular form they enable a company (or companies) to use their private data for research purpose (Molteni, 2017). What is more, for the algorithms to have a high accuracy, they need to have a lot of data at their disposal, which is not always feasible. For example, in the process of detecting the case of autism among kids, a lot of data needs to be gathered from MRI scans. This is not only time-consuming, but also expensive procedure. And it would not be realistic to have every child scanned in order to have a sufficient dataset (Vlasits, 2017).

Summing up, the rapid development of artificial intelligence and big data promises a future in which computers will be able to assist the doctors in providing quick and accurate diagnosis, thus saving human lives. However, one cannot forget that the algorithms should assist the doctors, instead of taking over their jobs. There will always be a fear that a machine provides an inaccurate prognosis, which can either scare the patient or make him calm, when he or she can be seriously sick. That is why human input is always valuable, to evaluate the machine’s diagnosis and verify its correctness. In other words, we should be very optimistic about the possibilities offered by artificial intelligence, but at the same time should not expect a bunch of robots treating patients in the hospital anytime soon.

References:

Asay M. (2017, January). Why AI is about to make some of the highest-paid doctors obsolete. Tech Republic, retrieved from: http://www.techrepublic.com/article/why-ai-is-about-to-make-some-of-the-highest-paid-doctors-obsolete/

Molteni, M. (2017, August). Want a diagnosis tomorrow, not next year? Turn to AI. Wired, retrieved from: https://www-wired-com.eur.idm.oclc.org/story/ai-that-will-crowdsource-your-next-diagnosis/

Molteni, M. (2017, September). 23andme is digging through your data for a Parkinson’s cure. Wired, retrieved from: https://www-wired-com.eur.idm.oclc.org/story/23andme-is-digging-through-your-data-for-a-parkinsons-cure/

Monegain, B. (2017, July). AI can speed up precision medicine, New York Genome Center-IBM Watson study shows. Healthcareitnews, retrieved from: http://www.healthcareitnews.com/news/ai-can-speed-precision-medicine-new-york-genome-center-ibm-watson-study-shows

Simonite, T. (2017, June). Google’s AI Eye Doctor Gets Ready to Go to Work in India. Wired, retrieved from: https://www.wired.com/2017/06/googles-ai-eye-doctor-gets-ready-go-work-india/

Vlasits, A. (2017, June). AI could target autism before it even emerges – but it’s no cure-all. Wired, retrieved from: https://www-wired-com.eur.idm.oclc.org/story/ai-could-target-autism-before-it-even-emerges-but-its-no-cure-all/

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The Future of Artificial Intelligence

3

October

2017

5/5 (1) Today artificial intelligence (AI) is receiving more and more attention from a company’s perspective, as it helps organizations doing things more efficiently as well as from a customer’s perspective, because people worry and fear about the capabilities of machines with artificial intelligence. This can be traced back by an old human memory of the Frankenstein’s monster in the 1960s. People fear about ultra-intelligence which means that a machine can far exceed every human intellectual activity whatsoever. As Floridi (2017) states that ‘’because the design of the machines is one of these intellectual activities an ultra-intelligent machine could design an even better machine’’.

Because of the threat of machines taking over human activities and becoming ‘’evil’’, it is very important that when, developing artificial intelligence or machine learning, the objective x is very well defined and includes all you care about. Nick Bostrom gave a good example of this in one of his TED videos. He brings up an old myth of King Midas (see figure 2). The King wishes that that everything he touches turns in to gold. However, as Bostrom says in the video ‘’he touches his daughter, she turns into gold. He touches his food, it turns into gold’’. This is not just a metaphor of greed but also shows what happens if you create a powerful optimization process with an ill-thought-out or badly specified goal (Bostrom, 2015).

Midas_gold2

Figure 2: King Midas and his daughter 

In my opinion, we should definitely be careful about artificial intelligence and the way we control it. Important here is to put our main focus on defining the goal of what a machine should do and how to do it. Without putting our own values at risk. In addition, we should find a way of controlling the AI and always understand their motivations of outcomes.

References

Bostrom, N. (2015) What happens when our computers get smarter than we are?

https://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are, 2 October 2017.

Floridi, L. (2017) Should we be worried about AI?

http://www.sciencefocus.com/article/future/should-we-be-worried-about-ai, 2 October 2017.

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Is Apple’s New Facial Recognition Can Become Revolutionary?

2

October

2017

No ratings yet. I am not a bit fan of Apple, and when I looked on the new IPhone X for the first time I thought its price tag is very expensive, especially when the Android technology is becoming stronger, friendlier and cheaper.
However, with its new iPhone, combined with the new iOS 11, Apple has done a breakthrough with regards to new emerging technologies, such as Augmented Reality (AR) and Artificial Intelligence (AI). When it comes to the latter one, Apple implemented its AI capabilities in their new facial recognition engine, which some say it will open doors to a new wave of use-cases and solutions..
From a first impression, it does sounds like a nice gimmick for the younger audience, that will help Apple to create a strong buzz to its new model. But under the veil of Marketing, Apple is thinking about something much bigger, which will probably lead to a fundamental infrastructure for a variety of new apps, services and authentication models. The main focus of Face ID technology is focusing on a higher level of security and privacy standards.

Phil Schiller, Apple's senior vice president of worldwide marketing, announces features of the new iPhone X at the Steve Jobs Theater on the new Apple campus on Tuesday, Sept. 12, 2017, in Cupertino, Calif. (AP Photo/Marcio Jose Sanchez)

According to Apple, its new Face ID is the most secure and safest way to unlock its iPhone. The chances someone else is breaking into the iPhone was 1 to a million, 40 times less likely than its current fingerprint mechanism. In addition, all the face ID data is being stored on a special chip in the processor, it never leaves the device, its being processed, encrypted, protected and optimized by using a AI mechanism on the phone itself. With its “TrueDepth” camera system in the front, Face ID projects 30,000 invisible dots on the owner’s face through infrared technology and maps the facial structure. Once the set-up is complete, Face ID matches its existing facial map to identify the user with every log-in attempt. Through machine learning, Face ID’s accuracy will improve, the company said.

With that being said, face recognition technology has already been here for quite a while, as Samsung and Microsoft already developed it 3 years ago. There are two main differences :
1. Unlike other companies, which use 2-D authentication, Apple’s technology provide a 3-D authentication, which is much more secure and comprehensive.
2. Apple Face ID is being processed, encrypted, and optimized on the phone itself, while other companies are using their AI algorithms on the cloud server. This is important to understand, because if the server or any channel along the way is being hacked, the facial data could be channeled to an unknown source.

it’s conceivable that the next generation of smartphones will eventually include sensors for face, iris, and fingerprint recognition. The cost isn’t that high for the hardware, and perhaps you’d use them in different combinations for different transactions—the user might decide which they want to use, or for a big purchase on the phone a merchant might want you to use all three.

But some legal questions remain, since police could unlock an iPhone X by pointing the phone at a suspect’s face without consent. But police officers cannot compel people to unlock their iPhones with their face without a search warrant as its against the U.S constitution.More importantly, Apple allows users to temporarily disable Face ID by gripping down on buttons on both sides of iPhone X. Nevertheless, when it comes to consumer-based products, Apple will continue to lead and become the premium benchmark of the industry.

 

References

Perala, (2017), “Just How Revolutionary is Apple’s Face ID System?”,

Just How Revolutionary Is Apple’s Face ID System?

Mafi, (2017), ”Apple’s iPhone X Hits Early Problems Due to Facial-Recognition Woes”, https://www.architecturaldigest.com/story/apple-iphone-x-hits-early-problems-facial-recognition-woes

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