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The exchange4media Group is hosting the fifth edition of its flagship property, India Brand Conclave 2021, virtually on the 15th of December, 2021 from 2 PM onwards.The Co-Powered By Partners at India Brand Conclave 2021 are ABP News, Colors and Microsoft-InMobi and Dolby is the Associate Partner.The theme of this edition of the summit is Brands: Capitalizing On Change.

The keynote address at the conclave will be delivered by Lalit Agarwal, Founder & MD, V-Mart. The topic for the session is, Pioneering The Retail Revolution In Indias Heartland, and is moderated by Dr. Annurag Batra, Chairman and Editor-in-Chief, BW Businessworld and exchange4media Group. Agarwal founded V-Mart Retail, in 2002 and pioneered the concept of value fashion retailing in Tier II, III, and IV towns in India.

The first special address will be delivered by Gulbahar Taurani, MD & CEO, Philips Domestic Appliances, India Subcontinent on the topic, Need Of The Hour: Capitalizing On Consumer Insights. The brands tagline, Innovation & You, highlights how Philips focus has been on the customer with its products and marketing tailored around the end consumer.

Also delivering the Special Address is Angelo George, CEO, Bisleri International speaks on the topic, Capitalizing On Change During Covid-19. Brand Bisleri is synonymous with bottled water in India and George tells us how the brand innovated and disrupted the category during the pandemic.

Up next is Karthi Marshan, President & Chief Marketing Officer, Kotak Mahindra Groupwho tells us, How An Agile & Adaptable Mindset Aided Kotak Mahindra to Remain on Top Of Consumers' Minds. Demonetisation followed by the pandemic has transformed how consumers interact with the BFSI space in India and how Kotak Mahindra Group has successfully led this change.

Harpic, Lysol, Vanish, Mortein these are just some of the iconic brands in the portfolio of our next speaker, Kapil Pillai, Regional Marketing Director, South Asia - Hygiene, Reckitt. He shares his insights on the topic, Finding Constancy in Variability.

From traditional to new age, our next brand is one that disrupted the jewellery space in India. From its genesis online, CaratLane now boasts of a strong retail arm . Avnish Anand, Co-founder & COO, CaratLane speaks on the topic, Caratlane's Omnichannel Journey Of Helping Customers Express Their Emotions.

Our next brand was started in 2018 with the vision of making financial inclusion a reality for Indian merchants and became a unicorn earlier this year. Dhruv Dhanraj Bahl, Chief Operating Officer, BharatPe shares his insights on, Better Customer Experience: A Game Changer For Companies.

Registration Link:

To register for the India Brand Conclave 2021: Virtual Summit 3.0, click and register on: bit.ly/3ngEasQ

More information on the event can be found on the event microsite:https://e4mevents.com/ibc-2021/

The Agenda for the Indian Brand Conclave is below:

INDIA BRAND CONCLAVE VIRTUAL EDITION - FIFTH EDITION

WEDNESDAY, DECEMBER 15, 2021

AGENDA

THEME: BRANDS: CAPITALIZING ON CHANGE

2: 00 p.m. 2: 15 p.m.

Welcome Address

Dr. Annurag Batra, Chairman and Editor-in-Chief, BW Businessworld and exchange4media

2:15 p.m. 2:45 p.m.

Special Address

Need Of The Hour: Capitalizing On Consumer Insights

Gulbahar Taurani,

MD & CEO, Philips Domestic Appliances, India Subcontinent

2:45 p.m. 3:20 p.m.

Keynote Address

Pioneering The Retail Revolution In Indias Heartland

Lalit Agarwal, Founder & MD, V-Mart Retail

Session Chair: Dr. Annurag Batra, Chairman and Editor-in-Chief, BW Businessworld and exchange4media Group

3:20 p.m. - 4:15 p.m.

Panel Discussion

Has Marketing Changed from Knowing your Customer to Knowing your Customer Segment?

Deepti Sampat, Vice President Marketing & Ancillary, Vistara

Kunal Bhardwaj,Senior Director - Marketing, UpStox

Maninder Bali, Head of Brand Marketing, Vedantu

Rohit Dosi, Director - MSA Business, InMobi

Vishal Sharma, Head of Marketing, Sleepwell

Session Chair: Preetha Athrey, Head Marketing,TwitterIndia

4:15 p.m. 4:35 p.m.

Speaker Session

Augmented Reality - The New Business Reality'

Prasanna Raman,Business Expansion Lead, Snapchat India

4:35 p.m. 4:55 p.m.

Speaker Session

Better Customer Experience: A Game Changer For Companies

Dhruv Dhanraj Bahl, Chief Operating Officer, BharatPe

4:55 p.m. 5:15 p.m.

