Tag Archives: Machine Learning

What are Olfactory Sensors?

We depend on our five senses to help us understand the world around us. Each of the five senses—touch, sight, smell, hearing, and taste—contributes individual information to our brains, which then combines them to create a better understanding of our environment.

Today, with the help of technology like ML, or machine learning, and AI, or Artificial Intelligence, we can make complex decisions with ease. ML and AI also empower machines to better understand their surroundings. Equipping them with sensors only augments their information-gathering capabilities.

So far, most sensory devices, like proximity and light-based ones, remain limited as they need clear physical contact or line of sight to function correctly. However, with today’s technology trending towards higher complexity, it is difficult to rely solely on simple sensing technology.

Olfaction, or the sense of smell, functions by chemically analyzing low concentrations of molecules suspended in the air. The biological nose has receptors for this activity, which, on encountering these molecules, transmit signals to the parts of the brain that are responsible for the detection of smell. A higher concentration of receptors means higher olfaction sensitivity, and this varies between species. For instance, compared to the human nose, a dog’s nose is far more sensitive, allowing a dog to identify chemical compounds that humans cannot notice.

Humans have recognized this superior olfactory ability in dogs and put it to various tasks. One advantage of olfaction over that of sight is the former does not rely on line-of-sight for detection. It is possible to detect odors from unseen objects, which may be obscured, hidden from sight, or simply not visible. That means the olfactory sensor technology can work without requiring invasive procedures. That makes olfactory sensors ideally suited for a range of applications.

With advanced technology, scientists have developed artificial smell sensors to mimic this extraordinary natural ability. The sensors can analyze chemical signatures in the air, and thereby unlock newer levels of safety, efficiency, and early detection in places like the doctor’s office, factory floors, and airports.

The healthcare industry holds the most exciting applications for olfactory sensors. This is because medical technology depends on early diagnosis to provide the most effective clinical outcomes to patients. Conditions like diabetes and cancer cause detectable olfactory changes in the body’s chemistry. Using olfactory sensors to detect the changes in body odor, with their non-invasive nature, provides a critical early diagnosis that can significantly improve the chances of effective treatment and recovery.

The industry is also adopting olfactory sensors. Industrial processes often produce hazardous byproducts. With olfactory sensors around, it is easy to monitor chemical conditions in the air and highlight the buildup of harmful gases that can be dangerous beyond a certain level.

As the sense of smell does not require physical contact, it is ideal for detection in large spaces. For instance, olfactory sensors are ideal for airport security, where they can collect information about passengers and their belongings as they pass by. All they need is a database of chemical signatures along with processing power to analyze many samples in real-time.

The Law, Big Data, and Artificial Intelligence

We use a lot of electronic gadgets in our lives, revel in Artificial Intelligence, and welcome the presence of robots. This trend is likely to increase in the future, as we continue to allow them to make many decisions about our lives.

For long, it has been a common practice using computer algorithms for assessing insurance and credit scoring among other things. Often people using these algorithms do not understand the principles involved, and depend on the computer’s decision with no questions asked.

With increasing use of machine learning and predictive modeling becoming more sophisticated in the near future, complex algorithm based decision-making is likely to intrude into every field. As such, expectedly, individuals in the future will have further reduced understanding of the complex web of decision-making they are likely to be subjected to when applying for employment, healthcare, or finance. However, there is also a resistance building up against the above, mainly in the EU, as two Oxford researchers are finding out from their understanding of a law expected to come into force in 2018.

With increasing number of corporations misusing data, the government is mulling the General Data Protection Regulation (GDPR), for imposing severe fines on these corporations. GDPR also contains a clause entitling citizens to have any machine-driven decision processes explained to them.

GDPR also codifies the ‘right to be forgotten’ while regulating the overseas transfer of private data of an EU citizen. Although this has been much talked about, not many are aware of two other clauses within GDPR.

The researchers feel the two clauses may heavily affect rollout of AI and machine learning technology. According to a report by Seth Flaxman of the Department of Statistics at the University of Oxford and Bryce Goodman of the Oxford Internet Institute, the two clauses may even potentially illegalize most of what is already happening involving personal data.

For instance, Article 22 allows individuals to retain the right not to be subject to a decision based solely on automatic processing, as these may produce legal complications concerning them or affect them significantly.

Organizations carrying out this type of activity use several escape clauses. For instance, one clause advocates use of automatic profiling—in theory covering any type of algorithmic or AI-driven profiling—provided they have the explicit consent of the individual. However, this brings up questions whether insurance companies, banks, and other financial institutions will restrict the individual’s application for credit or insurance, simply because they have consented. This can clearly have significant effect on an individual, if the institutes turn him or her down.

According to article 13, the individual has the right to a meaningful explanation of the logic involved. However, organizations often treat the inner working of their AI systems and machine learning a closely guarded secret—even when they are specifically designed to work with the private data of an individual. After January 2018, this may change for organizations intending to apply the algorithms to the data of EU citizens.

This means proponents of the machine learning and AI revolution will need to address certain issues in the near future.