This article was published as a part of the Data Science Blogathon. Many a time while driving, we need to adjust things like in-vehicle seat relaxation function, interior lighting, music, air-conditioning, fragrances, etc, manually while attempting to keep our eyes on the road. However, it is very inconvenient and sometimes it can potentially put the driver and the co-passengers at the risk. Furthermore, it becomes even more complex when a previous driver has different preferences for cabin temperature, audio playlist, and so on. In light of this, would not it be convenient if our vehicle's heating, ventilation, illumination, music, visor, and air conditioning (HVAC) system could learn our individual preferences, and automatically make these adjustments for us?

In 2019, a team of researchers published a meta-review of studies claiming a person's emotion can be inferred from their facial movements. They concluded that there's no evidence emotional state can be predicted from expression – regardless of whether a human or technology is making the determination. "[Facial expressions] in question are not'fingerprints' or diagnostic displays that reliably and specifically signal particular emotional states regardless of context, person, and culture," the coauthors wrote. "It is not possible to confidently infer happiness from a smile, anger from a scowl, or sadness from a frown." Alan Cowen might disagree with this assertion.

Artificial intelligence is an on-demand technology that is booming like never before. And so many big tech companies are adopting and using it in various modes to provide their users with great products and services. Affectiva is one such company that is based in Boston that builds artificial intelligence which is capable of understanding human emotions, activities, cognitive states, and objects people use, by analysing facial and vocal expressions. The company was spun out of MIT Media Lab and created the new technology category of artificial emotional intelligence. Affectiva builds its artificial intelligence with various other technologies such as deep learning, speech analytics, computer vision, and other large amounts of data that have been collected in real-world scenarios.

"Our faces are organs of emotional communication; by some estimates, we transmit more data with our expressions than with what we say," says Raffi Khatchadourian in his article We Know How You Feel. Pioneers in the emotional communication space include Emotient, Realeyes, Sension, and Affectiva. All are competing to create emotionally responsive machines dedicated to decoding the human face, a notoriously difficult subject, who essence is rarely captured, even by the world's greatest artist. The BBC's claim that there are no less than 19 types of smiles that can break across a person's face, but only six are for happiness should give pause to the idea that reading people's faces is an easy thing to do. In his article, Raffi Khatchadourian profiles Affectiva, a startup specializing in cutting-edge AI technologies that applies machine learning, deep learning, and data science to emotional intelligence.

Dr. Rana el Kaliouby is the author of Girl Decoded and a leading expert on technology and empathy and the ethics of AI. This past June, Affectiva, the company she co-founded, was acquired by Smart Eye. In this virtual sit-down, we set out to learn more about what inspires Dr. el Kaliouby and how new innovations will change how we interface with technology and connect and communicate as humans. Q: Dr. el Kaliouby, tell us how you got started on your journey to exploring the role of emotion in today's technology-driven landscape? My education and career pursuits led me to Cambridge and later MIT, which meant I spent a lot of time in front of devices communicating with family back home.

Artificial Intelligence as we've come to know it is an incredibly complex tool. All manner of things can be analysed with AI, from shopping habits to logistical solutions. But what we're interested in here is using AI in speech and language recognition. Regular AI is really good at detecting intelligent speech, however it struggles to detect the emotional intention behind speech. When learning another language at school, we learn specifics – cat chat, dog chien. We don't learn the nuances behind the language until we experience them.

The emotion recognition software segment is projected to grow dramatically in coming years, spelling success for companies that have established a beachhead in the market, while causing some who are skeptical about its accuracy and fairness to raise red flags. The global emotion detection and recognition market is projected to grow to $37.1 billion by 2026, up from an estimated $19.5 billion in 2020, according to a recent report from MarketsandMarkets. North America is home to the largest market. Software suppliers covered in the report include: NEC Global (Japan), IBM (US), Intel (US), Microsoft (US), Apple (US), Gesturetek (Canada), Noldus Technology (Netherlands), Google (US), Tobii (Sweden), Cognitec Systems (Germany), Cipia Vision Ltd (Formerly Eyesight Technologies) (Israel), iMotions (Denmark), Numenta (US), Elliptic Labs (Norway), Kairos (US), PointGrab (US), Affectiva (US), nViso (Switzerland), Beyond Verbal (Israel), Sightcorp (Holland), Crowd Emotion (UK), Eyeris (US), Sentiance (Belgium), Sony Depthsense (Belgium), Ayonix (Japan), and Pyreos (UK). Some question whether emotion recognition software is effective, and whether its use is ethical.

AI has the potential to change the world in many amazing ways. But like every revolution, it requires fuel. It's long been said that "data is the oil of the information age," and that's certainly true in many ways. But while data is a less finite resource than actual oil, it does come with some challenges. People are (rightly) protective of their personal data, and there are compliance and regulatory responsibilities that must be upheld if we're using that personal data (often the most valuable kind of data) to power AI and generate predictions.

Emotion recognition is a branch of artificial intelligence that aims at identifying emotion in human faces. In the last decade, it has seen increased interest both in academia and the industry, and the market is expected to grow to $85 billion by 2025. It has several applications, most of them at the very least ethically questionable. It allows employers to evaluate potential employees by scoring them on empathy or emotional intelligence, among other traits. It helps teachers remotely monitor students' engagement in schools or while they do classwork at home.

We hear that machines are capable of many things we humans are. We know that they are even capable of more than us in some areas. But what about the fields that are harder for them or even impossible? Some AIs create music, write articles and even paint for us now. All of these are pieces that require some kind of emotional attachment to them, and many of them are considered art when created by humans.