Speaker Session

Finding Constancy in Variability

Kapil Pillai, Regional Marketing Director, South Asia - Hygiene, Reckitt

5:15 p.m. 5:35 p.m.

Speaker Session

How Co-Marketing Can Help Build Brand Equity

Sameer Seth, Director, Marketing-India, Dolby Laboratories

5:35 p.m. 6:30 p.m.

Panel Discussion

Is Brand Loyalty Eroding?

Rahul Gandhi, CMO, iD Fresh Foods

Pradnya Popade, Head - Marketing Communications, Samsonite South Asia

Ram Suresh Akella, Executive Director Marketing, Maruti Suzuki India Limited

Dr. Ipsita Chatterjee,Head - Innovation Development & Brand Strategy,Lotus Herbals

Ruchika Gupta, CMO, Luminous Power Technologies

Siddharth Dabhade, Managing Director - India & SAARC, MiQ

Session Chair: Prasad Shejale, Founder & CEO, Logicserve Digital

6:30 p.m. 6: 50: p.m.

Speaker Session

Caratlane's Omnichannel Journey Of Helping Customers Express Their Emotions

Follow this link:
IBC 2021: Top leaders to share insights on 'Brands: Capitalizing on change' - Exchange4Media

Turkish President Tayyip Erdogan gives a statement after a cabinet meeting in Ankara, Turkey, May 17, 2021.

Murat Cetinmuhurdar | Reuters

Turkey's lira fell to a fresh record low on Monday after President Recep Tayyip Erdogan said he was pursuing the expulsion of 10 foreign ambassadors.

The beleaguered currency was trading at 9.738 to the dollar at 11.45 a.m. London time on Monday, hitting an all-time low of 9.82 to the dollar earlier in the day.

Erdogan declared during a rally on Saturday that he had demanded the status of "persona non grata" be applied to the ambassadors of the U.S. and nine other Western countries after they called for the release of Turkish philanthropist Osman Kavala from prison.

The lira, having already hit a record low the previous week after Turkey's central bank cut its key interest rate despite growing inflation, is in for more pain if Erdogan continues on this path, analysts warned. It's fallen 24% versus the dollar so far this year.

"If Erdogan's threat is carried out it would trigger the worst crisis between Turkey and the Western world since the AKP got into power in 2002," Teneo co-President Wolfango Piccoli wrote Monday, referencing the president's political party.

Observers note that the Foreign Ministry has not yet appeared to carry out Erdogan's instructions, as "none of the diplomats has been formally notified," Piccoli wrote. Turkish Foreign Minister Mevlut Cavusoglu, responsible for carrying out the order, has not yet commented on the matter.

Erdogan's comments boosted fears of heightened tensions between the West and Turkey, hitting the already weak lira. Investors have long been concerned about the central bank's lack of independence from Erdogan, who has said that interest rates are "the devil" and holds the unconventional belief that cutting them will reduce inflation the opposite of what most economists say is the case.

If the ambassadors were to be expelled, "the lack of Western diplomatic representative in Ankara will hurt Erdogan," said Timothy Ash, emerging markets strategist at BlueBay Asset Management. "The 10 will reduce interaction with the Erdogan regime and investment into Turkey will suffer."

The 10 countries whose ambassadors were targeted by Erdogan the U.S., Canada, France, Germany, Denmark, Norway, Sweden, Finland, New Zealand, and the Netherlands account for half of Turkey's top 10 trading partners. The group also includes seven NATO members and six EU members.

"It goes without saying that the beleaguered Turkish Lira would fall under intense pressure, after setting various record lows over the past week," Teneo's Piccoli added.

See more here:
Turkish lira hits record low as Erdogan aims to expel ...

Introduction

A best of five game series, $1 million dollars in prize money A high stakes shootout. Between 9 and 15 March, 2016, the second-highest ranked Go player, Lee Sidol, took on a computer program named AlphaGo.

AlphaGo emphatically outplayed and outclassed Mr. Sidol and won the series 4-1. Designed by Googles DeepMind, the program has spawned many other developments in AI, including AlphaGo Zero. These breakthroughs are widely considered as stepping stones towards Artificial General Intelligence (AGI).

In this article, I will introduce you to the algorithm at the heart of AlphaGo Monte Carlo Tree Search (MCTS). This algorithm has one main purpose given the state of a game, choose the most promising move.

To give you some context behind AlphaGo, well first briefly look at the history of game playing AI programs. Then, well see the components of AlphaGo, the Game Tree Concept, a few tree search algorithm, and finally dive into how the MCTS algorithm works.

