This story was written in collaboration with Forbes Finds. Forbes Finds covers products and experiences we think you'll love. Featured products are independently selected and linked to for your convenience. If you buy something using a link on this page, Forbes may receive a small share of that sale. Marc Andreessen's call that "software is eating the world" has proven quite prescient.

Big data was everywhere about 10 years ago, now the same is happening to Artificial Intelligence (AI). No single conference we attend, no single article we go through, no executive we talk to, or the term Artificial Intelligence is mentioned. Any self respecting software publisher, has the urge to mention in one way or the other Artificial Intelligence. When we dig deeper, mostly their AI are either automated processes or machine learning based algorithms providing specific models, for very specific and niche use cases. The real AI, as we know it from science fiction movies, is still far off.

DeepMind, a subsidiary of Alphabet (Google's parent company) is working to remove the inherent human biases from machine learning algorithms. The increased deployment of artificial intelligence and machine learning algorithms into the real world has coincided with increased concerns over biases in the algorithms' decision making. From loan and job applications to surveillance and even criminal justice, AI has been shown to exhibit bias – particularly in terms of race and gender – in its decision making. Researchers at DeepMind believe they've developed a useful framework for identifying and removing unfairness in AI decision making. Called Causal Bayesian Networks (CBNs), these are visual representations of datasets that can identify causal relationships within the data and help experts identify factors that might be unfairly weighed against or skewing others.

Currently, the digital media is in a transitional phase, where the format of the medium is changing from text-based to one with visuals. Due to this significant shift, advertising has to play catch up, to stay up-to-date with the latest trends the industry. On top of that, the marketing industry has to deal with ad-blockers, which blocks out intrusive advertisements. According to a study done by PageFair, there are at least 615 million devices that use Adblock regularly. As you can imagine, getting through these ad-blockers is an uphill task, because they keep disruptive advertisements at bay.

Artificial intelligence is developing in unprecedented ways and its future is unpredictable. Facebook announced recently that they had to deactivate one of their systems for an unheard of reason: it had begun to think for itself. The system developed its own language and tech experts still aren't sure how. Although this particular case got a lot of publicity, it isn't the only one that we've seen in recent years. In the past, other AI developers have seen some machines try to do things by themselves.

Roborace is the world's first competition for human AI teams, using both self-driving and manually-controlled cars. Race formats will feature new forms of immersive entertainment to engage the next generation of racing fans. Through sport, innovations in machine-driven technologies will be accelerated. A self-driving car has set a speed record of 175 mph. In November 2017, Musk said the next Tesla Roadster would have three motors and be able to travel a whopping 0 to 60 miles per hour in 1.9 seconds with a top speed of 250 mph or even more.

Delaying "brain age" may sound like the latest quick-fix gimmick on a late-night infomercial, but the science underlying the concept is very real. Rather than reflecting the average functional state of your chronological age, brain age looks at how well your brain is aging relative to how many birthdays you've celebrated. We all know people that seem sharper and act much younger than their age--that incredulous moment when you realize the 40-year-old you've been chatting with on the plane is actually a grandma in her 70s. Brain age, as a concept, hopes to capture the biological intricacies behind that cognitive dissociation. Longevity researchers have increasingly realized that how long you've lived isn't the best predictor of overall health.

In management, business, economics, scien ce, engineering, and research domains, L arge Scale Global Optimization (LSGO) plays a predominant and vital role. Though LSGO is applied in many of the application domains, it is a very troublesome and a perverse task . The Congress o n Evolutionary Comp utation (CEC) began a n LSGO competition to come up with algorithms with a bunch of standard benchmark unconstrained LS GO functions . Therefore, in this paper, we propos e a hybrid meta - heuristic algorithm, which combines a n I mproved and M odified Harmony Search (IMHS), along with a M odified Differential Evolution (MDE) with an alternate selection strategy . Harmony Search (HS) does the job of exploration and exploitation, and Differe ntial Evolution does the job of giving a perturbation to the exploration of IMHS, as harmony search suffers from being stuck at the basin of local optimal . To judge the performance of the suggested algorithm, we compare the proposed algorithm with ten excellent met a - heuristic algorithms on fifteen LSGO benchmark functions, which have 1000 continuous decision variables, of the CEC 2013 LSGO special session . The experimental results consistently show that our proposed hybrid meta - heuristic performs statistically on par with some algorithms in a few problems, while it turned out to be the best in a couple of problems.

In this paper, we introduce a distributed version of the classical stochastic Multi-Arm Bandit (MAB) problem. Our setting consists of a large number of agents $n$ that collaboratively and simultaneously solve the same instance of $K$ armed MAB to minimize the average cumulative regret over all agents. The agents can communicate and collaborate among each other \emph{only} through a pairwise asynchronous gossip based protocol that exchange a limited number of bits. In our model, agents at each point decide on (i) which arm to play, (ii) whether to, and if so (iii) what and whom to communicate with. Agents in our model are decentralized, namely their actions only depend on their observed history in the past. We develop a novel algorithm in which agents, whenever they choose, communicate only arm-ids and not samples, with another agent chosen uniformly and independently at random. The per-agent regret scaling achieved by our algorithm is $O \left( \frac{\lceil\frac{K}{n}\rceil+\log(n)}{\Delta} \log(T) + \frac{\log^3(n) \log \log(n)}{\Delta^2} \right)$. Furthermore, any agent in our algorithm communicates only a total of $\Theta(\log(T))$ times over a time interval of $T$. We compare our results to two benchmarks - one where there is no communication among agents and one corresponding to complete interaction. We show both theoretically and empirically, that our algorithm experiences a significant reduction both in per-agent regret when compared to the case when agents do not collaborate and in communication complexity when compared to the full interaction setting which requires $T$ communication attempts by an agent over $T$ arm pulls.

In this paper, we study the problem of computing $U$-statistics of degree $2$, i.e., quantities that come in the form of averages over pairs of data points, in the local model of differential privacy (LDP). The class of $U$-statistics covers many statistical estimates of interest, including Gini mean difference, Kendall's tau coefficient and Area under the ROC Curve (AUC), as well as empirical risk measures for machine learning problems such as ranking, clustering and metric learning. We first introduce an LDP protocol based on quantizing the data into bins and applying randomized response, which guarantees an $\epsilon$-LDP estimate with a Mean Squared Error (MSE) of $O(1/\sqrt{n}\epsilon)$ under regularity assumptions on the $U$-statistic or the data distribution. We then propose a specialized protocol for AUC based on a novel use of hierarchical histograms that achieves MSE of $O(\alpha^3/n\epsilon^2)$ for arbitrary data distribution. We also show that 2-party secure computation allows to design a protocol with MSE of $O(1/n\epsilon^2)$, without any assumption on the kernel function or data distribution and with total communication linear in the number of users $n$. Finally, we evaluate the performance of our protocols through experiments on synthetic and real datasets.