This requires a delicate balance between engaging smart, predictive technologies, such as ML and AI, and honoring the ethical standards that build trust between brands and customers. The trend toward collecting and cultivating zero- and first-party data through an open and transparent value exchange with consumers has become table stakes for creating personalized customer experiences that result in long-term loyal relationships. But using only historical data to build black-box algorithms that continuously learn upon themselves can perpetuate intrinsic biases with regard to such characteristics as gender, race, ethnicity and economic status. Ethical AI offers a unique opportunity to establish trust with consumers, who I've found are increasingly seeking humanized communication from the brands with which they engage. The question of ethics in marketing and AI has been a hot topic for some time, and many brands have worked hard to ensure their targeting algorithms aren't biased.

Predictive analytics is bringing smarter insights and better efficiency into many areas of our lives, even if we aren't always aware of it. Take healthcare, for example, a sector that has been firmly in the spotlight in recent months. Scientists have recently combined self-reported symptoms data and artificially intelligent (AI) modeling to predict which early signs of COVID-19 can be used for faster detection. Interest in using data stores to identify useful patterns is also sparking growing commercial interest. Companies that have watched leading players such as Amazon harness AI engines to fuel ever-more refined product suggestions are starting to see these advanced tools as a potential solution to many issues.

Understand how predictive marketing supports your goals: Predictive models can benefit organizations in any industry, but each strategy must be defined by unique challenges. The SalesForce State of Sales report found that account-based sales teams are 2.4 times more likely to seek out predictive technology. If an organization's goal is quantifiable, there's a good chance the company can benefit from a predictive model. Assess your model: Machine learning algorithms cannot read the future; they are fallible. The models employed by your organization should be reviewed and maintained by an expert.

DAN Programmatic, empowered by the Data Sciences Division of the Dentsu Aegis Network in India along with AMNET, the network's programmatic arm has announced the launch of'DAN Vision for Creative', a machine learning and neuroscience based engine. According to the company, it will deliver insights into the predicted performance of a creative even before the creative has actually run. The company added that'DAN Vision for Creative', through its sophisticated technology, endeavors to accurately present a visual heat map of effective elements of a creative and forecast the likelihood of its performance based on algorithms that have been trained over the performance of thousands of creatives and analysing millions of elements. Read Also: Agency Reckoner 2018-19: Top 15 Digital Marketing Agencies "One of the biggest game changers in modern marketing is the use of data to better the decision making capabilities of professionals to yield better outcomes. Creativity without data is a shot in the dark. Even when a creative professional claims to follow his gut, he or she is in reality analysing data generated through experiences for a particular outcome. Having co-founded an agency that deploys new age solutions to solve common problems, 'DAN Vision for Creative' is just the right weapon to solve the age old creative problem of "Why are certain creatives performing better than the others?." It is creating for us a unique differentiator by providing a justification or even starting points for our creative approach to deliver brand success," Sidharth Rao, CEO and co-founder, Dentsu Webchutney, said.

Healthcare organizations across the country are beginning to cash in on early efforts in artificial intelligence and data visualization. First reports on initial efforts to use these advanced technologies show tantalizing potential. Indeed, these clinical applications of AI and data visualization are where most healthcare organizations are concentrating early efforts in using this advanced assistive and predictive technology, according to results of a recent poll of Health Data Management readers. More than half (53 percent) of respondents to the survey say their organizations are using these advanced analytic technologies to improve clinical decision making. That's far and away the leading use of this information technology, but a range of other applications are being implemented, respondents say.

Approaching the end of current fiscal year, I take a serene glance at the memorable moments and guess what? I am quick to realize there was hardly any conference, webinar, debate, event or discussion in which there was no mention about Artificial Intelligence and its subset technologies, specifically Machine Learning, Deep Learning, Reinforcement Learning and Neural Networks. In fact, the latest 3-days conference I attended was titled'When Human Becomes Digital' and was centred around artificial intelligence. Analysts, as well as technologists, believe AI has and will continue to reign the world of technology for at least the next 2-3 years. The Ripple Effect created by these emerging technologies will gradually perish.

Artificial Intelligence – specifically machine learning and deep learning – was everywhere in 2018 and don't expect the hype to die down over the next 12 months. The hype will die eventually of course, and AI will become another consistent thread in the tapestry of our lives, just like the internet, electricity, and combustion did in days of yore. But for at least the next year, and probably longer, expect astonishing breakthroughs as well as continued excitement and hyperbole from commentators. This is because expectations of the changes to business and society which AI promises (or in some cases threatens) to bring about go beyond anything dreamed up during previous technological revolutions. AI points towards a future where machines not only do all of the physical work, as they have done since the industrial revolution but also the "thinking" work – planning, strategizing and making decisions.

SAN FRANCISCO – Alphabet Inc.'s Google in May introduced a slick feature for Gmail that automatically completes sentences for users as they type. Tap out "I love" and Gmail might propose "you" or "it." But users are out of luck if the object of their affection is "him" or "her." Google's technology will not suggest gender-based pronouns because the risk is too high that its Smart Compose technology might predict someone's sex or gender identity incorrectly and offend users, product leaders revealed in interviews. Gmail product manager Paul Lambert said a company research scientist discovered the problem in January when he typed "I am meeting an investor next week," and Smart Compose suggested a possible follow-up question: "Do you want to meet him?"

Artificial Intelligence – specifically machine learning and deep learning – was everywhere in 2018 and don't expect the hype to die down over the next 12 months. The hype will die eventually of course, and AI will become another consistent thread in the tapestry of our lives, just like the internet, electricity, and combustion did in days of yore. But for at least the next year, and probably longer, expect astonishing breakthroughs as well as continued excitement and hyperbole from commentators. This is because expectations of the changes to business and society which AI promises (or in some cases threatens) to bring about go beyond anything dreamed up during previous technological revolutions. AI points towards a future where machines not only do all of the physical work, as they have done since the industrial revolution but also the "thinking" work – planning, strategizing and making decisions.

Google is removing gender pronouns from the predictive text feature found in its Gmail platform. The feature will no longer suggest pronouns that indicate a specific gender such as'he', 'her', 'him' or'she' for fear of suggesting the wrong one and causing offence. Google staff have revealed the technology will not suggest gender-based pronouns because the risk is too high that its'Smart Compose' technology might predict someone's sex or gender identity incorrectly. The California-based firm's Smart Compose feature is found in Gmail which has 1.5 billion users around the world. Gmail product manager Paul Lambert said a company research scientist discovered the problem in January.