AI is a vast and complex field. But before AI officially became a recognized body of work, early pioneers in computer science wrote game-playing programs to test whether computers could solve human-intelligence level tasks.

To give you a sense of where Game Playing AI started from and its journey till date, I have put together the below key historical developments:

And this is just skimming the surface! There are plenty of other examples where AI programs exceeded expectations. But this should give you a fair idea of where we stand today.

The core parts of the Alpha Go comprise of:

In this blog, we willfocus on the working of Monte Carlo Tree Searchonly. This helps AlphaGo and AlphaGo Zero smartly explore and reach interesting/good states in a finite time period which in turn helps the AI reach human level performance.

Its application extends beyond games. MCTS can theoretically be applied to any domain that can be described in terms of {state,action} pairs and simulation used to forecast outcomes. Dont worry if this sounds too complex right now, well break down all these concepts in this article.

Game Trees are the most well known data structures that can represent a game. This concept is actually pretty straightforward.

Each node of a game tree represents a particular state in a game. On performing a move, one makes a transition from a node to its children. The nomenclature is very similar to decision trees wherein the terminal nodes are called leaf nodes.

For example, in the above tree, each move is equivalent to putting a cross at different positions. This branches into various other states where a zero is put at each position to generate new states. This process goes on until the leaf node is reached where the win-loss result becomes clear.

Our primary objective behind designing these algorithms is to find best the path to follow in order to win the game. In other words, look/search for a way of traversing the tree that finds the best nodes to achieve victory.

The majority of AI problems can be cast as search problems, which can be solved by finding the best plan, path, model or function.

Tree search algorithms can be seen as building a search tree:

The tree branches out because there are typically several different actions that can be taken in a given state. Tree search algorithms differ depending on which branches are explored and in what order.

Lets discuss a few tree search algorithms.

Uninformed Search algorithms, as the name suggests, search a state space without any further information about the goal. These are considered basic computer science algorithms rather than as a part of AI. Two basic algorithms that fall under this type of search are depth first search (DFS) and breadth first search (BFS). You can read more about them in this blog post.

The Best First Search (BFS) method explores a graph by expanding the most promising node chosen according to a specific rule.The defining characteristic of this search is that, unlikeDFSorBFS(which blindly examine/expand a cell without knowing anything about it), BFS uses an evaluation function (sometimes called a heuristic) to determine which node is the most promising, and then examines this node.

For example, A* algorithm keeps a list of open nodes which are next to an explored node. Note that these open nodes have not been explored. For each open node, an estimate of its distance from the goal is made. New nodes are chosen to explore based on the lowest cost basis, where the cost is the distance from the origin node plus the estimate of the distance to the goal.

For single-player games, simple uninformed or informed search algorithms can be used to find a path to the optimal game state. What should we do for two-player adversarialgames where there is another player to account for? The actions of both players depend on each other.

For these games, we rely on adversarial search. This includes the actions of two (or more) adversarial players. The basic adversarial search algorithm is called Minimax.

This algorithm has been used very successfully for playing classic perfect-information two-player board games such as Checkers and Chess. In fact, it was (re)invented specifically for the purpose of building a chess-playing program.

The core loop of the Minimax algorithm alternates between player 1 and player 2, quite like the white and black players in chess. These are called the min player and the max player. All possible moves are explored for each player.

For each resulting state, all possible moves by the other player are also explored. This goes on until all possible move combinations have been tried out to the point where the game ends (with a win, loss or draw). The entire game tree is generated through this process, from the root node down to the leaves:

Each node is explored to find the moves that give us the maximum value or score.

Games like tic-tac-toe, checkers and chess can arguably be solved using the minimax algorithm. However, things can get a little tricky when there are a large number of potential actions to be taken at each state. This is because minimax explores all the nodes available. It can become frighteningly difficult to solve a complex game like Go in a finite amount of time.

Go has a branching factor of approximately 300 i.e. from each state there are around 300 actions possible, whereas chess typically has around 30 actions to choose from. Further, the positional nature of Go, which is all about surrounding the adversary, makes it very hard to correctly estimate the value of a given board state. For more information on rules for Go, please refer this link.

There are several other games with complex rules that minimax is ill-equipped to solve. These include Battleship Poker with imperfect information and non-deterministic games such as Backgammon and Monopoly. Monte Carlo Tree Search, invented in 2007, provides a possible solution.

The basic MCTS algorithm is simple: a search tree is built, node-by-node, according to the outcomes of simulated playouts. The process can be broken down into the following steps:

Before we delve deeper and understand tree traversal and node expansion, lets get familiar with a few terms.

UCB Value

UCB1, or upper confidence bound for a node, is given by the following formula:

where,

What do we mean by a rollout? Until we reach the leaf node, we randomly choose an action at each step and simulate this action to receive an average reward when the game is over.

Flowchart for Monte Carlo Tree Search

Tree Traversal & Node Expansion

You start with S0, which is the initial state. If the current node is not a leaf node, we calculate the values for UCB1 and choose the node that maximises the UCB value. We keep doing this until we reach the leaf node.

Next, we ask how many times this leaf node was sampled. If its never been sampled before, we simply do a rollout (instead of expanding). However, if it has been sampled before, then we add a new node (state) to the tree for each available action (which we are calling expansion here).

Your current node is now this newly created node. We then do a rollout from this step.

Lets do a complete walkthrough of the algorithm to truly ingrain this concept and understand it in a lucid manner.

Iteration 1:

Initial State

Rollout from S1

Post Backpropogation

The way MCTS works is that we run it for a defined number of iterations or until we are out of time. This will tell us what is the best action at each step that one should take to get the maximum return.

Iteration 2:

Backpropogation from S2

Iteration 3:

Iteration 4:

That is the gist of this algorithm. We can perform more iterations as long as required (or is computationally possible). The underlying idea is thatthe estimate of values at each node becomes more accurate as the number of iterations keep increasing.

Deepminds AlphaGo and AlphaGo Zero programs are far more complex with various other facets that are outside the scope of this article. However, the Monte Carlo Tree Search algorithm remains at the heart of it. MCTS plays the primary role in making complex games like Go easier to crack in a finite amount of time. Some open source implementations of MCTS are linked below:

Implementation in Python

Implementation in C++

I expect reinforcement learning to make a lot of headway in 2019. It wont be surprising to see a lot more complex games being cracked by machines soon. This is a great time to learn reinforcement learning!

I would love to hear your thoughts and suggestions regarding this article and this algorithm in the comments section below. Have you used this algorithm before? If not, which game would you want to try it out on?

Related

See the article here:
Monte Carlo Tree Search Tutorial | DeepMind AlphaGo

A crisis has been escalating along the border that divides Belarus and the European Union. For several weeks, thousands of migrants looking to reach the EU were trapped between Poland and Belarus, living in freezing camps with no humanitarian aid. Today, the migrants have been moved to warehouses for shelter, but this crisis isnt over.

Since 2015, Europe has experienced several migration waves, but this one was different: This one was manufactured. Belarus lured migrants to the border to pressure the EU to lift sanctions. And while this particular crisis has started to die down, the problem isnt going away. Its the result of a complex EU migration policy that has opened the door to the exploitation of migrants, and until that policy is fixed, Belarus or other bordering nations could create a crisis all over again.

To understand how Belarus manufactured this crisis and the geopolitical context that allowed it to happen, watch the video above.

You can find this video and all of Voxs videos on YouTube.

Follow this link:
There is a migrant crisis at the Poland-Belarus border - Vox.com

The Czech Republic on Wednesday announcedits government hadapproved a mandate to send150 troops to the Belarus-Poland border as the migrant crisis there continues.

The measure must also be approved by both chambers of the nation's parliament, which is expected,according toRadio Free Europe. This action by theCzech Republic comes just weeks after both Estonia and Britain deployed troops to the Belarus-Poland border.

"The government has just approved a mandate to send troops to protect the Polish-Belarusian border. Up to 150 soldiers are ready for a period of up to 180 days. The mandate has yet to be approved by both chambers of Parliament!"Czech Minister of DefenseLubomr Metnar tweeted.

Vlda prv schvlila mandt na vysln vojk na ochranu polsko-blorusk hranice. Pipraveno je a 150 vojk na dobu psoben a do 180 dn. Mandt jet mus schvlit ob komory Parlamentu!

Politico Europe reportedaround 100 Estonian and 100 British were being sent to Poland to help secure its border with Belarus.

Thousandsof migrants, mostly from the Middle East, have gathered at Belarus' borders with its European Union-member neighbors. Travel agents and migrants have reportedthat the Belarusian government encouraged easy migration to the country. Upon arriving, migrants were reportedly driven to the border of the EU, given wire cutters and encouraged to illegally cross into countries like Poland, Lithuania and Latvia.

The EU has accused Belarusian President Alexander Lukashenko of manufacturing the crisis and using the migrants for his own political purposes, namely to unsettle the EU as retribution for sanctions that were issued against his government.

The sanctions were issued following crackdowns on the pro-democracy movement in Belarus, a country which has been controlled by Lukashenko often referred to as "Europe's last dictator" for over two decades.

See the rest here:
Czechs move closer to sending troops to Poland-Belarus border amid migrant crisis | TheHill - The Hill